lme4 - Linear Mixed-Effects Models using 'Eigen' and S4

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

zoomerjoin - Superlatively Fast Fuzzy Joins

Empowers users to fuzzily-merge data frames with millions or tens of millions of rows in minutes with low memory usage. The package uses the locality sensitive hashing algorithms developed by Datar, Immorlica, Indyk and Mirrokni (2004) <doi:10.1145/997817.997857>, and Broder (1998) <doi:10.1109/SEQUEN.1997.666900> to avoid having to compare every pair of records in each dataset, resulting in fuzzy-merges that finish in linear time.

worldbank - Client for the 'World Bank Indicators API'

Download and search data from the 'World Bank Indicators API', which provides access to nearly 16,000 time series indicators. See <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation> for further details about the API.

uncertainUCDP - Parametric Mixture Models for Uncertainty Estimation of Fatalities in UCDP Conflict Data

Provides functions for estimating uncertainty in the number of fatalities in the Uppsala Conflict Data Program (UCDP) data. The package implements a parametric reported-value Gumbel mixture distribution that accounts for the uncertainty in the number of fatalities in the UCDP data. The model is based on information from a survey on UCDP coders and how they view the uncertainty of the number of fatalities from UCDP events. The package provides functions for making random draws of fatalities from the mixture distribution, as well as to estimate percentiles, quantiles, means, and other statistics of the distribution. Full details on the survey and estimation procedure can be found in Vesco et al (2024).

tramME - Transformation Models with Mixed Effects

Likelihood-based estimation of mixed-effects transformation models using the Template Model Builder ('TMB', Kristensen et al., 2016) <doi:10.18637/jss.v070.i05>. The technical details of transformation models are given in Hothorn et al. (2018) <doi:10.1111/sjos.12291>. Likelihood contributions of exact, randomly censored (left, right, interval) and truncated observations are supported. The random effects are assumed to be normally distributed on the scale of the transformation function, the marginal likelihood is evaluated using the Laplace approximation, and the gradients are calculated with automatic differentiation (Tamasi & Hothorn, 2021) <doi:10.32614/RJ-2021-075>. Penalized smooth shift terms can be defined using 'mgcv'.

timeless - Fast General Purpose Date/Time Converter

Fast general purpose date/time converter using 'Rust'. The package implements date time, date and epoch time parser for heterogeneous vectors of dates.

tidyclust - A Common API to Clustering

A common interface to specifying clustering models, in the same style as 'parsnip'. Creates unified interface across different functions and computational engines.

sundialr - An Interface to 'SUNDIALS' Ordinary Differential Equation (ODE) Solvers

Provides a way to call the functions in 'SUNDIALS' C ODE solving library (<https://computing.llnl.gov/projects/sundials>). Currently the serial version of ODE solver, 'CVODE', sensitivity calculator 'CVODES' and differential algebraic solver 'IDA' from the 'SUNDIALS' library are implemented. The package requires ODE to be written as an 'R' or 'Rcpp' function and does not require the 'SUNDIALS' library to be installed on the local machine.

sevenbridges2 - The 'Seven Bridges Platform' API Client

R client and utilities for 'Seven Bridges Platform' API, from 'Cancer Genomics Cloud' to other 'Seven Bridges' supported platforms. API documentation is hosted publicly at <https://docs.sevenbridges.com/docs/the-api>.

redbookperu - Access and Analyze Data from the Red Book of Endemic Plants of Peru

Provides access to and analysis of data from "The Red Book of Endemic Plants of Peru" (León, B., Roque, J., Ulloa, C., Jorgensen, P.M., Pitman, N., Cano, A. 2006) <doi:10.15381/rpb.v13i2.1782>. This package offers comprehensive taxonomic, geographic, and conservation information about Peru's endemic plant species. It includes functions to verify species inclusion, obtain updated taxonomic details, and explore the dataset.

ps - List, Query, Manipulate System Processes

List, query and manipulate all system processes, on 'Windows', 'Linux' and 'macOS'.

ppRank - Classification of Algorithms

Implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions <doi:10.48550/arXiv.1902.00101>. Moreira and Carvalho (2023) analyze power in preprocessing methodologies for datasets with missing values <doi:10.1080/03610918.2023.2234683>.

mkde - 2D and 3D Movement-Based Kernel Density Estimates (MKDEs)

Provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions.

mcODE - Monte Carlo Solution of First Order Differential Equations

Two functions for simulating the solution of initial value problems of the form g'(x) = G(x, g) with g(x0) = g0. One is an acceptance-rejection method. The other is a method based on the Mean Value Theorem.

matrixmodp - Working with Matrices over Finite Prime Fields

Provides functions for row-reducing and inverting matrices with entries in many of the finite fields (those with a prime number of elements). With this package, users will be able to find the reduced row echelon form (RREF) of a matrix and calculate the inverse of a (square, invertible) matrix.

jlme - Regression Modelling with 'GLM.jl' and 'MixedModels.jl' in 'Julia'

Bindings to 'Julia' packages 'GLM.jl' <doi:10.5281/zenodo.3376013> and 'MixedModels.jl' <doi:10.5281/zenodo.12575371>, powered by 'JuliaConnectoR'. Fits (generalized) linear (mixed-effects) regression models in 'Julia' using familiar model fitting syntax from R. Offers 'broom'-style data frame summary functionalities for 'Julia' regression models.

inlpubs - USGS INL Project Office Publications

Contains bibliographic information for the U.S. Geological Survey (USGS) Idaho National Laboratory (INL) Project Office.

icpack - Survival Analysis of Interval-Censored Data

Survival analysis of interval-censored data with proportional hazards, and an explicit smooth estimate of the baseline log-hazard with P-splines.

foghorn - Summarize CRAN Check Results in the Terminal

The CRAN check results and where your package stands in the CRAN submission queue in your R terminal.

fddm - Fast Implementation of the Diffusion Decision Model

Provides the probability density function (PDF), cumulative distribution function (CDF), the first-order and second-order partial derivatives of the PDF, and a fitting function for the diffusion decision model (DDM; e.g., Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) with across-trial variability in the drift rate. Because the PDF, its partial derivatives, and the CDF of the DDM both contain an infinite sum, they need to be approximated. 'fddm' implements all published approximations (Navarro & Fuss, 2009, <doi:10.1016/j.jmp.2009.02.003>; Gondan, Blurton, & Kesselmeier, 2014, <doi:10.1016/j.jmp.2014.05.002>; Blurton, Kesselmeier, & Gondan, 2017, <doi:10.1016/j.jmp.2016.11.003>; Hartmann & Klauer, 2021, <doi:10.1016/j.jmp.2021.102550>) plus new approximations. All approximations are implemented purely in 'C++' providing faster speed than existing packages.

ecode - Ordinary Differential Equation Systems in Ecology

A framework to simulate ecosystem dynamics through ordinary differential equations (ODEs). You create an ODE model, tells 'ecode' to explore its behaviour, and perform numerical simulations on the model. 'ecode' also allows you to fit model parameters by machine learning algorithms. Potential users include researchers who are interested in the dynamics of ecological community and biogeochemical cycles.

cubble - A Vector Spatio-Temporal Data Structure for Data Analysis

A spatiotemperal data object in a relational data structure to separate the recording of time variant/ invariant variables.

constructive - Display Idiomatic Code to Construct Most R Objects

Prints code that can be used to recreate R objects. In a sense it is similar to 'base::dput()' or 'base::deparse()' but 'constructive' strives to use idiomatic constructors.

bspline - B-Spline Interpolation and Regression

Build and use B-splines for interpolation and regression. In case of regression, equality constraints as well as monotonicity and/or positivity of B-spline weights can be imposed. Moreover, knot positions (not only spline weights) can be part of optimized parameters too. For this end, 'bspline' is able to calculate Jacobian of basis vectors as function of knot positions. User is provided with functions calculating spline values at arbitrary points. These functions can be differentiated and integrated to obtain B-splines calculating derivatives/integrals at any point. B-splines of this package can simultaneously operate on a series of curves sharing the same set of knots. 'bspline' is written with concern about computing performance that's why the basis and Jacobian calculation is implemented in C++. The rest is implemented in R but without notable impact on computing speed.

brea - Bayesian Recurrent Events Analysis

Functions to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models and multistate models.

aqfig - Display Air Quality Model Output and Monitoring Data

Display air quality model output and monitoring data using scatterplots, grids, and legends.

aberrance - Detect Aberrant Behavior in Test Data

Detect several types of aberrant behavior, including answer copying, answer similarity, nonparametric misfit, parametric misfit, preknowledge, rapid guessing, and test tampering.

TSEAL - Time Series Analysis Library

The library allows to perform a multivariate time series classification based on the use of Discrete Wavelet Transform for feature extraction, a step wise discriminant to select the most relevant features and finally, the use of a linear or quadratic discriminant for classification. Note that all these steps can be done separately which allows to implement new steps. Velasco, I., Sipols, A., de Blas, C. S., Pastor, L., & Bayona, S. (2023) <doi:10.1186/S12938-023-01079-X>. Percival, D. B., & Walden, A. T. (2000,ISBN:0521640687). Maharaj, E. A., & Alonso, A. M. (2014) <doi:10.1016/j.csda.2013.09.006>.

REndo - Fitting Linear Models with Endogenous Regressors using Latent Instrumental Variables

Fits linear models with endogenous regressor using latent instrumental variable approaches. The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized method of moment approach that deals with endogeneity in a multilevel setting. These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed. See the publication related to this package in the Journal of Statistical Software for more details: <doi:10.18637/jss.v107.i03>. Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.

PRA - Project Risk Analysis

Data analysis for Project Risk Management via the Second Moment Method, Monte Carlo Simulation, Bayesian methods, Design Structure Matrices, and more.

One4All - Validate, Share, and Download Data

Designed to enhance data validation and management processes by employing a set of functions that read a set of rules from a 'CSV' or 'Excel' file and apply them to a dataset. Funded by the National Renewable Energy Laboratory and Possibility Lab, maintained by the Moore Institute for Plastic Pollution Research.

KLINK - Kinship Analysis with Linked Markers

A 'shiny' application for forensic kinship testing, based on the 'pedsuite' R packages. 'KLINK' is closely aligned with the (non-R) software 'Familias' and 'FamLink', but offers several unique features, including visualisations and automated report generation. The calculation of likelihood ratios supports pairs of linked markers, and all common mutation models.

GGIR - Raw Accelerometer Data Analysis

A tool to process and analyse data collected with wearable raw acceleration sensors as described in Migueles and colleagues (JMPB 2019), and van Hees and colleagues (JApplPhysiol 2014; PLoSONE 2015). The package has been developed and tested for binary data from 'GENEActiv' <https://activinsights.com/>, binary (.gt3x) and .csv-export data from 'Actigraph' <https://theactigraph.com> devices, and binary (.cwa) and .csv-export data from 'Axivity' <https://axivity.com>. These devices are currently widely used in research on human daily physical activity. Further, the package can handle accelerometer data file from any other sensor brand providing that the data is stored in csv format. Also the package allows for external function embedding.

FamEvent - Family Age-at-Onset Data Simulation and Penetrance Estimation

Simulates age-at-onset traits associated with a segregating major gene in family data obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment correction is utilized to estimate age-dependent penetrance functions either parametrically from the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm can infer missing genotypes and carrier probabilities estimated from family's genotype and phenotype information or from a fitted model. Plot functions include pedigrees of simulated families and predicted penetrance curves based on specified parameter values. For more information see Choi, Y.-H., Briollais, L., He, W. and Kopciuk, K. (2021) FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs, Journal of Statistical Software 97 (7), 1-30.

DrugExposureDiagnostics - Diagnostics for OMOP Common Data Model Drug Records

Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model.

Cubist - Rule- And Instance-Based Regression Modeling

Regression modeling using rules with added instance-based corrections.

CalibrationCurves - Calibration Performance

Plots calibration curves and computes statistics for assessing calibration performance. See De Cock Campo (2023) <doi:10.48550/arXiv.2309.08559> and Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>.

CPCAT - The Closure Principle Computational Approach Test

P-values and no/lowest observed (adverse) effect concentration values derived from the closure principle computational approach test (Lehmann, R. et al. (2015) <doi:10.1007/s00477-015-1079-4>) are provided. The package contains functions to generate intersection hypotheses according to the closure principle (Bretz, F., Hothorn, T., Westfall, P. (2010) <doi:10.1201/9781420010909>), an implementation of the computational approach test (Ching-Hui, C., Nabendu, P., Jyh-Jiuan, L. (2010) <doi:10.1080/03610918.2010.508860>) and the combination of both, that is, the closure principle computational approach test.

CBPE - Correlation-Based Penalized Estimators

Provides correlation-based penalty estimators for both linear and logistic regression models by implementing a new regularization method that incorporates correlation structures within the data. This method encourages a grouping effect where strongly correlated predictors tend to be in or out of the model together. See Tutz and Ulbricht (2009) <doi:10.1007/s11222-008-9088-5> and Algamal and Lee (2015) <doi:10.1016/j.eswa.2015.08.016>.

AIUQ - Ab Initio Uncertainty Quantification

Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>.

bookdown - Authoring Books and Technical Documents with R Markdown

Output formats and utilities for authoring books and technical documents with R Markdown.

vse4ts - Identify Memory Patterns in Time Series Using Variance Scale Exponent

Methods for calculating the variance scale exponent to identify memory patterns in time series data. Includes tests for white noise, short memory, and long memory. See Fu, H. et al. (2018) <doi:10.1016/j.physa.2018.06.092>.

vectorsurvR - Data Access and Analytical Tools for 'VectorSurv' Users

Allows registered 'VectorSurv' <https://vectorsurv.org/> users access to data through the 'VectorSurv API' <https://api.vectorsurv.org/>. Additionally provides functions for analysis and visualization.

track2KBA - Identifying Important Areas from Animal Tracking Data

Functions for preparing and analyzing animal tracking data, with the intention of identifying areas which are potentially important at the population level and therefore of conservation interest. Areas identified using this package may be checked against global or regionally-defined criteria, such as those set by the Key Biodiversity Area program. The method published herein is described in full in Beal et al. 2021 <doi:10.1111/2041-210X.13713>.

tbea - Pre- And Post-Processing in Bayesian Evolutionary Analyses

Functions are provided for prior specification in divergence time estimation using fossils as well as other kinds of data. It provides tools for interacting with the input and output of Bayesian platforms in evolutionary biology such as 'BEAST2', 'MrBayes', 'RevBayes', or 'MCMCTree'. It Implements a simple measure similarity between probability density functions for comparing prior and posterior Bayesian densities, as well as code for calculating the combination of distributions using conflation of Hill (2008). Functions for estimating the origination time in collections of distributions using the x-intercept (e.g., Draper and Smith, 1998) and stratigraphic intervals (Marshall 2010) are also available. Hill, T. 2008. "Conflations of probability distributions". Transactions of the American Mathematical Society, 363:3351-3372. <doi:10.48550/arXiv.0808.1808>, Draper, N. R. and Smith, H. 1998. "Applied Regression Analysis". 1--706. Wiley Interscience, New York. <DOI:10.1002/9781118625590>, Marshall, C. R. 2010. "Using confidence intervals to quantify the uncertainty in the end-points of stratigraphic ranges". Quantitative Methods in Paleobiology, 291--316. <DOI:10.1017/S1089332600001911>.

soiltestcorr - Soil Test Correlation and Calibration

A compilation of functions designed to assist users on the correlation analysis of crop yield and soil test values. Functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches such as: 1) the modified arcsine-log calibration curve (Correndo et al. (2017) <doi:10.1071/CP16444>); 2) the graphical Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical Cate-Nelson quadrants analysis (Cate & Nelson (1971) <doi:10.2136/sssaj1971.03615995003500040048x>), 4) the linear-plateau regression (Anderson & Nelson (1975) <doi:10.2307/2529422>), 5) the quadratic-plateau regression (Bullock & Bullock (1994) <doi:10.2134/agronj1994.00021962008600010033x>), and 6) the Mitscherlich-type exponential regression (Melsted & Peck (1977) <doi:10.2134/asaspecpub29.c1>). The package development stemmed from ongoing work with the Fertilizer Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) projects.

simdata - Generate Simulated Datasets

Generate simulated datasets from an initial underlying distribution and apply transformations to obtain realistic data. Implements the 'NORTA' (Normal-to-anything) approach from Cario and Nelson (1997) and other data generating mechanisms. Simple network visualization tools are provided to facilitate communicating the simulation setup.

scan - Single-Case Data Analyses for Single and Multiple Baseline Designs

A collection of procedures for analysing, visualising, and managing single-case data. These include piecewise linear regression models, multilevel models, overlap indices ('PND', 'PEM', 'PAND', 'PET', 'tau-u', 'baseline corrected tau', 'CDC'), and randomization tests. Data preparation functions support outlier detection, handling missing values, scaling, truncation, rank transformation, and smoothing. An export function helps to generate html and latex tables in a publication friendly style. More details can be found in the online book 'Analyzing single-case data with R and scan', Juergen Wilbert (2023) <https://jazznbass.github.io/scan-Book/>.

robustbase - Basic Robust Statistics

"Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.

rgexf - Build, Import, and Export GEXF Graph Files

Create, read, and write 'GEXF' (Graph Exchange 'XML' Format) graph files (used in 'Gephi' and others). Using the 'XML' package, rgexf allows reading and writing GEXF files, including attributes, 'GEXF' visual attributes (such as color, size, and position), network dynamics (for both edges and nodes), and edges' weights. Users can build/handle graphs element-by-element or massively through data frames, visualize the graph on a web browser through 'gexf-js' (a 'javascript' library), and interact with the 'igraph' package.

rempsyc - Convenience Functions for Psychology

Make your workflow faster and easier. Easily customizable plots (via 'ggplot2'), nice APA tables (following the style of the *American Psychological Association*) exportable to Word (via 'flextable'), easily run statistical tests or check assumptions, and automatize various other tasks.

psbcSpeedUp - Penalized Semiparametric Bayesian Cox Models

Algorithms to speed up the Bayesian Lasso Cox model (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and the Bayesian Lasso Cox with mandatory variables (Zucknick et al. Biometrical J, 2015 <doi:10.1002/bimj.201400160>).

pomp - Statistical Inference for Partially Observed Markov Processes

Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

pharmaverseadam - ADaM Test Data for the 'Pharmaverse' Family of Packages

A set of Analysis Data Model (ADaM) datasets constructed using the Study Data Tabulation Model (SDTM) datasets contained in the 'pharmaversesdtm' package and the template scripts from the 'admiral' family of packages. ADaM dataset specifications are described in: CDISC Analysis Data Model Team (2021) <https://www.cdisc.org/system/files/members/standard/foundational/ADaMIG_v1.3.pdf>.

parallelpam - Parallel Partitioning-Around-Medoids (PAM) for Big Sets of Data

Application of the Partitioning-Around-Medoids (PAM) clustering algorithm described in Schubert, E. and Rousseeuw, P.J.: "Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms." Information Systems, vol. 101, p. 101804, (2021). <doi:10.1016/j.is.2021.101804>. It uses a binary format for storing and retrieval of matrices developed for the 'jmatrix' package but the functionality of 'jmatrix' is included here, so you do not need to install it. Also, it is used by package 'scellpam', so if you have installed it, you do not need to install this package. PAM can be applied to sets of data whose dissimilarity matrix can be very big. It has been tested with up to 100.000 points. It does this with the help of the code developed for other package, 'jmatrix', which allows the matrix not to be loaded in 'R' memory (which would force it to be of double type) but it gets from disk, which allows using float (or even smaller data types). Moreover, the dissimilarity matrix is calculated in parallel if the computer has several cores so it can open many threads. The initial part of the PAM algorithm can be done with the BUILD or LAB algorithms; the BUILD algorithm has been implemented in parallel. The optimization phase implements the FastPAM1 algorithm, also in parallel. Finally, calculation of silhouette is available and also implemented in parallel.

pamr - Pam: Prediction Analysis for Microarrays

Some functions for sample classification in microarrays.

orbital - Predict with 'tidymodels' Workflows in Databases

Turn 'tidymodels' workflows into objects containing the sufficient sequential equations to perform predictions. These smaller objects allow for low dependency prediction locally or directly in databases.

nanoparquet - Read and Write 'Parquet' Files

Self-sufficient reader and writer for flat 'Parquet' files. Can read most 'Parquet' data types. Can write many 'R' data types, including factors and temporal types. See docs for limitations.

mvgam - Multivariate (Dynamic) Generalized Additive Models

Fit Bayesian Dynamic Generalized Additive Models to sets of time series. Users can build dynamic nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2022) <doi:10.1111/2041-210X.13974>.

mpmsim - Simulation of Matrix Population Models with Defined Life History Characteristics

Allows users to simulate matrix population models with particular characteristics based on aspects of life history such as mortality trajectories and fertility trajectories. Also allows the exploration of sampling error due to small sample size.

mlr3viz - Visualizations for 'mlr3'

Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, learners, predictions, benchmark results, tuning instances and filters via the 'autoplot()' generic of 'ggplot2'. The package draws plots with the 'viridis' color palette and applies the minimal theme. Visualizations include barplots, boxplots, histograms, ROC curves, and Precision-Recall curves.

mlr3pipelines - Preprocessing Operators and Pipelines for 'mlr3'

Dataflow programming toolkit that enriches 'mlr3' with a diverse set of pipelining operators ('PipeOps') that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as 'mlr3' 'Learners' and can therefore be resampled, benchmarked, and tuned.

mlr3mbo - Flexible Bayesian Optimization

A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006) <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008) <doi:10.1007/978-3-540-87700-4_78>.

mirai - Minimalist Async Evaluation Framework for R

High-performance parallel code execution and distributed computing. Designed for simplicity, a 'mirai' evaluates an R expression asynchronously, on local or network resources, resolving automatically upon completion. Modern networking and concurrency built on 'nanonext' and 'NNG' (Nanomsg Next Gen) ensures reliable and efficient scheduling, over fast inter-process communications or TCP/IP secured by TLS.

matlab - 'MATLAB' Emulation Package

Emulate 'MATLAB' code using 'R'.

matchMulti - Optimal Multilevel Matching using a Network Algorithm

Performs multilevel matches for data with cluster- level treatments and individual-level outcomes using a network optimization algorithm. Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis. Details in Pimentel et al. (2018) <doi:10.1214/17-AOAS1118>. The optmatch package, which is useful for running many of the provided functions, may be downloaded from Github at <https://github.com/markmfredrickson/optmatch> if not available on CRAN.

luajr - 'LuaJIT' Scripting

An interface to 'LuaJIT' <https://luajit.org>, a just-in-time compiler for the 'Lua' scripting language <https://www.lua.org>. Allows users to run 'Lua' code from 'R'.

lavaanExtra - Convenience Functions for Package 'lavaan'

Affords an alternative, vector-based syntax to 'lavaan', as well as other convenience functions such as naming paths and defining indirect links automatically, in addition to convenience formatting optimized for a publication and script sharing workflow.

latcontrol - Evaluation of the Role of Control Variables in Structural Equation Models

Various opportunities to evaluate the effects of including one or more control variable(s) in structural equation models onto model-implied variances, covariances, and parameter estimates. The derivation of the methodology employed in this package can be obtained from Blötner (2023) <doi:10.31234/osf.io/dy79z>.

khroma - Colour Schemes for Scientific Data Visualization

Color schemes ready for each type of data (qualitative, diverging or sequential), with colors that are distinct for all people, including color-blind readers. This package provides an implementation of Paul Tol (2018) and Fabio Crameri (2018) <doi:10.5194/gmd-11-2541-2018> color schemes for use with 'graphics' or 'ggplot2'. It provides tools to simulate color-blindness and to test how well the colors of any palette are identifiable. Several scientific thematic schemes (geologic timescale, land cover, FAO soils, etc.) are also implemented.

jagshelper - Extracting and Visualizing Output from 'jagsUI'

Tools are provided to streamline Bayesian analyses in 'JAGS' using the 'jagsUI' package. Included are functions for extracting output in simpler format, functions for streamlining assessment of convergence, and functions for producing summary plots of output. Also included is a function that provides a simple template for running 'JAGS' from 'R'. Referenced materials can be found at <DOI:10.1214/ss/1177011136>.

inlabru - Bayesian Latent Gaussian Modelling using INLA and Extensions

Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.

ieugwasr - Interface to the 'OpenGWAS' Database API

Interface to the 'OpenGWAS' database API <https://gwas-api.mrcieu.ac.uk/>. Includes a wrapper to make generic calls to the API, plus convenience functions for specific queries.

healthyR - Hospital Data Analysis Workflow Tools

Hospital data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative hospital data. Some of these include average length of stay, readmission rates, average net pay amounts by service lines just to name a few. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

ggpp - Grammar Extensions to 'ggplot2'

Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Geometries: geom_table(), geom_plot() and geom_grob() add insets to plots using native data coordinates, while geom_table_npc(), geom_plot_npc() and geom_grob_npc() do the same using "npc" coordinates through new aesthetics "npcx" and "npcy". Statistics: select observations based on 2D density. Positions: radial nudging away from a center point and nudging away from a line or curve; combined stacking and nudging; combined dodging and nudging.

fmesher - Triangle Meshes and Related Geometry Tools

Generate planar and spherical triangle meshes, compute finite element calculations for 1- and 2-dimensional flat and curved manifolds with associated basis function spaces, methods for lines and polygons, and transparent handling of coordinate reference systems and coordinate transformation, including 'sf' and 'sp' geometries. The core 'fmesher' library code was originally part of the 'INLA' package, and implements parts of "Triangulations and Applications" by Hjelle and Daehlen (2006) <doi:10.1007/3-540-33261-8>.

emmeans - Estimated Marginal Means, aka Least-Squares Means

Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.

databraryr - Interact with the 'Databrary.org' API

'Databrary.org' is a restricted access repository for research data, especially video and audio. This package provides commands to interact with the data stored on 'Databrary.org'.

clustMixType - k-Prototypes Clustering for Mixed Variable-Type Data

Functions to perform k-prototypes partitioning clustering for mixed variable-type data according to Z.Huang (1998): Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Variables, Data Mining and Knowledge Discovery 2, 283-304.

chem16S - Chemical Metrics for Microbial Communities

Combines taxonomic classifications of high-throughput 16S rRNA gene sequences with reference proteomes of archaeal and bacterial taxa to generate amino acid compositions of community reference proteomes. Calculates chemical metrics including carbon oxidation state ('Zc'), stoichiometric oxidation and hydration state ('nO2' and 'nH2O'), H/C, N/C, O/C, and S/C ratios, grand average of hydropathicity ('GRAVY'), isoelectric point ('pI'), protein length, and average molecular weight of amino acid residues. Uses precomputed reference proteomes for archaea and bacteria derived from the Genome Taxonomy Database ('GTDB'). Also includes reference proteomes derived from the NCBI Reference Sequence ('RefSeq') database and manual mapping from the 'RDP Classifier' training set to 'RefSeq' taxonomy as described by Dick and Tan (2023) <doi:10.1007/s00248-022-01988-9>. Processes taxonomic classifications in 'RDP Classifier' format or OTU tables in 'phyloseq-class' objects from the Bioconductor package 'phyloseq'.

campsis - Generic PK/PD Simulation Platform CAMPSIS

A generic, easy-to-use and intuitive pharmacokinetic/pharmacodynamic (PK/PD) simulation platform based on R packages 'rxode2', 'RxODE' and 'mrgsolve'. CAMPSIS provides an abstraction layer over the underlying processes of writing a PK/PD model, assembling a custom dataset and running a simulation. CAMPSIS has a strong dependency to the R package 'campsismod', which allows to read/write a model from/to files and adapt it further on the fly in the R environment. Package 'campsis' allows the user to assemble a dataset in an intuitive manner. Once the user’s dataset is ready, the package is in charge of preparing the simulation, calling 'rxode2', 'RxODE' or 'mrgsolve' (at the user's choice) and returning the results, for the given model, dataset and desired simulation settings.

bibliometrix - Comprehensive Science Mapping Analysis

Tool for quantitative research in scientometrics and bibliometrics. It implements the comprehensive workflow for science mapping analysis proposed in Aria M. and Cuccurullo C. (2017) <doi:10.1016/j.joi.2017.08.007>. 'bibliometrix' provides various routines for importing bibliographic data from 'SCOPUS', 'Clarivate Analytics Web of Science' (<https://www.webofknowledge.com/>), 'Digital Science Dimensions' (<https://www.dimensions.ai/>), 'OpenAlex' (<https://openalex.org/>), 'Cochrane Library' (<https://www.cochranelibrary.com/>), 'Lens' (<https://lens.org>), and 'PubMed' (<https://pubmed.ncbi.nlm.nih.gov/>) databases, performing bibliometric analysis and building networks for co-citation, coupling, scientific collaboration and co-word analysis.

b64 - Fast and Vectorized Base 64 Engine

Provides a fast, lightweight, and vectorized base 64 engine to encode and decode character and raw vectors as well as files stored on disk. Common base 64 alphabets are supported out of the box including the standard, URL-safe, bcrypt, crypt, 'BinHex', and IMAP-modified UTF-7 alphabets. Custom engines can be created to support unique base 64 encoding and decoding needs.

attachment - Deal with Dependencies

Manage dependencies during package development. This can retrieve all dependencies that are used in ".R" files in the "R/" directory, in ".Rmd" files in "vignettes/" directory and in 'roxygen2' documentation of functions. There is a function to update the "DESCRIPTION" file of your package with 'CRAN' packages or any other remote package. All functions to retrieve dependencies of ".R" scripts and ".Rmd" or ".qmd" files can be used independently of a package development.

WordListsAnalytics - Multiple Data Analysis Tools for Property Listing Tasks

Application to estimate statistical values using properties provided by a group of individuals to describe concepts using 'shiny'. It estimates the underlying distribution to generate new descriptive words Canessa et al. (2023) <doi:10.3758/s13428-022-01811-w>, applies a new clustering model, and uses simulations to estimate the probability that two persons describe the same words based on their descriptions Canessa et al. (2022) <doi:10.3758/s13428-022-02030-z>.

ThomasJeffersonUniv - Handy Tools for TJU/TJUH Employees

Functions for admin needs of employees of Thomas Jefferson University and Thomas Jefferson University Hospital, Philadelphia, PA.

RNAseqQC - Quality Control for RNA-Seq Data

Functions for semi-automated quality control of bulk RNA-seq data.

ProjectTemplate - Automates the Creation of New Statistical Analysis Projects

Provides functions to automatically build a directory structure for a new R project. Using this structure, 'ProjectTemplate' automates data loading, preprocessing, library importing and unit testing.

MacBehaviour - Behavioural Studies of Large Language Models

Efficient way to design and conduct psychological experiments for testing the performance of large language models. It simplifies the process of setting up experiments and data collection via language models’ API, facilitating a smooth workflow for researchers in the field of machine behaviour.

LatentBMA - Bayesian Model Averaging for Univariate Link Latent Gaussian Models

Bayesian model averaging (BMA) algorithms for univariate link latent Gaussian models (ULLGMs). For detailed information, refer to Steel M.F.J. & Zens G. (2024) "Model Uncertainty in Latent Gaussian Models with Univariate Link Function" <doi:10.48550/arXiv.2406.17318>. The package supports various g-priors and a beta-binomial prior on the model space. It also includes auxiliary functions for visualizing and tabulating BMA results. Currently, it offers an out-of-the-box solution for model averaging of Poisson log-normal (PLN) and binomial logistic-normal (BiL) models. The codebase is designed to be easily extendable to other likelihoods, priors, and link functions.

Langevin - Langevin Analysis in One and Two Dimensions

Estimate drift and diffusion functions from time series and generate synthetic time series from given drift and diffusion coefficients.

LSEbootLS - Bootstrap Methods for Regression Models with Locally Stationary Errors

Implements bootstrap methods for linear regression models with errors following a time-varying process, focusing on approximating the distribution of the least-squares estimator for regression models with locally stationary errors. It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis.

LKT - Logistic Knowledge Tracing

Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <https://ieeexplore.ieee.org/document/9616435>. 'LKT' is a method to compute features of student data that are used as predictors of subsequent performance. 'LKT' allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of 'LiblineaR', which enables extremely fast solutions compared to base glm() in R.

IncidencePrevalence - Estimate Incidence and Prevalence using the OMOP Common Data Model

Calculate incidence and prevalence using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. Incidence and prevalence can be estimated for the total population in a database or for a stratification cohort.

GUD - Bayesian Modal Regression Based on the GUD Family

Provides probability density functions and sampling algorithms for three key distributions from the General Unimodal Distribution (GUD) family: the Flexible Gumbel (FG) distribution, the Double Two-Piece (DTP) Student-t distribution, and the Two-Piece Scale (TPSC) Student-t distribution. Additionally, this package includes a function for Bayesian linear modal regression, leveraging these three distributions for model fitting. The details of the Bayesian modal regression model based on the GUD family can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.

EIEntropy - Ecological Inference Applying Entropy

Implements two estimations related to the foundations of info metrics applied to ecological inference. These methodologies assess the lack of disaggregated data and provide an approach to obtaining disaggregated territorial-level data. For more details, see the following references: Fernández-Vázquez, E., Díaz-Dapena, A., Rubiera-Morollón, F. et al. (2020) "Spatial Disaggregation of Social Indicators: An Info-Metrics Approach." <doi:10.1007/s11205-020-02455-z>. Díaz-Dapena, A., Fernández-Vázquez, E., Rubiera-Morollón, F., & Vinuela, A. (2021) "Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom." <doi:10.1111/rsp3.12379>.

DeepLearningCausal - Causal Inference with Super Learner and Deep Neural Networks

Functions to estimate Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT) from experimental or observational data using the Super Learner (SL) ensemble method and Deep neural networks. The package first provides functions to implement meta-learners such as the Single-learner (S-learner) and Two-learner (T-learner) described in Künzel et al. (2019) <doi:10.1073/pnas.1804597116> for estimating the CATE. The S- and T-learner are each estimated using the SL ensemble method and deep neural networks. It then provides functions to implement the Ottoboni and Poulos (2020) <doi:10.1515/jci-2018-0035> PATT-C estimator to obtain the PATT from experimental data with noncompliance by using the SL ensemble method and deep neural networks.

ClinSigMeasures - Clinical Significance Measures

Provides measures of effect sizes for summarized continuous variables as well as diagnostic accuracy statistics for 2x2 table data. Includes functions for Cohen's d, robust effect size, Cohen's q, partial eta-squared, coefficient of variation, odds ratio, likelihood ratios, sensitivity, specificity, positive and negative predictive values, Youden index, number needed to treat, number needed to diagnose, and predictive summary index.

BuyseTest - Generalized Pairwise Comparisons

Implementation of the Generalized Pairwise Comparisons (GPC) as defined in Buyse (2010) <doi:10.1002/sim.3923> for complete observations, and extended in Peron (2018) <doi:10.1177/0962280216658320> to deal with right-censoring. GPC compare two groups of observations (intervention vs. control group) regarding several prioritized endpoints to estimate the probability that a random observation drawn from one group performs better than a random observation drawn from the other group (Mann-Whitney parameter). The net benefit and win ratio statistics, i.e. the difference and ratio between the probabilities relative to the intervention and control groups, can then also be estimated. Confidence intervals and p-values are obtained based on asymptotic results (Ozenne 2021 <doi:10.1177/09622802211037067>), non-parametric bootstrap, or permutations. The software enables the use of thresholds of minimal importance difference, stratification, non-prioritized endpoints (O Brien test), and can handle right-censoring and competing-risks.

robustHD - Robust Methods for High-Dimensional Data

Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression. Specifically, the package implements robust least angle regression (Khan, Van Aelst & Zamar, 2007; <doi:10.1198/016214507000000950>), (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; <doi:10.1016/j.csda.2015.02.007>), and sparse least trimmed squares regression (Alfons, Croux & Gelper, 2013; <doi:10.1214/12-AOAS575>).

rcorpora - A Collection of Small Text Corpora of Interesting Data

A collection of small text corpora of interesting data. It contains all data sets from 'dariusk/corpora'. Some examples: names of animals: birds, dinosaurs, dogs; foods: beer categories, pizza toppings; geography: English towns, rivers, oceans; humans: authors, US presidents, occupations; science: elements, planets; words: adjectives, verbs, proverbs, US president quotes.

nodbi - 'NoSQL' Database Connector

Simplified document database access and manipulation, providing a common API across supported 'NoSQL' databases 'Elasticsearch', 'CouchDB', 'MongoDB' as well as 'SQLite/JSON1', 'PostgreSQL', and 'DuckDB'.

netplot - Beautiful Graph Drawing

A graph visualization engine that emphasizes on aesthetics at the same time providing default parameters that yield out-of-the-box-nice visualizations. The package is built on top of 'The Grid Graphics Package' and seamlessly work with 'igraph' and 'network' objects.

multilevelcoda - Estimate Bayesian Multilevel Models for Compositional Data

Implement Bayesian Multilevel Modelling for compositional data in a multilevel framework. Compute multilevel compositional data and Isometric log ratio (ILR) at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models.

moderndive - Tidyverse-Friendly Introductory Linear Regression

Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.

mlr3verse - Easily Install and Load the 'mlr3' Package Family

The 'mlr3' package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core 'mlr3' packages. Get more information about the 'mlr3' project at <https://mlr3book.mlr-org.com/>.

mevr - Fitting the Metastatistical Extreme Value Distribution MEVD

Extreme value analysis with the metastatistical extreme value distribution MEVD (Marani and Ignaccolo, 2015, <doi:10.1016/j.advwatres.2015.03.001>) and some of its variants. In particular, analysis can be performed with the simplified metastatistical extreme value distribution SMEV (Marra et al., 2019, <doi:10.1016/j.advwatres.2019.04.002>) and the temporal metastatistical extreme value distribution TMEV (Falkensteiner et al., 2023, <doi:10.1016/j.wace.2023.100601>). Parameters can be estimated with probability weighted moments, maximum likelihood and least squares. The data can also be left-censored prior to a fit. Density, distribution function, quantile function and random generation for the MEVD, SMEV and TMEV are included. In addition, functions for the calculation of return levels including confidence intervals are provided. For a description of use cases please see the provided references.

metrica - Prediction Performance Metrics

A compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.

lincom - Linear Biomarker Combination: Empirical Performance Optimization

Perform two linear combination methods for biomarkers: (1) Empirical performance optimization for specificity (or sensitivity) at a controlled sensitivity (or specificity) level of Huang and Sanda (2022) <doi:10.1214/22-aos2210>, and (2) weighted maximum score estimator with empirical minimization of averaged false positive rate and false negative rate. Both adopt the algorithms of Huang and Sanda (2022) <doi:10.1214/22-aos2210>. 'MOSEK' solver is used and needs to be installed; an academic license for 'MOSEK' is free.

lhs - Latin Hypercube Samples

Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples.

jlmerclusterperm - Cluster-Based Permutation Analysis for Densely Sampled Time Data

An implementation of fast cluster-based permutation analysis (CPA) for densely-sampled time data developed in Maris & Oostenveld, 2007 <doi:10.1016/j.jneumeth.2007.03.024>. Supports (generalized, mixed-effects) regression models for the calculation of timewise statistics. Provides both a wholesale and a piecemeal interface to the CPA procedure with an emphasis on interpretability and diagnostics. Integrates 'Julia' libraries 'MixedModels.jl' and 'GLM.jl' for performance improvements, with additional functionalities for interfacing with 'Julia' from 'R' powered by the 'JuliaConnectoR' package.

heimdall - Drift Adaptable Models

By analyzing streaming datasets, it is possible to observe significant changes in the data distribution or models' accuracy during their prediction (concept drift). The goal of 'heimdall' is to measure when concept drift occurs. The package makes available several state-of-the-art methods. It also tackles how to adapt models in a nonstationary context. Some concept drifts methods are described in Tavares (2022) <doi:10.1007/s12530-021-09415-z>.

file2meco - Transform Files to 'microtable' Object with 'microeco' Package

Transform output files of some tools to the 'microtable' object of 'microtable' class in 'microeco' package. The 'microtable' class is the basic class in 'microeco' package and is necessary for the downstream microbial community data analysis.

erify - Check Arguments and Generate Readable Error Messages

Provides several validator functions for checking if arguments passed by users have valid types, lengths, etc. and for generating informative and well-formatted error messages in a consistent style. Also provides tools for users to create their own validator functions. The error message style used is adopted from <https://style.tidyverse.org/error-messages.html>.

ctrdata - Retrieve and Analyze Clinical Trials in Public Registers

A system for querying, retrieving and analyzing protocol- and results-related information on clinical trials from four public registers, the 'European Union Clinical Trials Register' ('EUCTR', <https://www.clinicaltrialsregister.eu/>), 'ClinicalTrials.gov' (<https://clinicaltrials.gov/> and also translating queries the retired classic interface), the 'ISRCTN' (<http://www.isrctn.com/>) and the 'European Union Clinical Trials Information System' ('CTIS', <https://euclinicaltrials.eu/>). Trial information is downloaded, converted and stored in a database ('PostgreSQL', 'SQLite', 'DuckDB' or 'MongoDB'; via package 'nodbi'). Documents in registers associated with trials can also be downloaded. Other functions identify deduplicated records, easily find and extract variables (fields) of interest even from complex nested data as used by the registers, merge variables and update queries. The package can be used for meta-analysis and trend-analysis of the design and conduct as well as of the results of clinical trials.

PoPdesign - Posterior Predictive (PoP) Design for Phase I Clinical Trials

The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses.

BayesSUR - Bayesian Seemingly Unrelated Regression Models in High-Dimensional Settings

Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.

xhaz - Excess Hazard Modelling Considering Inappropriate Mortality Rates

Fits relative survival regression models with or without proportional excess hazards and with the additional possibility to correct for background mortality by one or more parameter(s). These models are relevant when the observed mortality in the studied group is not comparable to that of the general population or in population-based studies where the available life tables used for net survival estimation are insufficiently stratified. In the latter case, the proposed model by Touraine et al. (2020) <doi:10.1177/0962280218823234> can be used. The user can also fit a model that relaxes the proportional expected hazards assumption considered in the Touraine et al. excess hazard model. This extension was proposed by Mba et al. (2020) <doi:10.1186/s12874-020-01139-z> to allow non-proportional effects of the additional variable on the general population mortality. In non-population-based studies, researchers can identify non-comparability source of bias in terms of expected mortality of selected individuals. An excess hazard model correcting this selection bias is presented in Goungounga et al. (2019) <doi:10.1186/s12874-019-0747-3>. This class of model with a random effect at the cluster level on excess hazard is presented in Goungounga et al. (2023) <doi:10.1002/bimj.202100210>.

tidyrules - Utilities to Retrieve Rulelists from Model Fits, Filter, Prune, Reorder and Predict on Unseen Data

Provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe.

this.path - Get Executing Script's Path

Determine the path of the executing script. Compatible with a few popular GUIs: 'Rgui', 'RStudio', 'VSCode', 'Jupyter', 'Emacs', and 'Rscript' (shell). Compatible with several functions and packages: 'source()', 'sys.source()', 'debugSource()' in 'RStudio', 'compiler::loadcmp()', 'utils::Sweave()', 'box::use()', 'knitr::knit()', 'plumber::plumb()', 'shiny::runApp()', 'package:targets', and 'testthat::source_file()'.

tabr - Music Notation Syntax, Manipulation, Analysis and Transcription in R

Provides a music notation syntax and a collection of music programming functions for generating, manipulating, organizing, and analyzing musical information in R. Music syntax can be entered directly in character strings, for example to quickly transcribe short pieces of music. The package contains functions for directly performing various mathematical, logical and organizational operations and musical transformations on special object classes that facilitate working with music data and notation. The same music data can be organized in tidy data frames for a familiar and powerful approach to the analysis of large amounts of structured music data. Functions are available for mapping seamlessly between these formats and their representations of musical information. The package also provides an API to 'LilyPond' (<https://lilypond.org/>) for transcribing musical representations in R into tablature ("tabs") and sheet music. 'LilyPond' is open source music engraving software for generating high quality sheet music based on markup syntax. The package generates 'LilyPond' files from R code and can pass them to the 'LilyPond' command line interface to be rendered into sheet music PDF files or inserted into R markdown documents. The package offers nominal MIDI file output support in conjunction with rendering sheet music. The package can read MIDI files and attempts to structure the MIDI data to integrate as best as possible with the data structures and functionality found throughout the package.

survivoR - Data from all Seasons of Survivor (US) TV Series in Tidy Format

Several datasets which detail the results and events of each season of Survivor. This includes details on the cast, voting history, immunity and reward challenges, jury votes and viewers. This data is useful for practicing data wrangling, graph analytics and analysing how each season of Survivor played out. Includes 'ggplot2' scales and colour palettes for visualisation.

sos - Search Contributed R Packages, Sort by Package

Search contributed R packages, sort by package.

secr - Spatially Explicit Capture-Recapture

Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.

scplot - Plot Function for Single-Case Data Frames

Add-on for the 'scan' package that creates plots from single-case data frames ('scdf'). It includes functions for styling single-case plots, adding phase-based lines to indicate various statistical parameters, and predefined themes for presentations and publications. More information and in depth examples can be found in the online book "Analyzing Single-Case Data with R and 'scan" Jürgen Wilbert (2023) <https://jazznbass.github.io/scan-Book/>.

rtrek - Data Analysis Relating to Star Trek

Provides datasets related to the Star Trek fictional universe and functions for working with the data. The package also provides access to real world datasets based on the televised series and other related licensed media productions. It interfaces with the Star Trek API (STAPI) (<http://stapi.co/>), Memory Alpha (<https://memory-alpha.fandom.com/wiki/Portal:Main>), and Memory Beta (<https://memory-beta.fandom.com/wiki/Main_Page>) to retrieve data, metadata and other information relating to Star Trek. It also contains several local datasets covering a variety of topics. The package also provides functions for working with data from other Star Trek-related R data packages containing larger datasets not stored in 'rtrek'.

quantdates - Manipulate Dates for Finance

Functions to manipulate dates and count days for quantitative finance analysis. The 'quantdates' package considers leap, holidays and business days for relevant calendars in a financial context to simplify quantitative finance calculations, consistent with International Swaps and Derivatives Association (ISDA) (2006) <https://www.isda.org/book/2006-isda-definitions/> regulations.

mlr3tuning - Hyperparameter Optimization for 'mlr3'

Hyperparameter optimization package of the 'mlr3' ecosystem. It features highly configurable search spaces via the 'paradox' package and finds optimal hyperparameter configurations for any 'mlr3' learner. 'mlr3tuning' works with several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically optimize learners and estimate the performance of optimized models with nested resampling.

mlr3hyperband - Hyperband for 'mlr3'

Successive Halving (Jamieson and Talwalkar (2016) <doi:10.48550/arXiv.1502.07943>) and Hyperband (Li et al. 2018 <doi:10.48550/arXiv.1603.06560>) optimization algorithm for the mlr3 ecosystem. The implementation in mlr3hyperband features improved scheduling and parallelizes the evaluation of configurations. The package includes tuners for hyperparameter optimization in mlr3tuning and optimizers for black-box optimization in bbotk.

mlr3fselect - Feature Selection for 'mlr3'

Feature selection package of the 'mlr3' ecosystem. It selects the optimal feature set for any 'mlr3' learner. The package works with several optimization algorithms e.g. Random Search, Recursive Feature Elimination, and Genetic Search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.

misty - Miscellaneous Functions 'T. Yanagida'

Miscellaneous functions for (1) data management (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation, multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), and (6) statistical analysis (e.g., confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination).

mcgf - Markov Chain Gaussian Fields Simulation and Parameter Estimation

Simulating and estimating (regime-switching) Markov chain Gaussian fields with covariance functions of the Gneiting class (Gneiting 2002) <doi:10.1198/016214502760047113>. It supports parameter estimation by weighted least squares and maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations.

greenR - Green Index Quantification, Analysis and Visualization

Quantification, analysis, and visualization of urban greenness within city networks using data from 'OpenStreetMap' <https://www.openstreetmap.org>.

gms - 'GAMS' Modularization Support Package

A collection of tools to create, use and maintain modularized model code written in the modeling language 'GAMS' (<https://www.gams.com/>). Out-of-the-box 'GAMS' does not come with support for modularized model code. This package provides the tools necessary to convert a standard 'GAMS' model to a modularized one by introducing a modularized code structure together with a naming convention which emulates local environments. In addition, this package provides tools to monitor the compliance of the model code with modular coding guidelines.

ggrcs - Draw Histograms and Restricted Cubic Splines (RCS)

You can use this function to easily draw a combined histogram and restricted cubic spline. The function draws the graph through 'ggplot2'. RCS fitting requires the use of the rcs() function of the 'rms' package. Can fit cox regression, logistic regression. This method was described by Per Kragh (2003) <doi:10.1002/sim.1497>.

fastTopics - Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

Implements fast, scalable optimization algorithms for fitting topic models ("grade of membership" models) and non-negative matrix factorizations to count data. The methods exploit the special relationship between the multinomial topic model (also, "probabilistic latent semantic indexing") and Poisson non-negative matrix factorization. The package provides tools to compare, annotate and visualize model fits, including functions to efficiently create "structure plots" and identify key features in topics. The 'fastTopics' package is a successor to the 'CountClust' package. For more information, see <doi:10.48550/arXiv.2105.13440> and <doi:10.1186/s13059-023-03067-9>.

cucumber - Behavior-Driven Development for R

Write executable specifications in a natural language that describes how your code should behave. Write specifications in feature files using 'Gherkin' language and execute them using functions implemented in R. Use them as an extension to your 'testthat' tests to provide a high level description of how your code works.

castor - Efficient Phylogenetics on Large Trees

Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.

PDXpower - Time to Event Outcome in Experimental Designs of Pre-Clinical Studies

Conduct simulation-based customized power calculation for clustered time to event data in a mixed crossed/nested design, where a number of cell lines and a number of mice within each cell line are considered to achieve a desired statistical power, motivated by Eckel-Passow and colleagues (2021) <doi:10.1093/neuonc/noab137> and Li and colleagues (2024) <doi:10.48550/arXiv.2404.08927>. This package provides two commonly used models for powering a design, linear mixed effects and Cox frailty model. Both models account for within-subject (cell line) correlation while holding different distributional assumptions about the outcome. Alternatively, the counterparts of fixed effects model are also available, which produces similar estimates of statistical power.

CohortSurvival - Estimate Survival from Common Data Model Cohorts

Estimate survival using data mapped to the Observational Medical Outcomes Partnership common data model. Survival can be estimated based on user-defined study cohorts.

toscutil - Utility Functions

Base R sometimes requires verbose statements for simple, often recurring tasks, such as printing text without trailing space, ending with newline. This package aims at providing shorthands for such tasks.

tagtools - Work with Data from High-Resolution Biologging Tags

High-resolution movement-sensor tags typically include accelerometers to measure body posture and sudden movements or changes in speed, magnetometers to measure direction of travel, and pressure sensors to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; calibration (from raw data to calibrated data in scientific units); visualization (for example, multi-panel time-series plots); data processing (such as event detection, calculation of derived metrics like jerk and dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis).

swagger - Dynamically Generates Documentation from a 'Swagger' Compliant API

A collection of 'HTML', 'JavaScript', and 'CSS' assets that dynamically generate beautiful documentation from a 'Swagger' compliant API: <https://swagger.io/specification/>.

stpp - Space-Time Point Pattern Simulation, Visualisation and Analysis

Many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena are covered in 'stpp'. This package provides statistical tools for analyzing the global and local second-order properties of spatio-temporal point processes, including estimators of the space-time inhomogeneous K-function and pair correlation function. It also includes tools to get static and dynamic display of spatio-temporal point patterns. See Gabriel et al (2013) <doi:10.18637/jss.v053.i02>.

stops - Structure Optimized Proximity Scaling

Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices.

statBasics - Basic Functions to Statistical Methods Course

Basic statistical methods with some modifications for the course Statistical Methods at Federal University of Bahia (Brazil). All methods in this packages are explained in the text book of Montgomery and Runger (2010) <ISBN: 978-1-119-74635-5>.

spatstat.univar - One-Dimensional Probability Distribution Support for the 'spatstat' Family

Estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, kernel properties, quantiles and integration.

segregatr - Segregation Analysis for Variant Interpretation

An implementation of the full-likelihood Bayes factor (FLB) for evaluating segregation evidence in clinical medical genetics. The method was introduced by Thompson et al. (2003) <doi:10.1086/378100>. This implementation supports custom penetrance values and liability classes, and allows visualisations and robustness analysis as presented in Ratajska et al. (2023) <doi:10.1002/mgg3.2107>. See also the online app 'shinyseg', <https://chrcarrizosa.shinyapps.io/shinyseg>, which offers interactive segregation analysis with many additional features (Carrizosa et al. (2024) <doi:10.1093/bioinformatics/btae201>).

rsparse -

rapidsplithalf - A Fast Split-Half Reliability Algorithm

Accurately estimates the reliability of cognitive tasks using a fast and flexible permutated split-half reliability algorithm that supports stratified splitting while maintaining equal split sizes. See Kahveci, Bathke, and Blechert (2022) <doi:10.31234/osf.io/ta59r> for details.

qlifetable - Managing and Building of Quarterly Life Tables

Manages, builds and computes statistics and datasets for the construction of quarterly (sub-annual) life tables by exploiting micro-data from either a general or an insured population. References: Pavía and Lledó (2022) <doi:10.1111/rssa.12769>. Pavía and Lledó (2023) <doi:10.1017/asb.2023.16>. Acknowledgements: The authors wish to thank Consellería de Educación, Universidades y Empleo, Generalitat Valenciana (grant AICO/2021/257), Ministerio de Ciencia e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre (grant 'Modelización espacial e intra-anual de la mortalidad en España. Una herramienta automática para el cálculo de productos de vida') for supporting this research.

poweRbal - Phylogenetic Tree Models and the Power of Tree Shape Statistics

The first goal of this package is to provide a multitude of tree models, i.e., functions that generate rooted binary trees with a given number of leaves. Second, the package allows for an easy evaluation and comparison of tree shape statistics by estimating their power to differentiate between different tree models. Please note that this R package was developed alongside the manuscript "Tree balance in phylogenetic models" by S. J. Kersting, K. Wicke, and M. Fischer (2024) <doi:10.48550/arXiv.2406.05185>, which provides further background and the respective mathematical definitions. This project was supported by the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM – Artificial Intelligence in Farming funded by the German Federal Ministry of Education and Research (No. 03WIR4805).

pedtools - Creating and Working with Pedigrees and Marker Data

A comprehensive collection of tools for creating, manipulating and visualising pedigrees and genetic marker data. Pedigrees can be read from text files or created on the fly with built-in functions. A range of utilities enable modifications like adding or removing individuals, breaking loops, and merging pedigrees. An online tool for creating pedigrees interactively, based on 'pedtools', is available at <https://magnusdv.shinyapps.io/quickped>. 'pedtools' is the hub of the 'pedsuite', a collection of packages for pedigree analysis. A detailed presentation of the 'pedsuite' is given in the book 'Pedigree Analysis in R' (Vigeland, 2021, ISBN:9780128244302).

osmapiR - 'OpenStreetMap' API

Interface to 'OpenStreetMap API' for fetching and saving data from/to the 'OpenStreetMap' database (<https://wiki.openstreetmap.org/wiki/API_v0.6>).

nimbleHMC - Hamiltonian Monte Carlo and Other Gradient-Based MCMC Sampling Algorithms for 'nimble'

Provides gradient-based MCMC sampling algorithms for use with the MCMC engine provided by the 'nimble' package. This includes two versions of Hamiltonian Monte Carlo (HMC) No-U-Turn (NUTS) sampling, and (under development) Langevin samplers. The `NUTS_classic` sampler implements the original HMC-NUTS algorithm as described in Hoffman and Gelman (2014) <doi:10.48550/arXiv.1111.4246>. The `NUTS` sampler is a modern version of HMC-NUTS sampling matching the HMC sampler available in version 2.32.2 of Stan (Stan Development Team, 2023). In addition, convenience functions are provided for generating and modifying MCMC configuration objects which employ HMC sampling.

movieROC - Visualizing the Decision Rules Underlying Binary Classification

Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations proposed in the literature: - making the classification subsets flexible to cover those scenarios where both extremes of the marker are associated with a higher risk of being positive, considering two thresholds (gROC() function); - transforming the marker by a proper function trying to improve the classification performance (hROC() function); - when dealing with multivariate markers, considering a proper transformation to univariate space trying to maximize the resulting AUC of the TPR for each FPR (multiROC() function). The classification regions behind each point of the ROC curve are displayed in both static graphics (plot_buildROC(), plot_regions() or plot_funregions() function) or videos (movieROC() function).

mlr3learners - Recommended Learners for 'mlr3'

Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.

mlr3 - Machine Learning in R - Next Generation

Efficient, object-oriented programming on the building blocks of machine learning. Provides 'R6' objects for tasks, learners, resamplings, and measures. The package is geared towards scalability and larger datasets by supporting parallelization and out-of-memory data-backends like databases. While 'mlr3' focuses on the core computational operations, add-on packages provide additional functionality.

mlflow - Interface to 'MLflow'

R interface to 'MLflow', open source platform for the complete machine learning life cycle, see <https://mlflow.org/>. This package supports installing 'MLflow', tracking experiments, creating and running projects, and saving and serving models.

microeco - Microbial Community Ecology Data Analysis

A series of statistical and plotting approaches in microbial community ecology based on the R6 class. The classes are designed for data preprocessing, taxa abundance plotting, alpha diversity analysis, beta diversity analysis, differential abundance test, null model analysis, network analysis, machine learning, environmental data analysis and functional analysis.

mgi.report.reader - Read Mouse Genome Informatics Reports

Provides readers for easy and consistent importing of Mouse Genome Informatics (MGI) report files: <https://www.informatics.jax.org/downloads/reports/index.html>. These data are provided by Baldarelli RM, Smith CL, Ringwald M, Richardson JE, Bult CJ, Mouse Genome Informatics Group. (2024) <doi:10.1093/genetics/iyae031>.

mexhaz - Mixed Effect Excess Hazard Models

Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty). A detailed description of the package functionalities is provided in Charvat and Belot (2021) <doi: 10.18637/jss.v098.i14>.

lqr - Robust Linear Quantile Regression

It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.

glmertree - Generalized Linear Mixed Model Trees

Recursive partitioning based on (generalized) linear mixed models (GLMMs) combining lmer()/glmer() from 'lme4' and lmtree()/glmtree() from 'partykit'. The fitting algorithm is described in more detail in Fokkema, Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>).

gggenomes - A Grammar of Graphics for Comparative Genomics

An extension of 'ggplot2' for creating complex genomic maps. It builds on the power of 'ggplot2' and 'tidyverse' adding new 'ggplot2'-style geoms & positions and 'dplyr'-style verbs to manipulate the underlying data. It implements a layout concept inspired by 'ggraph' and introduces tracks to bring tidiness to the mess that is genomics data.

gbm - Generalized Boosted Regression Models

An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway. Newer version available at github.com/gbm-developers/gbm3.

fullRankMatrix - Generation of Full Rank Design Matrix

Creates a full rank matrix out of a given matrix. The intended use is for one-hot encoded design matrices that should be used in linear models to ensure that significant associations can be correctly interpreted. However, 'fullRankMatrix' can be applied to any matrix to make it full rank. It removes columns with only 0's, merges duplicated columns and discovers linearly dependent columns and replaces them with linearly independent columns that span the space of the original columns. Columns are renamed to reflect those modifications. This results in a full rank matrix that can be used as a design matrix in linear models. The algorithm and some functions are inspired by Kuhn, M. (2008) <doi:10.18637/jss.v028.i05>.

foreign - Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', ...

Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.

fitODBOD - Modeling Over Dispersed Binomial Outcome Data Using BMD and ABD

Contains Probability Mass Functions, Cumulative Mass Functions, Negative Log Likelihood value, parameter estimation and modeling data using Binomial Mixture Distributions (BMD) (Manoj et al (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial Distributions (ABD) (Paul (1985) <doi:10.1080/03610928508828990>), also Journal article to use the package(<doi:10.21105/joss.01505>).

evitaicossa - Antiassociative Algebra

Methods to deal with the free antiassociative algebra over the reals with an arbitrary number of indeterminates. Antiassociativity means that (xy)z = -x(yz). Antiassociative algebras are nilpotent with nilindex four (Remm, 2022, <doi:10.48550/arXiv.2202.10812>) and this drives the design and philosophy of the package. Methods are defined to create and manipulate arbitrary elements of the antiassociative algebra, and to extract and replace coefficients. A vignette is provided.

epiCo - Statistical and Viz Tools for Vector-Borne Diseases in Colombia

Provides statistical and visualization tools for the analysis of demographic indicators, and spatio-temporal behavior and characterization of outbreaks of vector-borne diseases (VBDs) in Colombia. It implements travel times estimated in Bravo-Vega C., Santos-Vega M., & Cordovez J.M. (2022), and the endemic channel method (Bortman, M. (1999) <https://iris.paho.org/handle/10665.2/8562>).

easyWechat - A Notifier for R Users by 'WeChat'

This is a 'WeChat' Notifier for R users to notice when script run complete.

dynamicSDM - Species Distribution and Abundance Modelling at High Spatio-Temporal Resolution

A collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution.

dbscan - Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

A fast reimplementation of several density-based algorithms of the DBSCAN family. Includes the clustering algorithms DBSCAN (density-based spatial clustering of applications with noise) and HDBSCAN (hierarchical DBSCAN), the ordering algorithm OPTICS (ordering points to identify the clustering structure), shared nearest neighbor clustering, and the outlier detection algorithms LOF (local outlier factor) and GLOSH (global-local outlier score from hierarchies). The implementations use the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. An R interface to fast kNN and fixed-radius NN search is also provided. Hahsler, Piekenbrock and Doran (2019) <doi:10.18637/jss.v091.i01>.

convertBCD - Convert Decimal to Binary-Coded Decimal (BCD) Form and Vice Versa

Convert BCD (raw bytes) to decimal numbers and vice versa. BCD format is used to preserve decimals exactly, as opposed to the binary rounding errors inherent in "numeric" or "floating-point" formats.

cna - Causal Modeling with Coincidence Analysis

Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2018) <doi:10.1017/psrm.2018.45>. CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures.

clidamonger - Monthly Climate Data for Germany, Usable for Heating and Cooling Calculations

This data package contains monthly climate data in Germany, it can be used for heating and cooling calculations (external temperature, heating / cooling days, solar radiation).

clap - Detecting Class Overlapping Regions in Multidimensional Data

The issue of overlapping regions in multidimensional data arises when different classes or clusters share similar feature representations, making it challenging to delineate distinct boundaries between them accurately. This package provides methods for detecting and visualizing these overlapping regions using partitional clustering techniques based on nearest neighbor distances.

cit - Causal Inference Test

A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. Described in Millstein, Chen, and Breton (2016), <DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.

cartographr - Crafting Print-Ready Maps and Layered Visualizations

Simplifying the creation of print-ready maps, this package offers a user-friendly interface derived from 'ggplot2' for handling OpenStreetMap data. It streamlines the map-making process, allowing users to focus on the story their maps tell. Transforming raw geospatial data into informative visualizations is made easy with simple features 'sf' geometries. Whether for urban planning, environmental studies, or impactful public presentations, this tool facilitates straightforward and effective map creation. Enhance the dissemination of spatial information with high-quality, narrative-driven visualizations!

bsvars - Bayesian Estimation of Structural Vector Autoregressive Models

Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>.

bigBits - Perform Boolean Operations on Large Numbers

A set of Boolean operators which accept integers of any size, in any base from 2 to 36, including 2's complement format, and perform actions like "AND," "OR", "NOT", "SHIFTR/L" etc. The output can be in any base specified. A direct base to base converter is included.

bbotk - Black-Box Optimization Toolkit

Features highly configurable search spaces via the 'paradox' package and optimizes every user-defined objective function. The package includes several optimization algorithms e.g. Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband (in 'mlr3hyperband'). bbotk is the base package of 'mlr3tuning', 'mlr3fselect' and 'miesmuschel'.

TMB - Template Model Builder: A General Random Effect Tool Inspired by 'ADMB'

With this tool, a user should be able to quickly implement complex random effect models through simple C++ templates. The package combines 'CppAD' (C++ automatic differentiation), 'Eigen' (templated matrix-vector library) and 'CHOLMOD' (sparse matrix routines available from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through 'BLAS' and parallel user templates.

Rlabkey - Data Exchange Between R and 'LabKey' Server

The 'LabKey' client library for R makes it easy for R users to load live data from a 'LabKey' Server, <https://www.labkey.com/>, into the R environment for analysis, provided users have permissions to read the data. It also enables R users to insert, update, and delete records stored on a 'LabKey' Server, provided they have appropriate permissions to do so.

PieGlyph - Axis Invariant Scatter Pie Plots

Extends 'ggplot2' to help replace points in a scatter plot with pie-chart glyphs showing the relative proportions of different categories. The pie glyphs are independent of the axes and plot dimensions, to prevent distortions when the plot dimensions are changed.

Nonpareil - Metagenome Coverage Estimation and Projections for 'Nonpareil'

Plot, process, and analyze NPO files produced by 'Nonpareil' <http://enve-omics.ce.gatech.edu/nonpareil/>.

MagmaClustR - Clustering and Prediction using Multi-Task Gaussian Processes with Common Mean

An implementation for the multi-task Gaussian processes with common mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', are available to perform predictions for supervised learning problems, in particular for time series or any functional/continuous data applications. The corresponding articles has been respectively proposed by Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2022) <doi:10.1007/s10994-022-06172-1>, and Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2023) <https://jmlr.org/papers/v24/20-1321.html>. Theses approaches leverage the learning of cluster-specific mean processes, which are common across similar tasks, to provide enhanced prediction performances (even far from data) at a linear computational cost (in the number of tasks). 'MagmaClust' is a generalisation of 'Magma' where the tasks are simultaneously clustered into groups, each being associated to a specific mean process. User-oriented functions in the package are decomposed into training, prediction and plotting functions. Some basic features (classic kernels, training, prediction) of standard Gaussian processes are also implemented.

LogisticCopula - A Copula Based Extension of Logistic Regression

An implementation of a method of extending a logistic regression model beyond linear effects of the co-variates. The extension in is constructed by first equating the logistic regression model to a naive Bayes model where all the margins are specified to follow natural exponential distributions conditional on Y, that is, a model for Y given X that is specified through the distribution of X given Y, where the columns of X are assumed to be mutually independent conditional on Y. Subsequently, the model is expanded by adding vine - copulas to relax the assumption of mutual independence, where pair-copulas are added in a stage-wise, forward selection manner. Some heuristics are employed during the process of selecting edges, as well as the families of pair-copula models. After each component is added, the parameters are updated by a (smaller) number of gradient steps to maximise the likelihood. When the algorithm has stopped adding edges, based the criterion that a new edge should improve the likelihood more than k times the number new parameters, the parameters are updated with a larger number of gradient steps, or until convergence.

GaussSuppression - Tabular Data Suppression using Gaussian Elimination

A statistical disclosure control tool to protect tables by suppression using the Gaussian elimination secondary suppression algorithm. A suggestion is to start by working with functions SuppressSmallCounts() and SuppressDominantCells(). These functions use primary suppression functions for the minimum frequency rule and the dominance rule, respectively. Novel functionality for suppression of disclosive cells is also included. General primary suppression functions can be supplied as input to the general working horse function, GaussSuppressionFromData(). Suppressed frequencies can be replaced by synthetic decimal numbers as described in Langsrud (2019) <doi:10.1007/s11222-018-9848-9>.

DIDmultiplegtDYN - Estimation in Difference-in-Difference Designs with Multiple Groups and Periods

Estimation of event-study Difference-in-Difference (DID) estimators in designs with multiple groups and periods, and with a potentially non-binary treatment that may increase or decrease multiple times.

DEPONS2R - Read, Plot and Analyse Output from the DEPONS Model

Methods for analyzing population dynamics and movement tracks simulated using the DEPONS model <https://www.depons.eu> (v.3.0), for manipulating input raster files, shipping routes and for analyzing sound propagated from ships.

CommEcol - Community Ecology Analyses

Autosimilarity curves, standardization of spatial extent, dissimilarity indexes that overweight rare species, phylogenetic and functional (pairwise and multisample) dissimilarity indexes and nestedness for phylogenetic, functional and other diversity metrics. The methods for phylogenetic and functional nestedness is described in Melo, Cianciaruso and Almeida-Neto (2014) <doi:10.1111/2041-210X.12185>. This should be a complement to available packages, particularly 'vegan'.

CWT - Continuous Wavelet Transformation for Spectroscopy

Fast application of Continuous Wavelet Transformation ('CWT') on time series with special attention to spectroscopy. It is written using data.table and 'C++' language and in some functions it is possible to use parallel processing to speed-up the computation over samples. Currently, only the second derivative of a Gaussian wavelet function is implemented.

AssocBin - Measuring Association with Recursive Binning

An iterative implementation of a recursive binary partitioning algorithm to measure pairwise dependence with a modular design that allows user specification of the splitting logic and stop criteria. Helper functions provide suggested versions of both and support visualization and the computation of summary statistics on final binnings. For a complete description of the functionality and algorithm, see Salahub and Oldford (2023) <doi:10.48550/arXiv.2311.08561>.

roxygen2 - In-Line Documentation for R

Generate your Rd documentation, 'NAMESPACE' file, and collation field using specially formatted comments. Writing documentation in-line with code makes it easier to keep your documentation up-to-date as your requirements change. 'roxygen2' is inspired by the 'Doxygen' system for C++.

pkgload - Simulate Package Installation and Attach

Simulates the process of installing a package and then attaching it. This is a key part of the 'devtools' package as it allows you to rapidly iterate while developing a package.

zoltr - Interface to the 'Zoltar' Forecast Repository API

'Zoltar' <https://www.zoltardata.com/> is a website that provides a repository of model forecast results in a standardized format and a central location. It supports storing, retrieving, comparing, and analyzing time series forecasts for prediction challenges of interest to the modeling community. This package provides functions for working with the 'Zoltar' API, including connecting and authenticating, getting meta information (projects, models, and forecasts, and truth), and uploading, downloading, and deleting forecast and truth data.

waywiser - Ergonomic Methods for Assessing Spatial Models

Assessing predictive models of spatial data can be challenging, both because these models are typically built for extrapolating outside the original region represented by training data and due to potential spatially structured errors, with "hot spots" of higher than expected error clustered geographically due to spatial structure in the underlying data. Methods are provided for assessing models fit to spatial data, including approaches for measuring the spatial structure of model errors, assessing model predictions at multiple spatial scales, and evaluating where predictions can be made safely. Methods are particularly useful for models fit using the 'tidymodels' framework. Methods include Moran's I ('Moran' (1950) <doi:10.2307/2332142>), Geary's C ('Geary' (1954) <doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from 'Ji' and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement metrics from 'Willmott' (1981) (<doi: 10.1080/02723646.1981.10642213>) and 'Willmott' 'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an implementation of the area of applicability methodology from 'Meyer' and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an implementation of multi-scale assessment as described in 'Riemann' 'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).

tsibble - Tidy Temporal Data Frames and Tools

Provides a 'tbl_ts' class (the 'tsibble') for temporal data in an data- and model-oriented format. The 'tsibble' provides tools to easily manipulate and analyse temporal data, such as filling in time gaps and aggregating over calendar periods.

tok - Fast Text Tokenization

Interfaces with the 'Hugging Face' tokenizers library to provide implementations of today's most used tokenizers such as the 'Byte-Pair Encoding' algorithm <https://huggingface.co/docs/tokenizers/index>. It's extremely fast for both training new vocabularies and tokenizing texts.

tfevents - Write Events for 'TensorBoard'

Provides a convenient way to log scalars, images, audio, and histograms in the 'tfevent' record file format. Logged data can be visualized on the fly using 'TensorBoard', a web based tool that focuses on visualizing the training progress of machine learning models.

simpletex - Mathematical Formulas and Character Recognition

By calling the 'SimpleTex' <https://simpletex.cn/> open API implements text and mathematical formula recognition on the image, and the output formula can be used directly with 'Markdown' and 'LaTeX'.

scorematchingad - Score Matching Estimation by Automatic Differentiation

Hyvärinen's score matching (Hyvärinen, 2005) <https://jmlr.org/papers/v6/hyvarinen05a.html> is a useful estimation technique when the normalising constant for a probability distribution is difficult to compute. This package implements score matching estimators using automatic differentiation in the 'CppAD' library <https://github.com/coin-or/CppAD> and is designed for quickly implementing score matching estimators for new models. Also available is general robustification (Windham, 1995) <https://www.jstor.org/stable/2346159>. Already in the package are estimators for directional distributions (Mardia, Kent and Laha, 2016) <doi:10.48550/arXiv.1604.08470> and the flexible Polynomially-Tilted Pairwise Interaction model for compositional data. The latter estimators perform well when there are zeros in the compositions (Scealy and Wood, 2023) <doi:10.1080/01621459.2021.2016422>, even many zeros (Scealy, Hingee, Kent, and Wood, 2024) <doi:10.1007/s11222-024-10412-w>.

rtables - Reporting Tables

Reporting tables often have structure that goes beyond simple rectangular data. The 'rtables' package provides a framework for declaring complex multi-level tabulations and then applying them to data. This framework models both tabulation and the resulting tables as hierarchical, tree-like objects which support sibling sub-tables, arbitrary splitting or grouping of data in row and column dimensions, cells containing multiple values, and the concept of contextual summary computations. A convenient pipe-able interface is provided for declaring table layouts and the corresponding computations, and then applying them to data.

rsi - Efficiently Retrieve and Process Satellite Imagery

Downloads spatial data from spatiotemporal asset catalogs ('STAC'), computes standard spectral indices from the Awesome Spectral Indices project (Montero et al. (2023) <doi:10.1038/s41597-023-02096-0>) against raster data, and glues the outputs together into predictor bricks. Methods focus on interoperability with the broader spatial ecosystem; function arguments and outputs use classes from 'sf' and 'terra', and data downloading functions support complex 'CQL2' queries using 'rstac'.

rpmodel - P-Model

Implements the P-model (Stocker et al., 2020 <doi:10.5194/gmd-13-1545-2020>), predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis, assimilation, and dark respiration rates as a function of the environment (temperature, CO2, vapour pressure deficit, light, atmospheric pressure).

roads - Road Network Projection

Iterative least cost path and minimum spanning tree methods for projecting forest road networks. The methods connect a set of target points to an existing road network using 'igraph' <https://igraph.org> to identify least cost routes. The cost of constructing a road segment between adjacent pixels is determined by a user supplied weight raster and a weight function; options include the average of adjacent weight raster values, and a function of the elevation differences between adjacent cells that penalizes steep grades. These road network projection methods are intended for integration into R workflows and modelling frameworks used for forecasting forest change, and can be applied over multiple time-steps without rebuilding a graph at each time-step.

psych - Procedures for Psychological, Psychometric, and Personality Research

A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.

phoenics - Pathways Longitudinal and Differential Analysis in Metabolomics

Perform a differential analysis at pathway level based on metabolite quantifications and information on pathway metabolite composition. The method is based on a Principal Component Analysis step and on a linear mixed model. Automatic query of metabolic pathways is also implemented.

nphRCT - Non-Proportional Hazards in Randomized Controlled Trials

Perform a stratified weighted log-rank test in a randomized controlled trial. Tests can be visualized as a difference in average score on the two treatment arms. These methods are described in Magirr and Burman (2018) <doi:10.48550/arXiv.1807.11097>, Magirr (2020) <doi:10.48550/arXiv.2007.04767>, and Magirr and Jimenez (2022) <doi:10.48550/arXiv.2201.10445>.

nimbleEcology - Distributions for Ecological Models in 'nimble'

Common ecological distributions for 'nimble' models in the form of nimbleFunction objects. Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models. (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).

mpath - Regularized Linear Models

Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2021) <doi:10.1007/s11749-021-00770-2>, Wang (2024) <doi:10.1111/anzs.12409>.

mlts - Multilevel Latent Time Series Models with 'R' and 'Stan'

Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using 'knitr'. Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.

mergen - AI-Driven Code Generation, Explanation and Execution for Data Analysis

Employing artificial intelligence to convert data analysis questions into executable code, explanations, and algorithms. The self-correction feature ensures the generated code is optimized for performance and accuracy. 'mergen' features a user-friendly chat interface, enabling users to interact with the AI agent and extract valuable insights from their data effortlessly.

lmtp - Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes.

lme4breeding - Relationship-Based Mixed-Effects Models

Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'.

khisr - An R Client to Retrieve Data from DHIS2

Provides a user-friendly interface for interacting with the District Health Information Software 2 (DHIS2) instance. It streamlines data retrieval, empowering researchers, analysts, and healthcare professionals to obtain and utilize data efficiently.

ggpmisc - Miscellaneous Extensions to 'ggplot2'

Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Statistics: locate and tag peaks and valleys; label plot with the equation of a fitted polynomial or other types of models; labels with P-value, R^2 or adjusted R^2 or information criteria for fitted models; label with ANOVA table for fitted models; label with summary for fitted models. Model fit classes for which suitable methods are provided by package 'broom' and 'broom.mixed' are supported. Scales and stats to build volcano and quadrant plots based on outcomes, fold changes, p-values and false discovery rates.

geommc - Geometric Markov Chain Sampling

Simulates from discrete and continuous target distributions using geometric Metropolis-Hastings (MH) algorithms. Users specify the target distribution by an R function that evaluates the log un-normalized pdf or pmf. The package also contains a function implementing a specific geometric MH algorithm for performing high dimensional Bayesian variable selection.

geohabnet - Geographical Risk Analysis Based on Habitat Connectivity

The geohabnet package is designed to perform a geographically or spatially explicit risk analysis of habitat connectivity. Xing et al (2021) <doi:10.1093/biosci/biaa067> proposed the concept of cropland connectivity as a risk factor for plant pathogen or pest invasions. As the functions in geohabnet were initially developed thinking on cropland connectivity, users are recommended to first be familiar with the concept by looking at the Xing et al paper. In a nutshell, a habitat connectivity analysis combines information from maps of host density, estimates the relative likelihood of pathogen movement between habitat locations in the area of interest, and applies network analysis to calculate the connectivity of habitat locations. The functions of geohabnet are built to conduct a habitat connectivity analysis relying on geographic parameters (spatial resolution and spatial extent), dispersal parameters (in two commonly used dispersal kernels: inverse power law and negative exponential models), and network parameters (link weight thresholds and network metrics). The functionality and main extensions provided by the functions in geohabnet to habitat connectivity analysis are a) Capability to easily calculate the connectivity of locations in a landscape using a single function, such as sensitivity_analysis() or msean(). b) As backbone datasets, the geohabnet package supports the use of two publicly available global datasets to calculate cropland density. The backbone datasets in the geohabnet package include crop distribution maps from Monfreda, C., N. Ramankutty, and J. A. Foley (2008) <doi:10.1029/2007gb002947> "Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022" and International Food Policy Research Institute (2019) <doi:10.7910/DVN/PRFF8V> "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, Harvard Dataverse, V4". Users can also provide any other geographic dataset that represents host density. c) Because the geohabnet package allows R users to provide maps of host density (as originally in Xing et al (2021)), host landscape density (representing the geographic distribution of either crops or wild species), or habitat distribution (such as host landscape density adjusted by climate suitability) as inputs, we propose the term habitat connectivity. d) The geohabnet package allows R users to customize parameter values in the habitat connectivity analysis, facilitating context-specific (pathogen- or pest-specific) analyses. e) The geohabnet package allows users to automatically visualize maps of the habitat connectivity of locations resulting from a sensitivity analysis across all customized parameter combinations. The primary function is sean() and sensitivity analysis(). Most functions in geohabnet provide as three main outcomes: i) A map of mean habitat connectivity across parameters selected by the user, ii) a map of variance of habitat connectivity across the selected parameters, and iii) a map of the difference between the ranks of habitat connectivity and habitat density. Each function can be used to generate these maps as 'final' outcomes. Each function can also provide intermediate outcomes, such as the adjacency matrices built to perform the analysis, which can be used in other network analysis. Refer to article at <https://garrettlab.github.io/HabitatConnectivity/articles/analysis.html> to see examples of each function and how to access each of these outcome types. To change parameter values, the file called parameters.yaml stores the parameters and their values, can be accessed using get_parameters() and set new parameter values with set_parameters(). Users can modify up to ten parameters.

frailtypack - Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

fields - Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

escalation - A Modular Approach to Dose-Finding Clinical Trials

Methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from 'magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients.

ecce - Translate English Sentence into Chinese, or Translate Chinese Sentence into English

If translate English or Chinese sentence, there is a faster way for R user. You can pass in an English or Chinese sentence, ecce package support both English and Chinese translation. It also support browse translation results in website. In addition, also support obtain the pinyin of the Chinese character, you can more easily understand the pronunciation of the Chinese character.

dipsaus - A Dipping Sauce for Data Analysis and Visualizations

Works as an "add-on" to packages like 'shiny', 'future', as well as 'rlang', and provides utility functions. Just like dipping sauce adding flavors to potato chips or pita bread, 'dipsaus' for data analysis and visualizations adds handy functions and enhancements to popular packages. The goal is to provide simple solutions that are frequently asked for online, such as how to synchronize 'shiny' inputs without freezing the app, or how to get memory size on 'Linux' or 'MacOS' system. The enhancements roughly fall into these four categories: 1. 'shiny' input widgets; 2. high-performance computing using the 'future' package; 3. modify R calls and convert among numbers, strings, and other objects. 4. utility functions to get system information such like CPU chip-set, memory limit, etc.

datagovsgR - Call Real Time APIs from Data Gov Singapore

A wrapper for the Data.gov.sg developer resources, which provide real time and historical information, ranging from carpark availability to weather forecasts. The functions makes the API calls for a given date and time, before returning the relevant information in a data frame. All APIs are supported, less the IPOS one which is not returning any data. Relevant information can be found here <https://data.gov.sg/developer>.

dartR.popgen - Analysing 'SNP' and 'Silicodart' Data Generated by Genome-Wide Restriction Fragment Analysis

Facilitates the analysis of SNP (single nucleotide polymorphism) and silicodart (presence/absence) data. 'dartR.popgen' provides a suit of functions to analyse such data in a population genetics context. It provides several functions to calculate population genetic metrics and to study population structure. Quite a few functions need additional software to be able to run (gl.run.structure(), gl.blast(), gl.LDNe()). You find detailed description in the help pages how to download and link the packages so the function can run the software. 'dartR.popgen' is part of the the 'dartRverse' suit of packages. Gruber et al. (2018) <doi:10.1111/1755-0998.12745>. Mijangos et al. (2022) <doi:10.1111/2041-210X.13918>.

dacc - Detection and Attribution Analysis of Climate Change

Conduct detection and attribution of climate change using methods including optimal fingerprinting via generalized total least squares or estimating equation approach from Ma et al. (2023) <doi:10.1175/JCLI-D-22-0681.1>. Provide shrinkage estimators for covariance matrix from Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>, and Ledoit and Wolf (2017) <doi:10.2139/ssrn.2383361>.

cops - Cluster Optimized Proximity Scaling

Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly (COPS-C) with given explicit power transformations and implicit ratio, interval and non-metric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm'' (Rusch, Mair & Hornik, 2021,<doi:10.1080/10618600.2020.1869027>) and a functions to calculate the phi-distances for count data or histograms.

bayesCureRateModel - Bayesian Cure Rate Modeling for Time-to-Event Data

A fully Bayesian approach in order to estimate a general family of cure rate models under the presence of covariates, see Papastamoulis and Milienos (2023) <doi:10.48550/arXiv.2310.06926>. The promotion time can be modelled (a) parametrically using typical distributional assumptions for time to event data (including the Weibull, Exponential, Gompertz, log-Logistic distributions), or (b) semiparametrically using finite mixtures of Gamma distributions. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings steps with Langevin diffusion dynamics for parameter updates. The main MCMC algorithm is embedded within a parallel tempering scheme by considering heated versions of the target posterior distribution.

antaresViz - Antares Visualizations

Visualize results generated by Antares, a powerful open source software developed by RTE to simulate and study electric power systems (more information about 'Antares' here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). This package provides functions that create interactive charts to help 'Antares' users visually explore the results of their simulations.

UComp - Automatic Univariate Time Series Modelling of many Kinds

Comprehensive analysis and forecasting of univariate time series using automatic time series models of many kinds. Harvey AC (1989) <doi:10.1017/CBO9781107049994>. Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>. Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>. Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>. Gómez V, Maravall A (2000) <doi:10.1002/9781118032978>. Pedregal DJ, Trapero JR and Holgado E (2024) <doi:10.1016/j.ijforecast.2023.09.004>.

SPARTAAS - Statistical Pattern Recognition and daTing using Archaeological Artefacts assemblageS

Statistical pattern recognition and dating using archaeological artefacts assemblages. Package of statistical tools for archaeology. hclustcompro()/perioclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021, ISBN:978-3-030-60103-4). mapclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021) <doi:10.1016/j.jas.2021.105431>. seriograph(): Desachy Bruno (2004) <doi:10.3406/pica.2004.2396>. cerardat(): Bellanger Lise, Husi Philippe (2012) <doi:10.1016/j.jas.2011.06.031>.

Rvcg - Manipulations of Triangular Meshes Based on the 'VCGLIB' API

Operations on triangular meshes based on 'VCGLIB'. This package integrates nicely with the R-package 'rgl' to render the meshes processed by 'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is an open source portable C++ templated library for manipulation, processing and displaying with OpenGL of triangle and tetrahedral meshes. The library, composed by more than 100k lines of code, is released under the GPL license, and it is the base of most of the software tools of the Visual Computing Lab of the Italian National Research Council Institute ISTI <https://vcg.isti.cnr.it/>, like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk <https://github.com/cnr-isti-vclab/vcglib> and patched to work with options determined by the configure script as well as to work with the header files included by 'RcppEigen'.

Robyn - Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science

Semi-Automated Marketing Mix Modeling (MMM) aiming to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making providing a budget allocation and diminishing returns curves and allows ground-truth calibration to account for causation.

RiskScorescvd - Cardiovascular Risk Scores Calculator

A tool to calculate Cardiovascular Risk Scores in large data frames. Cardiovascular risk scores are statistical tools used to assess an individual's likelihood of developing a cardiovascular disease based on various risk factors, such as age, gender, blood pressure, cholesterol levels, and smoking. Here we bring together the six most commonly used in the emergency department. Using 'RiskScorescvd', you can calculate all the risk scores in an extended dataset in seconds. PCE (ASCVD) described in Goff, et al (2013) <doi:10.1161/01.cir.0000437741.48606.98>. EDACS described in Mark DG, et al (2016) <doi:10.1016/j.jacc.2017.11.064>. GRACE described in Fox KA, et al (2006) <doi:10.1136/bmj.38985.646481.55>. HEART is described in Mahler SA, et al (2017) <doi:10.1016/j.clinbiochem.2017.01.003>. SCORE2/OP described in SCORE2 working group and ESC Cardiovascular risk collaboration (2021) <doi:10.1093/eurheartj/ehab309>. TIMI described in Antman EM, et al (2000) <doi:10.1001/jama.284.7.835>. SCORE2-Diabetes described in SCORE2-Diabetes working group and ESC Cardiovascular risk collaboration (2023) <doi:10.1093/eurheartj/ehab260>. SCORE2/OP with CKD add-on described in Kunihiro M et al (2022) <doi:10.1093/eurjpc/zwac176>.

PeakSegJoint - Joint Peak Detection in Several ChIP-Seq Samples

Jointly segment several ChIP-seq samples to find the peaks which are the same and different across samples. The fast approximate maximum Poisson likelihood algorithm is described in "PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples" <doi:10.48550/arXiv.1506.01286> by TD Hocking and G Bourque.

OlinkAnalyze - Facilitate Analysis of Proteomic Data from Olink

A collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform.

Mhorseshoe - Approximate Algorithm for Horseshoe Prior

Provides exact and approximate algorithms for the horseshoe prior in linear regression models, which were proposed by Johndrow et al. (2020) <https://www.jmlr.org/papers/v21/19-536.html>.

GaSP - Train and Apply a Gaussian Stochastic Process Model

Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>. Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.

FAIRmaterials - Ontology Tools with Data FAIRification in Development

Translates several CSV files with ontological terms and corresponding data into RDF triples. These RDF triples are stored in OWL and JSON-LD files, facilitating data accessibility, interoperability, and knowledge unification. The triples are also visualized in a graph saved as an SVG. The input CSVs must be formatted with a template from a public Google Sheet; see README or vignette for more information. This is a tool is used by the SDLE Research Center at Case Western Reserve University to create and visualize material science ontologies, and it includes example ontologies to demonstrate its capabilities. This work was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Numbers E-EE0009353 and DE-EE0009347, Department of Energy (National Nuclear Security Administration) under Award Number DE-NA0004104 and Contract number B647887, and U.S. National Science Foundation Award under Award Number 2133576.

EdSurvey - Analysis of NCES Education Survey and Assessment Data

Read in and analyze functions for education survey and assessment data from the National Center for Education Statistics (NCES) <https://nces.ed.gov/>, including National Assessment of Educational Progress (NAEP) data <https://nces.ed.gov/nationsreportcard/> and data from the International Assessment Database: Organisation for Economic Co-operation and Development (OECD) <https://www.oecd.org/>, including Programme for International Student Assessment (PISA), Teaching and Learning International Survey (TALIS), Programme for the International Assessment of Adult Competencies (PIAAC), and International Association for the Evaluation of Educational Achievement (IEA) <https://www.iea.nl/>, including Trends in International Mathematics and Science Study (TIMSS), TIMSS Advanced, Progress in International Reading Literacy Study (PIRLS), International Civic and Citizenship Study (ICCS), International Computer and Information Literacy Study (ICILS), and Civic Education Study (CivEd).

CVXR - Disciplined Convex Optimization

An object-oriented modeling language for disciplined convex programming (DCP) as described in Fu, Narasimhan, and Boyd (2020, <doi:10.18637/jss.v094.i14>). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution. Interfaces to solvers on CRAN and elsewhere are provided, both commercial and open source.

BRBVS - Variable Selection and Ranking in Copula Survival Models Affected by General Censoring Scheme

Performs variable selection and ranking based on several measures for the class of copula survival model(s) in high dimensional domain. The package is based on the class of copula survival model(s) implemented in the 'GJRM' package.

verbalisr - Describe Pedigree Relationships in Words

Describe in words the genealogical relationship between two members of a given pedigree, using the algorithm in Vigeland (2022) <doi:10.1186/s12859-022-04759-y>. 'verbalisr' is part of the 'pedsuite' collection of packages for pedigree analysis. For a demonstration of 'verbalisr', see the online app 'QuickPed' at <https://magnusdv.shinyapps.io/quickped>.

usedthese - Summarises Package & Function Usage

Consistent with 'knitr' syntax highlighting, 'usedthese' adds a summary table of package & function usage to a Quarto document and enables aggregation of usage across a website.

tutorial.helpers - Helper Functions for Creating Tutorials

Helper functions for creating, editing, and testing tutorials created with the 'learnr' package. Provides a simple method for allowing students to download their answers to tutorial questions. For examples of its use, see the 'r4ds.tutorials' and 'primer.tutorials' packages.

tables - Formula-Driven Table Generation

Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.

sparsegl - Sparse Group Lasso

Efficient implementation of sparse group lasso with optional bound constraints on the coefficients. It supports the use of a sparse design matrix as well as returning coefficient estimates in a sparse matrix. Furthermore, it correctly calculates the degrees of freedom to allow for information criteria rather than cross-validation with very large data. Finally, the interface to compiled code avoids unnecessary copies and allows for the use of long integers.

sparseR - Variable Selection under Ranked Sparsity Principles for Interactions and Polynomials

An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods.

signal - Signal Processing

A set of signal processing functions originally written for 'Matlab' and 'Octave'. Includes filter generation utilities, filtering functions, resampling routines, and visualization of filter models. It also includes interpolation functions.

rsatscan - Tools, Classes, and Methods for Interfacing with 'SaTScan' Stand-Alone Software

'SaTScan'(TM) <https://www.satscan.org> is software for finding regions in Time, Space, or Time-Space that have excess risk, based on scan statistics, and uses Monte Carlo hypothesis testing to generate P-values for these regions. The 'rsatscan' package provides functions for writing R data frames in 'SaTScan'-readable formats, for setting 'SaTScan' parameters, for running 'SaTScan' in the OS, and for reading the files that 'SaTScan' creates.

rrapply - Revisiting Base Rapply

The minimal 'rrapply'-package contains a single function rrapply(), providing an extended implementation of 'R'-base rapply() by allowing to recursively apply a function to elements of a nested list based on a general condition function and including the possibility to prune or aggregate nested list elements from the result. In addition, special arguments can be supplied to access the name, location, parents and siblings in the nested list of the element under evaluation. The rrapply() function builds upon rapply()'s native 'C' implementation and requires no other package dependencies.

readme2vignette - Convert README to Vignette During Package Installation

Convert README.md to vignettes when installing packages without vignettes.

reactR - React Helpers

Make it easy to use 'React' in R with 'htmlwidget' scaffolds, helper dependency functions, an embedded 'Babel' 'transpiler', and examples.

pbkrtest - Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models

Computes p-values based on (a) Satterthwaite or Kenward-Rogers degree of freedom methods and (b) parametric bootstrap for mixed effects models as implemented in the 'lme4' package. Implements parametric bootstrap test for generalized linear mixed models as implemented in 'lme4' and generalized linear models. The package is documented in the paper by Halekoh and Højsgaard, (2012, <doi:10.18637/jss.v059.i09>). Please see 'citation("pbkrtest")' for citation details.

ows4R - Interface to OGC Web-Services (OWS)

Provides an Interface to Web-Services defined as standards by the Open Geospatial Consortium (OGC), including Web Feature Service (WFS) for vector data, Web Coverage Service (WCS), Catalogue Service (CSW) for ISO/OGC metadata, Web Processing Service (WPS) for data processes, and associated standards such as the common web-service specification (OWS) and OGC Filter Encoding. Partial support is provided for the Web Map Service (WMS). The purpose is to add support for additional OGC service standards such as Web Coverage Processing Service (WCPS), the Sensor Observation Service (SOS), or even new standard services emerging such OGC API or SensorThings.

neodistr - Neo-Normal Distribution

Provides functions for calculating the density, cumulative distribution, quantile, and random number of neo-normal distribution. It also interfaces with the 'brms' package, allowing the use of the neo-normal distribution as a custom family. This integration enables the application of various 'brms' formulas for neo-normal regression. The package implements the following distributions: Modified to be Stable as Normal from Burr (MSNBurr), Modified to be Stable as Normal from Burr-IIa (MSNBurr-IIa), Generalized of MSNBurr (GMSNBurr), and Jones-Faddy Skew-t. References: Choir, A. S. (2020).Unpublished Dissertation. Iriawan, N. (2000).Unpublished Dissertation. Jones, M. C. and Faddy,M. J. (2003).<doi:10.1111/1467-9868.00378>. Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) <doi:10.1201/9780429298547>.

multiplex - Algebraic Tools for the Analysis of Multiple Social Networks

Algebraic procedures for analyses of multiple social networks are delivered with this package as described in Ostoic (2020) <DOI:10.18637/jss.v092.i11>. 'multiplex' makes possible, among other things, to create and manipulate multiplex, multimode, and multilevel network data with different formats. Effective ways are available to treat multiple networks with routines that combine algebraic systems like the partially ordered semigroup with decomposition procedures or semiring structures with the relational bundles occurring in different types of multivariate networks. 'multiplex' provides also an algebraic approach for affiliation networks through Galois derivations between families of the pairs of subsets in the two domains of the network with visualization options.

multbxxc - Auxiliary Routines for Influx Software

Contains auxiliary routines for influx software. This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.

mmrm - Mixed Models for Repeated Measures

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.

landscapemetrics - Landscape Metrics for Categorical Map Patterns

Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' (<https://www.fragstats.org/>) and new ones from the current literature on landscape metrics. This package supports 'terra' SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics.

irtawsi - Items Response Theory Analysis with Steps and Interpretation

Dichotomous and polytomous data analysis and their scoring using the unidimensional Item Response Theory model (Chalmers (2012) <doi:10.18637/jss.v048.i06>) with user-friendly graphic User Interface. Suitable for beginners who are learning item response theory.

inferCSN - Inferring Cell-Specific Gene Regulatory Network

A method for inferring cell-specific gene regulatory network from single-cell sequencing data.

ggFishPlots - Visualise and Calculate Life History Parameters for Fisheries Science using 'ggplot2'

Contains functions to create life history parameter plots from raw data. The plots are created using 'ggplot2', and calculations done using the 'tidyverse' collection of packages. The package contains references to FishBase (Froese R., Pauly D., 2023) <https://www.fishbase.se/>.

gamlssx - Generalized Additive Extreme Value Models for Location, Scale and Shape

Fits generalized additive models for the location, scale and shape parameters of a generalized extreme value response distribution. The methodology is based on Rigby, R.A. and Stasinopoulos, D.M. (2005), <doi:10.1111/j.1467-9876.2005.00510.x> and implemented using functions from the 'gamlss' package <doi:10.32614/CRAN.package.gamlss>.

fresh - Create Custom 'Bootstrap' Themes to Use in 'Shiny'

Customize 'Bootstrap' and 'Bootswatch' themes, like colors, fonts, grid layout, to use in 'Shiny' applications, 'rmarkdown' documents and 'flexdashboard'.

frequentistSSDBinary - Screened Selection Design with Binary Endpoints

A study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the response rate) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. The proposed designs can “partially” control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal_2arm_binary() or optimal_3arm_binary() function) for each arm. All the design needed components (sample size, operating characteristics) are supported.

ewp - An Empirical Model for Underdispersed Count Data

Count regression models for underdispersed small counts (lambda < 20) based on the three-parameter exponentially weighted Poisson distribution of Ridout & Besbeas (2004) <DOI:10.1191/1471082X04st064oa>.

deming - Deming, Theil-Sen, Passing-Bablock and Total Least Squares Regression

Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.

ddml - Double/Debiased Machine Learning

Estimate common causal parameters using double/debiased machine learning as proposed by Chernozhukov et al. (2018) <doi:10.1111/ectj.12097>. 'ddml' simplifies estimation based on (short-)stacking as discussed in Ahrens et al. (2024) <doi:10.1177/1536867X241233641>, which leverages multiple base learners to increase robustness to the underlying data generating process.

crmPack - Object-Oriented Implementation of CRM Designs

Implements a wide range of model-based dose escalation designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. The focus is on Bayesian inference, making it very easy to setup a new design with its own JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules.

clusterSim - Searching for Optimal Clustering Procedure for a Data Set

Distance measures (GDM1, GDM2, Sokal-Michener, Bray-Curtis, for symbolic interval-valued data), cluster quality indices (Calinski-Harabasz, Baker-Hubert, Hubert-Levine, Silhouette, Krzanowski-Lai, Hartigan, Gap, Davies-Bouldin), data normalization formulas (metric data, interval-valued symbolic data), data generation (typical and non-typical data), HINoV method, replication analysis, linear ordering methods, spectral clustering, agreement indices between two partitions, plot functions (for categorical and symbolic interval-valued data). (MILLIGAN, G.W., COOPER, M.C. (1985) <doi:10.1007/BF02294245>, HUBERT, L., ARABIE, P. (1985) <doi:10.1007%2FBF01908075>, RAND, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, JAJUGA, K., WALESIAK, M. (2000) <doi:10.1007/978-3-642-57280-7_11>, MILLIGAN, G.W., COOPER, M.C. (1988) <doi:10.1007/BF01897163>, JAJUGA, K., WALESIAK, M., BAK, A. (2003) <doi:10.1007/978-3-642-55721-7_12>, DAVIES, D.L., BOULDIN, D.W. (1979) <doi:10.1109/TPAMI.1979.4766909>, CALINSKI, T., HARABASZ, J. (1974) <doi:10.1080/03610927408827101>, HUBERT, L. (1974) <doi:10.1080/01621459.1974.10480191>, TIBSHIRANI, R., WALTHER, G., HASTIE, T. (2001) <doi:10.1111/1467-9868.00293>, BRECKENRIDGE, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, WALESIAK, M., DUDEK, A. (2008) <doi:10.1007/978-3-540-78246-9_11>).

clinicalomicsdbR - Interface with the 'ClinicalOmicsDB' API, Allowing for Easy Data Downloading and Importing

Provides an interface to the 'ClinicalOmicsDB' API, allowing for easy data downloading and importing. 'ClinicalOmicsDB' is a database of clinical and 'omics' data from cancer patients. The database is accessible at <http://trials.linkedomics.org>.

aire.zmvm - Download Mexico City Pollution, Wind, and Temperature Data

Tools for downloading hourly averages, daily maximums and minimums from each of the pollution, wind, and temperature measuring stations or geographic zones in the Mexico City metro area. The package also includes the locations of each of the stations and zones. See <http://aire.cdmx.gob.mx/> for more information.

VsusP - Variable Selection using Shrinkage Priors

Bayesian variable selection using shrinkage priors to identify significant variables in high-dimensional datasets. The package includes methods for determining the number of significant variables through innovative clustering techniques of posterior distributions, specifically utilizing the 2-Means and Sequential 2-Means (S2M) approaches. The package aims to simplify the variable selection process with minimal tuning required in statistical analysis.

Rveg - Digitization of Phytosociological Relevés

Simple and fast tool for transforming phytosociological vegetation data into digital form for the following analysis. Danihelka, Chrtek, and Kaplan (2012, ISSN:00327786). Hennekens, and Schaminée (2001) <doi:10.2307/3237010>. Tichý (2002) <doi:10.1111/j.1654-1103.2002.tb02069.x>. Wickham, François, Henry, Müller (2022) <https://CRAN.R-project.org/package=dplyr>.

OneSampleMR - One Sample Mendelian Randomization and Instrumental Variable Analyses

Useful functions for one-sample (individual level data) Mendelian randomization and instrumental variable analyses. The package includes implementations of; the Sanderson and Windmeijer (2016) <doi:10.1016/j.jeconom.2015.06.004> conditional F-statistic, the multiplicative structural mean model Hernán and Robins (2006) <doi:10.1097/01.ede.0000222409.00878.37>, and two-stage predictor substitution and two-stage residual inclusion estimators explained by Terza et al. (2008) <doi:10.1016/j.jhealeco.2007.09.009>.

DGEAR - Differential Gene Expression Analysis with R

Analyses gene expression data derived from experiments to detect differentially expressed genes by employing the concept of majority voting with five different statistical models. It includes functions for differential expression analysis, significance testing, etc. It simplifies the process of uncovering meaningful patterns and trends within gene expression data, aiding researchers in downstream analysis. Boyer, R.S., Moore, J.S. (1991) <doi:10.1007/978-94-011-3488-0_5>.

DEET - Differential Expression Enrichment Tool

Abstract of Manuscript. Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the user’s gene lists, aiding in hypothesis generation and data-driven literature review. Sokolowski, Dustin J., et al. "Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression." Nucleic Acids Research Genomics and Bioinformatics (2023).

CFO - CFO-Type Designs in Phase I Clinical Trials

In phase I clinical trials, the primary objective is to ascertain the maximum tolerated dose (MTD) corresponding to a specified target toxicity rate. The 'CFO' package facilitates the implementation of dose-finding trials by utilizing calibration-free odds type (CFO-type) designs. Specifically, it encompasses the calibration-free odds (CFO) (Jin and Yin (2022) <doi:10.1177/09622802221079353>), two-dimensional CFO (2dCFO) (Wang et al. (2023) <doi:10.3389/fonc.2023.1294258>), time-to-event CFO (TITE-CFO) (Jin and Yin (2023) <doi:10.1002/pst.2304>), fractional CFO (fCFO), accumulative CFO (aCFO), TITE-aCFO, and f-aCFO designs (Fang and Yin (2024) <doi: 10.1002/sim.10127>). The ‘CFO' package accommodates diverse CFO-type designs, allowing users to tailor the approach based on factors such as dose information inclusion, handling of late-onset toxicity, and the nature of the target drug (single-drug or drug-combination). The functionalities embedded in 'CFO' package include the determination of the dose level for the next cohort, the selection of the MTD for a real trial, and the execution of single or multiple simulations to obtain operating characteristics. Moreover, these functions are equipped with early stopping and dose elimination rules to address safety considerations. Users have the flexibility to choose different distributions, thresholds, and cohort sizes among others for their specific needs. The output of the 'CFO' package can be summary statistics as well as various plots for better visualization.

BIGL - Biochemically Intuitive Generalized Loewe Model

Response surface methods for drug synergy analysis. Available methods include generalized and classical Loewe formulations as well as Highest Single Agent methodology. Response surfaces can be plotted in an interactive 3-D plot and formal statistical tests for presence of synergistic effects are available. Implemented methods and tests are described in the article "BIGL: Biochemically Intuitive Generalized Loewe null model for prediction of the expected combined effect compatible with partial agonism and antagonism" by Koen Van der Borght, Annelies Tourny, Rytis Bagdziunas, Olivier Thas, Maxim Nazarov, Heather Turner, Bie Verbist & Hugo Ceulemans (2017) <doi:10.1038/s41598-017-18068-5>.