Introduction | Background | Spectral density and the DFT | The CGLASSO and deCGLASSO estimators | Step 1 — Generate a multivariate white noise process | Step 2 — Bandwidth selection via GCV | Step 3 — Compute the DFT and smoothed periodogram | Step 4 — Fit CGLASSO and compute the deCGLASSO estimator | Step 5 — Estimate the asymptotic variance | Step 6 — Entry-wise tests and confidence regions | Step 7 — FDR-controlled support recovery | Summary of functions | References

Simple chord diagram | Asymmetric flows | Faceting | Individual layers for more control

Preference-approvals | Distance between preference-approvals | The DIVA consensus | French Presidential Election (2002) | Sensitivity to the weighting parameter | Formula 1 World Championship (1950) | References

Checking a file | Checking identifiers or a data frame | PubMed Central articles | Choosing sources | Offline mode | Identifier normalization

The labeled corpus | Result 1: the assertion gate never false-accuses | Result 2: 0.90 is the empirical sweet spot for surfacing | Conclusion | Reproducing

Gate a manuscript render | Fail CI on retracted citations | Systematic reviews | Monitor a bibliography over time | Multiple sources | Interpret and export | Offline and at scale | Reproducible pipelines with targets

Overview | Setup | Inspect data | Define population targets | Rake | Inspect results | Design effect | Per-variable diagnostics | Tidy output | Augmenting the data | Advanced options | Weight bounding | Variable selection | Checking discrepancies directly

Goal | Prepare the data | Fit a classifier | Fit calibrators | Compare calibration metrics | Plot the calibrated probabilities

Why calibration matters | A three-split workflow | Fit a calibrator | Compare methods | Reliability diagram | Cross-validated calibration | Optional reference validation | Current scope

The main decision | Match the input scale | Compare methods on held-out data | Practical guidance

From two classes to several | Simulating an overconfident classifier | Measuring multiclass calibration | Temperature scaling on logits | Dirichlet calibration on probabilities | One-vs-rest calibration | Comparing the calibrators | Reliability diagram | Out-of-fold calibration | Scope

Purpose | Optional checks | Why not compare every method | Running the optional tests

Introduction | Exploratory Spatial Data Analysis (ESDA) & Spatial autocorrelation | Moran's I | Exploring the data | Calculate the global Moran's I | Case 1: function moransI | Case 2: functions w.matrix and moransI.w | Local Moran's I | References

Introduction | Analysis | References

Introduction | Stochastic Model | Option Market Data | Pricing Options | Including Default Intensities | Including Term Structures | Fitting Term Structures of Volatility | Fitting Default Intensity | Daycount Conventions | References

1. Data Requirements | 1.1 Representing more complex prerequisite relationships | 1.2 Converting the notation to all ANDs | 1.3 Representing electives | 1.3.1 Electives as generic, standalone courses | 1.3.2 Electives as generic, but connected courses | 1.3.3 Customizing pathways with frequently taken electives | 2. Conducting simple curricular analytics | 2.1 Blocking Factor | 2.2 Delay Factor | 2.3 Cruciality | 2.4 Structural Complexity | 2.4.1 Structural complexity and the quarter system | 3. Digging deeper into a plan of study | 3.1 Extracting course sequences | 3.2 Unbundling layers of requirements | 3.3 Other course-level metrics | 3.3.1 Deferment factor | 3.3.2 Bottleneck courses | 3.3.3 Reachability Factor | 3.4.1 Curriculum rigidity | 4. Transfer-Sensitive Metrics | 4.1 Transfer Delay Factor | 4.2 Explained Complexity | 4.3 Inflexibility Factor

Introduction | Scope | Data preparation | loading catchment data | Object model preparation | Calibration and evaluation of the new CemaNeige module | Comparison with the performance of the initial CemaNeige version | References

Overview | Who is this vignette for? | Data requirements | The data4health workflow | 1. Loading data with d4h_load() | 2. Cleaning data with d4h_clean() | 3. Filtering data with d4h_filter() | 4. Aggregating data with d4h_aggregate() | 5. Saving results with d4h_save() | Putting it all together

What are map projections? | Geodesic vs Spherical model | The planisphere package | Project a baseamap | Custom projections | One last trick

Introduction | Setup | Extracting JSON data and formatting to LGF model | Extracting the cytoband locations, and the frequency data | Turning CytoData into an S4 Object | Generating Graphs | Plotting Cytoband Data Along the Genome | Plotting Cytoband-Level Data Along One Chromosome | Idiograms | One Data Column | More Data Columns | Gallery | Appendix

Predictive margins (PM) | Other software | Load R packages | Linear model | Generate simulated data | Analysis with Stata | Regression analysis | Predictive margins | Define the desired margins | Calculate the predictive margins | Confidence intervals | Contrasts using the survey package | Multinomial logistic regression model with svyVGAM package | Symmetric confidence intervals | Asymmetric confidence intervals | More formal presentation of the variance estimation

Introduction | Software | Data | Methods | Results | Conclusions | Licensing

1. Introduction | 2. Quick start with synthetic data | 2.1 Tuning mtry globally | 2.2 Optimal bandwidth | 2.3 Fitting the GRF | 2.4 Inspecting the fit | 2.5 Predicting at new locations | 3. Real-world example: Greek municipal income | 4. Practical tips | References

Introduction | Installation | Example: classo | Example: cglasso | Example: Sparse Spectral Precision Matrix Estimation | Step 1 — Generate a multivariate Gaussian white noise process: | Step 2 — Compute the DFT and smoothed periodogram: | Step 3 — Estimate sparse spectral precision matrix via \texttt{cglasso}: | Step 4 — Visualize the estimated spectral precision matrix: | Step 5 — Compare to the true precision matrix:

Introduction | Example Dataset | Running the Clustering Algorithm | Cluster Assignment | Cluster Quality | Prototype Interpretation | Xie-Beni Index Visualization | Conclusion

Overview | Load the package | Built-in synthetic kiosk demo | 1. Import Gazepoint biometric exports | 2. Inspect real-data readiness | 3. Run the full workflow | 4. EDA, GSR, and SCR examples | 5. Pulse, IBI, HRV, and respiration examples | 6. TTL alignment and model-ready data | 7. Reporting bundle | 8. Feature inventory | 9. Interpretation guardrails | 10. Private real-data smoke tests

All combinations | Preparing the data | Ground-truth labels | Defining the feature methods | Defining the clustering methods | One call to rule them all

Overview | The data | Step 1: reshape into long format | Step 2: extract rolling features | Step 3: normalize | Step 4: choose epsilon (visual heuristic) | Step 5: run DBSCAN | Step 6: evaluate

The pairwise coefficient | Example 1 | Example 2 | Example 3 | Deterministic reference grids | The unified interface: covercorr() | Joint dependence among many variables | A note on the K-variable fixed-grid case | Visualising the joint density on real data

A license can be present yet not open for reuse | Controlled-access and sensitive data is not a FAIR failure | Identifier hygiene | FAIR-TLC: Traceable, Licensed, Connected | The canonical FAIR principles

Assessing an object | Interpreting beyond the score | Exporting results | Interactive use

1. Background: FAIR, the FAIRsFAIR metrics, and F-UJI | 2. The assessment pipeline | Identifier handling | Harvesting and content negotiation | The metric model | 3. What each FAIR category measures (v0.8) | 4. Software FAIR (FRSM) | 5. Fidelity to F-UJI | 6. Beyond F-UJI | 7. Limitations | References

1. The scorecard plot | 2. Score tables | 3. How to read the numbers | 4. The context rfair adds beyond the score | 5. Exporting the illustration | Summary

What this indicator captures | The 2023 year gate | In the all-indicators output | Across a corpus | Notes on precision

Overview | How detection works | Article parsing | Rule-based detection | Languages | Usage: PMC XML | All indicators at once | Individual indicators | Data and code sharing | AI-use disclosure | Usage: TXT files | Processing many articles | Summarizing a corpus | Downloading PMC XML | Validation | Naming convention and dependencies

What the indicators mean | Known limitations | Output schema | Linking to FAIR assessment

From one article to many | Prevalence of each indicator | Correcting for detector error | How many practices per article | Subgroups | Plots | Putting it together

Overview | Installation | Load the package | Example data | Data structure | Fitting a model | Prediction | Model output | Practical interpretation | Notes for CRAN | References

new to networks | overview books | academic articles | networks & conflict | model-specific

the netify object: a base r object with attributes | extracting parts | tidy interop | predicates and descriptors | object-level validation | open-cohort panels with actor_pds | the actor_pds roster, step by step | density and per-period actor sets | na versus zero in weighted networks | writing a custom graph-level statistic | writing a custom actor-level statistic | reading the dyad_data nested list | writing a custom exporter (to_* function) | performance characteristics | see also

1. cross-sectional pipeline | before you fit: three sanity checks | dyadic edge covariates: the _e suffix | 2. longitudinal pipeline (per-time fits) | 3. multilayer pipeline (per-layer fits) | 4. round-tripping simulated networks back to netify | tl;dr | references

1. build | 2. explore | 3. test (basic inferential) | 4. bridge | tl;dr | a note for non-time use cases

Overview | 0. Load the package and sample data | 1. Key c4h_downscale arguments | 2. Downscaling methods (gridded data) | Method 1: Interpolation | Method 2: Interpolation plus bias correction | Method 3: Interpolation plus linear regression | Method 4: Analogs | 3. Downscaling to point locations | 4. Calibration | 5. Considerations for downscaling common variables | Interpolation-based methods | Specific recommendations for precipitation downscaling | General recommendations | Parameter selection

Overview | 0. Load the package | 1. c4h_get arguments | 2. c4h_get_help | 3. Specifying arguments in c4h_get() | Specifying temporal range | Specifying spatial extent | Specifying variables | Specifying the file path and name | 4. Obtain your Personal Access Token (PAT) | 5. Example downloads | 5.1. ERA5-Land data | 5.2. Hindcast data | 5.3. Forecast data

Overview | Glossary

Overview | Installation | Data requirements | clim4health structure | 1. Obtain input data | 2. Transform and process the data | 3. Prepare outputs | clim4health workflow | 1. Loading, inspecting, and preprocessing data | Dataset description | Data preprocessing | Plot the raw data | 2. Processing the data | Spatial aggregation | Downscaling | Verification and postprocessing | Masking data | Aggregating data temporally | Collapsing data across a dimension | 3. Outputting the data | Convert data from an s2dv_cube to a different data type | Save the data

Overview | 0. Load the package and sample data | 1. Key c4h_verify arguments | 2. Verification metrics | "BSS" - Brier Skill Score | "RPSS" - Ranked Probability Skill Score | "CRPSS" - Continuous Ranked Probability Skill Score | "AbsBiasSS" - Absolute Bias Skill Score | "MSE" - Mean Squared Error | "MSSS" - Mean Squared Error Skill Score | "RMSE" - Root Mean Squared Error | "RMSSS" - Root Mean Square Skill Score | "ROCSS" - Relative Operating Characteristic Skill Score | Statistical Significance | 3. Example skill assessment | Choosing arguments in c4h_verify | ref | brier_thresholds | prob_thresholds | sig_method and sig_method.type | alpha | ncores | N.eff | indices_for_clim | cross.val and clim.cross.val | weights_exp and weights_ref | comp_dim and limits | conf | na.rm | sign and pval | rocss_cat

Overview | 0. Load the package | 1. Forecasts, hindcasts, and reanalyses | 2. Climate data in clim4health | Loading multi-dimensional data with c4h_load | Loading example data | The forecast data is stored with dimensions | The hindcast data is stored with dimensions | The reanalysis data is stored with dimensions | Exploring the loaded data | Data dimensions in clim4health | Exploring the s2dv_cube structure | 3. Constructing an s2dv_cube from scratch | 1. Load the data | 2. Reshape the data | 3. Add dimensions, coordinates, and attributes | 4. Create the s2dv_cube object

Construct simulated maps | Simulate populations and counts | Prepare and fit the model | Predict on the fine grid

Introduction | Example Data | Generate Strata | Evaluate Stratification | Summary | Visualization | Conclusion

Introduction | The data | Fitting the model | Local influence diagnostics | Deletion analysis | Model adequacy | Density shapes | Reproducing the full study | References

Introduction | Package installation | Application to the NCDS Example: Does Education Attainment Increase Wages? | 1. Data Overview | 2. Balancing covariates by weighting | 3. vbm package---ATT and standard error estimation | 4. vbm package--sensitivity analysis | Variance-based Sensitivity Model | Marginal Sensitivity Model | Comparison among VBM and VBM with More Conservative Bounds | References

Source | Visualization | Triad census | Global triad closure | Local triad closure | Wedge-dependent triad closure

Step 1: assess baseline equivalence — before looking at the outcome | Step 2: read the categories | Step 3: visualise | Step 4: a report-ready table | What this does and doesn't tell you

The problem | A worked example | The building blocks | Visualise and format | Scope

Overview | Installation | Requirements | Input files | Running the full pipeline | Step 1 — prepare_cpg_snp_input() | Step 2 — extract_bam_regions() | Step 3 — ASM() | Output files

Presentation of dataset: CRC Example | Preview of the data | 1) The abundance table | 2) The metadata | 3) The taxonomy | 4) The graph | Aim of this use case | Test if the default parameters of NeighborFinder are suitable for your species of interest & dataset | Apply NeighborFinder & look for Escherichia coli neighbors in CRC patients | Visualize the corresponding network | Apply NeighborFinder with covariate option | Look at the intersection of neighbors found in the 3 subgroups

Why use NeighborFinder? | How to use it? | Input dataframe format | What is behind apply_NeighborFinder() ? | 1) Pre-processing: Counts & Normalization | a) Prevalence filter & shotgun pre-treatment | b) Normalization | 2) Regularized linear regressions | a) Simple case: no covariates | b) Handling covariates | 3) Post-processing | a) Filtering the results | b) Increasing robusteness | How to calibrate the parameters values ?

Introduction | 1. Setup & Data Preparation | 2. The Three-Step Analysis Workflow | Step 1: Determine the Optimal Number of Cuts | Choosing an Information Criterion | Step 2: Pinpointing the Exact Cut-point Values | 🔍 Automated Diagnostic Check: The Two-Tier Schoenfeld Diagnostic | Step 3: Validate Threshold Stability | 🔍 Automated Performance Grading: The Four-Tier Stability Assessment | 3. Advanced Visualisation & Reporting | 3.1 Group Composition Table | 3.2 Adjusted Hazard Ratio Forest Plot (type = "forest") | 3.3 Time-Dependent Diagnostics (type = "diagnostic") | 3.4 2D Cut-point Stability Surface (plot_validation()) | 3.5 Adjusted Kaplan-Meier Survival Curves (type = "outcome") | 3.6 Exporting Your Stratified Data | 4. Conclusion | 5. Session Information

Overview | The Two Strategies | "all" - Independent sampling | "restrictionEspace" - Linked sampling | Visualizing the Search Space | "all" - Independent space | "restrictionEspace" - Restricted space | Simulated Data | Running the optimization | Strategy "all" - Independent sampling | Strategy "restrictionEspace" - linked sampling | Comparing the Two Strategies | Session Info

Overview | Data Loading | BlueCode Reference Signature Matrix | CellMixtures Bulk Dataset | Data Inspection | Gene Overlap | Expression Distribution | Deconvolution with dicepro() | Results | Optimal Hyperparameters | Hyperparameter Optimization Report | Pareto Frontier | Estimated Cell-Type Proportions | Top Contributing Cell Types | Per-Sample Composition | Compartment-Level Summary | Session Info

Overview | Strategy 1 -- Fully Synthetic Simulation | Data Generation | Noise Model Sanity Check | Deconvolution | Results | Optimal Hyperparameters | Hyperparameter Optimisation Report | Pareto Frontier | Recovered vs True Proportions | Per-Cell-Type Correlation | Quantitative Performance Metrics | Strategy 2 -- BlueCode-Based Simulation | Compartment Structure | Proportion Distribution by Compartment | Comparing Both Strategies | Session Info

Overview | A simple binary forecasting example | Compare the forecasts | Interpreting the confidence sequence | Interpreting the e-process | Using lower-level functions directly | Choosing a scoring rule | Practical guidance

What's in episodes? | How often does Rick Steves visit each country? | Are highly-rated episodes a particular kind of place? | Mapping the show | Production cadence

Where does the show go? | A static atlas | Country revisits

Overview | Installation | The Q matrix and components | Basic usage — LLTM | Basic usage — GMLTM-D | Customising prior distributions | Available priors per model | Model comparison with LOO-CV | Customizing prior distributions | How priors work in GMLTM | LLTM priors | MLTM priors | GMLTM-D priors | Prior sensitivity analysis | GMLTM default priors | References

Table of contents | Introduction | General remarks | Some general remarks on the competence extension to KST | Core data structure | File format | Functions in CbKST | Input/Output | Example | Mapping states between skills and performances | Mapping structures between skills and performances | Example data | References

Example 1: Palmer Penguins Classification | Train SVMODT Model | Examine Tree Structure | Make Predictions | Visualize Decision Boundaries | Trace Prediction Path | Example 2: Wisconsin Breast Cancer Diagnosis | About the Data | Load and Prepare Data | Train with Class Weights | Evaluate Performance | Advanced Features | Feature Selection with Penalties | Dynamic Feature Selection | Custom Class Weights | Example 3: Multiclass Classification — Wine Dataset | Train Multiclass SVMODT | Visualize Decision Surface | Tree Structure | Performance | Trace a Prediction Path | Model Comparison

Inline prose | Tables | Assets | Volcano plot | Experimental auto-linking

Overview | Using individual estimation functions | When to use individual functions | Using the all-in-one wrapper | Custom column names | Diagnostic plots | What if I have no pre-intervention data? | Handling non-standard situations | Date-indexed time column | Missing values | Very short pre-periods | Typical parameter ranges for monthly hospital data | References

Motivating example | Step 1 — Load pre-intervention data | Step 2 — Estimate nuisance parameters | Step 3 — Specify the clinical hypothesis | Step 4 — Calculate power for a single design | Step 5 — Optimise with a design sweep | Step 6 — Plot a simulated ITS realisation | Step 7 — Sensitivity analysis | Step 8 — Multi-site analysis | Summary table | One-call shortcut | References

Overview | Create the R Markdown template | Build your Shiny application

Overview | Data | 1. Point the package at the bioacoustics flavor | 2. Deployments (from the deployment notebook) | 3. Media (derived from the observation notebook's file names) | 4. Observations (from the observation notebook) | 5. Metadata, relations, write, validate | 6. Inspecting the acoustic requirements

概要 | データ | 1. bioacoustics フレーバーを指定 | 2. deployments（設置野帳から） | 3. media（観察野帳のファイル名から生成） | 4. observations（観察野帳から） | 5. メタデータ・リレーション・書き出し・検証 | 6. 音響スキーマの要件確認

概要 | データ | 1. バージョンの選択とスキーマの確認（任意） | スキーマ内の外部（URL）参照 | 2. 3 つの中核テーブルの作成 | デプロイメントの作成 | メディアの作成 | observationの作成 | 3. データパッケージの組み立て | R6 オブジェクトの作成（バージョン指定） | テーブルの登録（スキーマ検証付き） | テーブル間リレーションの検査 | 4. メタデータ | どのメタデータが必須かを profile で確認する | 必須メタデータ | 貢献者（Contributors） | プロジェクト（Project） | 空間・時間（Spatial and temporal） | 分類（Taxonomic） | 作成日時（Created） | 任意メタデータ | ライセンス（Licenses） | 関連識別子（Related identifiers） | プロパティ・ソース・参考文献 | カスタムリソース | 5. データパッケージの出力 | 6. Frictionless による検証 | 適合の事前チェック（Python を呼ぶ前に） | Frictionless の実行 | 7. 任意のスプレッドシートを直接変換する | 8. 別のスキーマフレーバー（例: bioacoustics） | カメラトラップの列を音響フレーバーへ対応づける

Overview | Data | 1. Choose a version and inspect its schema (optional) | External (URL) references in a schema | 2. Build the three core tables | Create deployments | Create media | Create observations | 3. Assemble the data package | Create the R6 object (with a version) | Import the tables (now schema-validated) | Check relations between tables | 4. Metadata | Check which metadata the profile requires | Required metadata | Contributors | Project | Spatial and temporal | Taxonomic | Created | Optional metadata | Licenses | Related identifiers | Properties, sources and references | Custom resources | 5. Output the data package | 6. Validate the written package with Frictionless | Conformance pre-checks (before calling Python) | Run Frictionless | 7. Converting an arbitrary spreadsheet directly | 8. Other schema flavors (e.g. bioacoustics) | Mapping camera-trap columns to the acoustic flavor

Data | Create deployment | Create media | Create observation | Create datapackages

Data | Create deployment | Create media | Create observation | Create datapackages

Specify A Model | Compare Candidate Models | Full Automated CFA

Item Screening | Reverse Scoring And Scale Scores | Exploratory Factor Analysis

Model | Sequential Invariance Testing

1 Introduction | 2 Object Families | 2.1 Representations of Plots and Stands | 2.2 Species Coding System | 2.3 Yield Tables | 3 Low Level Methods | 4 High Level Methods | 5 Expert Methods | 6 Technical Specifics | 7 Acknowledgments | References

1. Introduction | 2. Where to find things and what they are good for | 2.1 The species master table | 2.2 Specific species codings | 2.2.1 General setup | 2.2.2 Species groups and hierarchical codings | 2.2.3 Non-tree codes | 2.2.4 Implemented codings | 2.2.5 A field-ready coding table | 3. Usage | 3.1 Creating a species code vector | 3.2 Display options | 3.3 Species code conversions | 3.4 Practical examples | 4. Information for developers | 4.1 The data behind the codings | 4.2 How to update the master table or an existing coding | 4.3 Adding a brand-new coding | 4.3.1 Copy the R-source file of an existing coding and adapt it | 4.3.2 Add a species coding cast function to each other coding | 4.3.3 Document the new coding | 4.3.4 Add your new coding to the automated tests for species codings | 4.4 Never touch the source file fe_species_helper_functions.R | References

Endogenous reciprocity | Time-varying global covariates | Composing endogenous and global | Caveat

US state distance matrix | Defining a non-linear effect | Simulating events with exogenous covariates | Recovering the effect with a GAM | Plotting estimated vs true effect

1. Load a bundled REM dataset | 2. Build candidate specifications | 3. Compare by AIC | Multiple controls per case | 4. Inspect coefficients of a chosen specification | 5. Cross-implementation guarantee | References

Simulating actor covariates | Simulating event sequences

Why model invasions as relational events? | A synthetic invasion process | Recovering the drivers | Where to go from here

Introduction | Running blockRAR | Required input | Frequentist Approach | Bayesian Approach | Obtaining Power and Sample Size | Session Info

Overview | Creating memory-mapped objects | Creating objects associated to new files | Creating objects in memory | Conversion to an R object | Mapping pre-existing files | Descriptor Files | Basic usage | Compatibility with bigmemory | Restoring Broken Pointers | Copying objects | Data manipulation | Changing dimensions | Accessing values | Assigning values | Arithmetic Operations | There's no type promotion in houba | In-Place Arithmetic Operations | Row and columns operations | Sums and means | Applying Functions | Contributing to houba

Overview | A single estimate | Vectorisation | Working with units | Cystatin C and combined equations | Staging the result | Body surface area adjustment | Where to go next

ClusterRandSSAdj | Example Use

What is Statistical Depth? | The Five Depth Functions | Mahalanobis Depth | Tukey (Halfspace) Depth | Liu Simplicial Depth | Projection Depth | Spatial Depth | The depth Object | Depth-Based Median | Depth-Based Ranks | Central Region | Outlier Detection | Visualising Outliers | The DD-Plot | High-Dimensional Performance | References

1. One disaggregation engine, not two | Where the engine is used here | 2. Leave-Cluster-Out | Why a cluster, not a single sector | The cluster map is pluggable | A documented fallback until the MIP arrives | Reading the statistical layer honestly

Introduction | Basic usage | Coupling significance (corrected null) | Convergence test | Methodological notes and design decisions | Reproducibility

1. The problem: one line, two stories | 2. Why quantiles, and why a mixture | 3. An illustrative dataset | 4. Fitting your first model | 5. Visualizing the estimates | 5.1 The two regimes | 5.2 A coefficient comparison | 6. Who is in which group? | 7. Diagnostics: can you trust it? | 7.1 The component error densities | 7.2 Two engines agree | 7.3 A caveat worth knowing | 8. Inference done right | 9. Beyond the median: the whole distribution | 10. How many groups? | 11. Reporting and reproducibility | Citation | References

A two-regime example | A first picture | Where to next | References

Introduction | Installation | Getting Started | Differential Exprssion, Variability and Skewness Analyses | Examples | DE analysis | DV analysis | DS analysis | Simultaneous DE, DV and DS analysis | Notes on CLR-transfromation in SIEVEseq

Dichotomous outcomes | Continuous outcomes | From raw data | From summary statistics | Reverse CFI: distance from significance | Tidy interface | References

The setup | Naive pooling inherits the bias | $\lambda$ moves efficiency, not bias | Choosing $\lambda$ | Takeaways | Reproducing

Two objectives, two estimators | Example data | Ability-risk tuning: Minimizing $\mathbb{E}[g'\Sigma_\gamma g]$ | Cross-fit $\lambda$ tuning (recommended workflow) | Frozen expected-count estimator (fast approximation) | Minimizing $\text{Tr}\big[\Sigma_\gamma\big]$ (diagnostic only) | Choosing a procedure

Background | Background (frozen expected-count estimator) | Linking implementations | Simulation | Fitting human and LLM models | Applying the three methods | Parameter alignment after linking | TCC alignment | Gradient asymmetry: what linking fixes and what it does not | Lambda sweep: how $\lambda$ interacts with linking quality | The role of power tuning | Validation: what does $\lambda^*$ measure? | Test A — Perfect paired surrogate ($F = Y$) | Test B — Partially overlapping predictions | Test C — Stochastic LLM predictions (practical baseline) | Summary: PPI++ score vs. ability risk | Summary of findings | Recommendation | The marginal-MML fix

Simulate a 1PL test | Step 1: Fit the 1PL baseline | Step 2: Fit mixed-subjects MML (1PL) | Step 3: Correct covariance — $(J+1) \times (J+1)$ sandwich | Step 4: Ability-score risk and lambda tuning | Step 5: Verify — F = Y gives lambda > 0 | Compare 1PL and 2PL | Ability-score risk: 1PL vs 2PL parameterization

Simulate example data | Step 1: Fit the human baseline | Step 2: Fit the MML mixed-subjects model | Step 3: Select $\lambda$ by ability-score risk | Step 3b (recommended workflow): cross-fit $\lambda$ tuning | Step 4: Inspect the covariance | Compare calibrations | When the LLM is uninformative | Validation

Why per-item lambda? | Simulate a heterogeneous test | Step 1: Fit 2PL baseline and get global scalar lambda | Step 2: PPI++ score per item (fast diagnostic) | Step 3: Per-item ability-risk tuning | Step 4: Compare scalar vs. per-item parameter recovery | Important note on initialization | Approximation caveat

Design | Does $\lambda$-selection track predictor quality? | Do standard errors achieve appropriate coverage? | Does the method improve downstream scoring? | What is the role of cross-fitting? | Is coverage valid at the tuned $\lambda$? | Summary | Reproducing these results

Why this vignette exists | Key Intuition | The three response matrices | 1. Observed human responses: $O$ | 2. Paired LLM-predicted human responses: $P$ | 3. Additional LLM-generated responses: $G$ | The mixed-subjects IRT objective | What lambda is learning | $$L_O^ | Ability-risk tuning | The approximate target is$$\widehat R(\lambda) | Why row alignment matters | Case A: perfect paired prediction | $$\lambda_ | \frac | Case B: row-shuffled perfect predictions | Case C: same DGP, fresh Bernoulli draw | $$\operatorname{Cov}(O_{ij},P_{ij}) | $$\operatorname{Var}(P_{ij}) | What kind of LLM data produces higher lambda? | One approach to row alignment: leave-one-item-out prediction | Another approach: covariate-based prediction | Something that probably won't work: item-text-only generation | How to generate $G$ | Summary | Technical Explanation | Overview: four objects, one objective | 1. The estimator and its estimating equation | 2. The sandwich covariance of $\hat\gamma$ | 3. Ability scoring and the implicit gradient | 4. Delta-method propagation and the risk | 5. Why this differs from the PPI++ trace objective

Introduction | Example: Dynamic Density Raster | Key Features | Dynamic Binning | Pan/Zoom Interaction | Performance | Color Encoding | Use Cases | Try It Yourself | Advanced: Customizing Pixel Size

Introduction | Example: Gaia Star Catalog | Key Features | Natural Earth Projection | Hertzsprung-Russell Diagram | Multiple Crossfilter Brushes | Performance | Try It Yourself

Introduction | Installation | Example: Voronoi Diagram with YAML | Key Features | Next Steps

Introduction | Example: NYC Taxi Rides with Crossfilter | Key Features | Crossfilter Selection | Remote Data Loading | Spatial Transformations | Performance | Try It Yourself

Introduction | Example: Olympic Athletes Dashboard | Key Features | Multiple Selection Types | Coordinated Views | Regression Analysis | Interactive Workflow

Introduction | Example: Protein Design Explorer | Key Features | Crossfilter Menus and Brushing | Dense Metric View | Marginal Distributions | Linked Table | Try It Yourself

Introduction | Example: Voronoi Diagram with ESM | Advantages of ESM Format | When to Use ESM | Comparison of Formats

Introduction | Example: Voronoi Diagram with JSON | Working with JSON | When to Use JSON

Setup | Load the example sources | Step 1: Compare the source tables | Step 2: Standardise the sources | Step 3: Reconcile species names with the tree | Step 4: Add manual corrections | Step 5: Collapse to one row per species | Step 6: Align the database and the tree | Final database ready for model fitting | References

Setup | The recipe | What actually works (status table) | Branch lengths and time-calibration | Where are the VertLife trees? | Note on tip labels before comparing | What to do with the result | When the backends return the same species set and similar topology (Jaccard ≈ 1, RF small) | When tip sets differ (Jaccard < 1) | When topologies disagree (RF large) | When branch-length correlation is low | Caching the comparisons | What pr_tree_compare() doesn't do (yet) | See also

Setup | Part I: Core Workflow | Step 1: Load data and tree | Step 2: Reconcile data against the tree | Step 3: Produce aligned objects | Step 4: Run a comparative analysis | Phylogenetic generalised least squares (PGLS) | Phylogenetic generalised linear mixed model (PGLMM) | Part II: Advanced Topics | Reconciling two datasets | Multi-row species | Asymmetric datasets | Using a taxonomy crosswalk | Reconciling against multiple trees | Fuzzy matching for typos | Tree augmentation for missing species | Exporting to files | HTML reports | Key points | Data sources | References

What we'll build | Step 1 — Retrieve a posterior of trees | Step 2 — Reconcile the data to the posterior | Step 3 — Format citations (do this before you submit) | Step 4 — Hand the posterior to pigauto | Choosing a backend | What's not in this pipeline | See also

The problem | Installation | Example 1: Reconcile a dataset against a tree | Inspect the result | Apply manual overrides | Produce aligned objects | Example 2: Reconcile two datasets | Understanding match types | Example 3: Using a taxonomic authority | Example 4: Pre-built overrides | Example 5: Multiple datasets against one tree | Key design principles | Typical workflow | References

A worked example: thermal-tolerance plasticity across animal classes | Setup | Step 1-3: topology -> bifurcating -> Grafen branches, in one call | The manual pipeline (without prepR4pcm) | The same in one call (with prepR4pcm) | Confirm the two pipelines produce the same tree | Step 4: phylogenetic correlation matrix | Step 5: fit the meta-analysis with metafor | Manual tree grafting when Open Tree of Life doesn't have a species | Why Grafen (and not DateLife / a time-calibrated tree)? | What this vignette deliberately doesn't cover | See also

Basic input format | Sort and rename individuals | Reduce pedigree size | Inbreeding and Purging | Wright's inbreeding coefficient | Partial inbreeding coefficient | Ancestral inbreeding coefficient | Purged inbreeding coefficient | Opportunity of purging | Population parameters | Effective population size | Number of equivalents to complete generations | Number of founders and ancestors | Hardy-Weinberg deviation | Fitness functions | Other functions | Maternal effects | igraph input | References

Overview | Introduction | Installation | Open the Vignette in R | CAFT: Compositional Accelerated Failure Time Model for Microbiome Data Differential Abundance | Example data sets illustration | Binary outcome phenotypes example | Multicategory outcome phenotypes example | Separate estimation and testing workflow | Parallel computation | Bootstrap implementation of CAFT | How to cite CAFT | References

Configuração | Consultar um processo | Consultar vários processos | Documentos e publicações | Listagens | Tratamento de erros | Operações de escrita

Visão geral do fluxo | Dados a enviar no pedido | 1. Sigla do sistema (SiglaSistema) | 2. IPs de saída (liberação de firewall) | 3. Tipos de operação (métodos) | A IdentificacaoServico (chave de acesso) | Endpoints (treinamento e produção) | Configurando o rsei com o que foi liberado | Boas práticas de privacidade e segurança (LGPD)

Creating a concept database for MiADE and MedCAT | SNOMED CT concept decomposition | More information

Overview | Workflow Overview | Adjusted Risk Model | Stage 1 — Finite Mixture Model (FMM) | Stage 2 — Confounder-Adjusted Risk Model | Function Usage | Percentile-Based Cutoff Derivation | Method Overview | Low-Risk Group and Rule-Out Cutoff | High-Risk Group and Rule-In Cutoff | Fallback Behavior for Small Datasets | Classification Rules | ROC-Based Cutoff Derivation | ROC Curve Construction | Selection of Rule-Out and Rule-In Thresholds | Rule-Out Threshold (T_L) | Rule-In Threshold (T_H) | Mapping Risk Thresholds Back to Biomarker Values | Performance Evaluation | Performance Metrics | Interpretation | Comparison of Cutoff Methods | Visualization of Two-Cutoff Classification | Classification Regions | Plot Structure | Confusion Matrix Visualization | Multi-Panel Visualization | Single-Method Visualization | Comparison of Multiple Cutoff Methods | Decision Curve Analysis | Net Benefit | Strategies Compared | Decision Curve Analysis Example

1. The problem: a treatment people choose | Why covariates enter | 2. The four core estimators in one picture | Why the paper (and the package default) prefers IPWRA | 3. A worked example | The naive answers fail | The drlate answer | Seeing double robustness work | 4. Checking the design: diagnostics | Overlap | Covariate balance | Weight distributions | A formal balance test | Profiling the compliers | 5. Choosing models and options | Outcome and treatment families | Instrument propensity score flavors | LATT: the effect for treated compliers | 6. Abadie's kappa: the weighting-estimator menu | 7. When the instrument is weak: Fieller confidence sets | 8. Bootstrap inference | 9. How much does the estimator choice matter? | 10. Do you even need the instrument? The DR Hausman test | 11. Coming from Stata | References

Overview | Joint inference | Example | Other estimators | Abadie-kappa weighting estimators | LATT, other model families, and IPT | Clustered standard errors and weights | Diagnostics | Inference beyond the default sandwich | The DR Hausman test of unconfoundedness | Comparing estimators | Replicating the Stata examples | Citation | References

Basic Usage | Querying Available Methods | Nominal Association Measures | Cramér's V | Phi Coefficient | Contingency Coefficient | Tschuprow's T | Ordinal Association Measures | Goodman-Kruskal Gamma | Somers' D | Pairwise Matrix for Multiple Variables | Handling Missing Values | Choosing the Right Method

Installation and Setup | Basic Usage with Vectors | Classical Methods | Matrix and Data Frame Input | Tidy Output using as.data.frame | Controlling P-value Computation | Robust Correlations | Biweight Midcorrelation | Percentage Bend Correlation | Winsorized Correlation | Ordinal Correlations | Polychoric Correlation | Tetrachoric Correlation | Partial and Semi-Partial Correlations | Partial Correlation | Semi-Partial Correlation | Nonparametric Dependence Measures | Ball Correlation | Bergsma-Dassios Tau* | Querying Available Methods | Categorical Association Measures

Overview | Installation | Graph construction | Generating a causal coefficient vector | Simulating data | Fitting the estimators | IV-LASSO | IVGL and IVGL-S | Performance evaluation | Graph misspecification | Simulation study | Application context

Scaffold | How it is wired | The frontend | Helpers | Why not a fireproof guard? | Production checklist

Build an image | Choosing a flavor | Publishing to a registry | Runtime configuration (environment variables) | Sharing assets across apps (statics:) | Behind a reverse proxy / load balancer | ShinyProxy | Ruscker | Checklist

What aurora is | Scaffold, run, add a route | The app contract (convention, not a manifest) | Writing a route | Wiring UI to the API | Theming, data, auth, telemetry | Deploy

From Shiny: the mental-model shift | From plumber v1: it is not a find-and-replace | 1. Query params no longer bind to named handler args | 2. req/res become reqres request/response | 3. No @filter / preempt / forward() | 4. pr_*() → api_*() (not 1:1) | 5. No mount-prefixing — the path lives in the annotation | Testing a ported handler

What you get for free | Turning it on in aurora | Actually exporting data | Custom spans and logs in your handlers | See also

Proportional-symbol (bubble) maps | Equal-area tile grids | Flow maps | Labels | Maps that need optional packages

Abstract | Introduction | Core data assembly | world_data() | country_data() and attach_geometry() | Visualising: the choropleth and beyond | One-line choropleths | Proportional-symbol maps | Equal-area tile grids | Flow maps | The join engine | Diagnostics: never lose a country silently | Reference data and code translation | Analysis helpers | Performance and offline use | Conclusion | Session information

A map-ready tibble in one call | Your first choropleth | Choosing indicators | Next steps

Standardise any frame | One call to a map | Reconcile two messy tables | Check before you trust | Custom origins

An equal-area, projected choropleth | Just the canvas | Recentring and the antimeridian | Region subsetting | Simplifying for the web

Setup data | Sensitivity analysis to Borusyak and Hull (2023) | Reference

Overview | Puzzle Rules | Programmatic Play | Autoplay Strategies | Benchmarking | Local High Score

Play The Applet | Shiny Or Static HTML | Export A Local Copy

Introduction | Background | Key Features | Mathematical Background | The HOIF Framework | U-Statistics Formulation | Sample Splitting | Installation | Setting up the Python backend | No Python at all? | Quick Start Example | Generate Simulated Data | Split the Sample | Estimate (Misspecified) Nuisance Functions on the Nuisance Sample | The First-Order AIPW Estimator and its Bias | Compute the eHOIF Estimator (with sample splitting) | Compute the sHOIF Estimator (without sample splitting) | Debias the AIPW Estimator | Visualize Convergence | Main Function: hoif_ate() | Arguments | Return Value | Advanced Usage | Using Basis Expansion | Sample Splitting (Cross-Fitting) | Regularized Gram Matrix Inversion | Pure R Backend | Computational Details | Python Backend (ustats) | Pure R Implementation | Performance Considerations | Practical Recommendations | Choosing the Transformation Method | Choosing the Order | Use a GPU if Available | Troubleshooting | Python Backend Issues | Numerical Instability | Extreme Propensity Scores | References

Preliminaries | Factorial (Multi-Split) Designs | Example 1: A Two-by-Two Factorial Trial | Example 2: Larger Factorial Grids | Example 3: Asymmetric Factorial Designs | Example 4: Factorial Designs from Row-Level Data | Example 5: Pooling Twice into a Single Cohort | Example 6: Factorial Layouts via the DOT Engine | Hierarchical (Nested) Exclusion Reasons | Example 7: Manual Nested Reasons | Example 8: Two-Column Reasons from Row-Level Data | Example 9: Nested Reasons via the DOT Engine | Visual Customization | Example 10: Custom Font Sizes | Example 11: Custom Colors | Example 12: Font Family | Example 13: Regional Number Formatting | Global Options | Multi-Line Phase Labels | Example 14: Wrapped Phase Labels | Example 15: Explicit Line Breaks | Further Reading

Preliminaries | Operating Modes | CONSORT — Randomized Controlled Trials | Example 1: Data-Driven Two-Arm Trial | Example 2: Data-Driven Three-Arm Trial | Example 3: Manual Mode | Example 4: Count-First Display Mode | STROBE — Observational Cohort Studies | Example 5: Single-Arm Cohort | Example 6: Exposure-Stratified Cohort | STARD — Diagnostic Accuracy Studies | Example 7: Index Test and Reference Standard | Cohort Extraction | Inspecting the Diagram Structure | Saving to File | Further Reading

Preliminaries | Generating DOT Output | Example 1: Basic DOT String | Example 2: Multi-Arm Trial (CONSORT) | Example 3: Systematic Review (PRISMA) | Customizing DOT Output | Example 4: Changing Node Colors | Example 5: Count-First Layout | Example 6: Rich (HTML) Formatting | Example 7: Times Typography | Example 8: Adding Graphviz Attributes | Font Formatting Notes | The plot() Method | Bullets vs. Indentation | Saving to File | Advanced Rendering Options | Saving as HTML | Saving as PNG | Choosing Between Engines | Further Reading

Preliminaries | Manual Entry | Example 1: Screening Validation Study | Example 2: Per-Stratum Exclusion Reasons | Data-Driven Flow | Example 3: Data-Driven Split and Recombine | Cohort Extraction | Re-Splitting after Recombination | Example 4: Risk Stratification Followed by Randomization | Design Considerations | Saving to File | Further Reading

Preliminaries | PRISMA — Three-Column Layout | Example 1: Full Three-Column PRISMA Diagram | Example 2: Three-Column Count-First Layout | PRISMA — Two-Column Layout | Example 3: Two-Column Sources | PRISMA — Single-Column Layout | Example 4: Flat Source List | MOOSE — Observational Meta-Analysis | Example 5: MOOSE Flow Diagram | Source Group Structure | Saving to File | Further Reading

Benchmark | Data analysis pipeline | Genomes-to-Field data | R-scripts | Data preparation | Simulation experiments | Visualizing results | Application in Genomic Prediction | Bayesian Ridge Regression (BRR) implementation | Analysis of variance | Cross-validation | References

Basic Workflow | sentix_summarize() | sentix_annotate() | Managing udpipe model | With multiple texts | With dataframe | Using Different Lexicons | Polypathy Handling | References

Setup | sentixr with tidytext | Get the Lexicon | Tokenize and Join | Analyze | Polarity Analysis | sentixr with Quanteda | Creating a Quanteda Dictionary

Setup | Activities | Activity detail | Daily wellness metrics | Training load | Workout programmes | Training calendar | Putting it together

Why taichi? | Reading a single symbol | The example data | A first taichi grid | Fewer cells, bigger glyphs | Which source should be yin? | Customizing the color scales | Removing the panel padding | Comparing places with facets | Theming | Acknowledgement

Overview | Inducing blockwise missingness | Train / test split | Fit BRM with a logistic-regression learner | Evaluate | Citation

Overview | Inducing a blockwise missing pattern | Train / test split | Fit BRM | Predict and score | Comparison to a listwise-deletion baseline | Citation

Overview | Induce missingness, split, fit | Citation

Overview | Features | Requirements | Installation | From GitHub (recommended — latest version) | From release tarball | CLI Setup | Verify installation | Quick Start Examples | 1. Basic imputation | 2. Imputation with missing SNP fill-in | 3. Training a new model | 4. Evaluating a trained model | Core Usage Reference | Help | impute command | Required flags | Optional flags | train command | KIR file format | evaluate command | Pre-phasing the KIR Region | hg19 | hg38 | Improving Imputation Accuracy | Option A: Local pre-imputation (built-in, quick) | Option B: External pre-imputation (recommended for highest accuracy) | Option C: Force imputation (not recommended) | Next Steps

Overview | Prerequisites | Step 1: Prepare Input Data | Step 2: Run Basic PONG2 Imputation | Step 3: Check SNP Overlap | Step 4: Pre-imputation (when SNP overlap < 50%) | Pre-phase with Eagle2 | hg19 | hg38 | Option A: Local Pre-imputation with minimac4 (built-in) | Option B: External Pre-imputation (recommended for highest accuracy) | Step B1: Export phased VCF | Step B2: Upload to Michigan Imputation Server | Step B3: Download and convert imputed VCF to PLINK | Step B4: Run PONG2 on imputed data | Option C: Force imputation (not recommended) | Step 5: Interpreting Output | Output CSV format | Large sample datasets | Summary: Which Workflow to Choose? | Next Steps

Overview | 1. Installation and Setup | 2. Example Data | 3. KIR Genotype Prediction | 4. Model Training | 5. Model Evaluation | 6. CLI Usage | Session Info

Overview | Prerequisites | Step 1: Prepare Input Data | 1a. Reference genotypes (--bfile) | Using the 1000 Genomes Project (1KGP) as reference panel | Using your own reference dataset | 1b. Known KIR allele calls (--kfile) | Format | Rules | Step 2: Run Training | With optional parameters | Key training parameters | Step 3: Training Output | Step 4: Evaluate Model Performance | Option A: Evaluate from the terminal (recommended) | Option B: Evaluate in R | Step 5: Use a Custom Model for Imputation | Troubleshooting | Next Steps

A deliberately messy dataset | One call to profile it | Drilling in with summary() | The object is just a list | Figures | Tuning the run | Beyond correlation (0.2.0) | A full HTML report

Load packages and helper function | Prepare the data | Choose settings | Run double cross-validation | Optional parallel execution | Compare model sizes | Select a model size | Inspect the selected model size | Williams plot across outer folds | Best fitness plot | Permutation test | Save results | Summary

Load packages and helper function | Prepare the data | Choose settings | Split the data | Run the GA for several model sizes | Optional parallel execution | Predict the test set | Compare Q2 values | Williams plot, fitness plot and Observed versus Predicted plot | Select one model | Permutation test | Save results | Summary

Overview | Example: Drosera in the United Kingdom | Step 1 — Find taxon and place IDs | Step 2 — Fetch observations | Step 3 — Download photos and metadata | Step 4 — Export metadata separately | Finding taxon and place IDs | All annotation options | Further filtering options | Photo licenses & attribution

Input data | Minimal example | Outcome | Customising "clinically relevant change" | Baseline scheme | Additional options | Multiple events | Event confirmation | Sustained CDW or CDI | Relapse-based classification of CDW events | Relapse-associated worsening (RAW) | Progression independent of relapse activity (PIRA) | MSprog() outputs | What to include in results | Printing progress info | Time to disability milestone | References

Defining PIRA | Detecting PIRA | References

Time to first disability worsening event | Time to disability milestone | Multiple events

Introduction | The Package in a Glance | General Formulation of the Univariate Stratification Problem \label | Dynamic Programming Technique as a Solution Procedure | Optimum Sample Sizes Using Neyman Allocation | Overview of Package Functionalities | The Function strata.data() | The Function strata.distr() | Application to Numerous Survey Populations | Stratification for a Survey Variable with Pareto Type II Distribution | MPP Formulation for Pareto Type II Distribution | DP Solution for Pareto Type II Distribution | A Numerical Example for Pareto Type II Distribution | Stratification for a Survey Variable with Triangular Distribution | DP Solution for Triangular Distribution | A Numerical Example for Triangular Distribution | Stratification for a Survey Variable with Right-Triangular Distribution | DP Solution for Right-Triangular Distribution | A Numerical Example for Right-Triangular Distribution | Stratification for a Survey Variable with Weibull Distribution | DP Solution for Weibull Distribution | A Numerical Example for Weibull Distribution | Stratification for a Survey Variable with Gamma Distribution | DP Solution for Gamma Distribution | A Numerical Example for Gamma Distribution | Stratification for a Survey Variable with Exponential Distribution | DP Solution for Exponential Distribution | A Numerical Example for Exponential Distribution | Stratification for a Survey Variable with Uniform Distribution | DP Solution for Uniform Distribution | A Numerical Example for Uniform Distribution | Stratification for a Survey Variable with Normal Distribution | DP Solution for Normal Distribution | A Numerical Example for Normal Distribution | Stratification for a Survey Variable with Log-Normal Distribution | DP Solution for Log-Normal Distribution | A Numerical Example for Log-Normal Distribution | Stratification for a Survey Variable with Cauchy Distribution | DP Solution for Cauchy Distribution | A Numerical Example for Cauchy Distribution | References

Overview | Installation | Quick start | Offline imputation: choosing a method | LLM-mode imputation | Domain-specific imputation | Choosing a model | Inspecting the audit trail | Large datasets | Tips for best results

1. Minimal example: data with prey replacement | 2. Minimal example: data without prey replacement

The problem: names lie, data does not | A worked example | How a role is scored | Shannon entropy | Normalized mutual information | The optional, capped name bonus | Header-aware loading | Attribution

Introduction | Background: Threat Taxonomy and Related Software | Disclosure Threat Taxonomy | Risk Assessment Paradigms | Utility Assessment Paradigms | Related Software | Software Design and Architecture | Design Philosophy | The synth_pair Container | S3 Method Pattern | Integration with the R Ecosystem | Risk Measures | Privacy Models and Frequency-Based Risk | Attribution-Based Risk: The CAP Family | ML-Based Risk: RAPID | Distance-Based Risk | Record Linkage Risk | Membership Inference and Anonymization Failure Criteria | Cross-Family Comparison | Data Utility Measures | Global Utility: Propensity Scores | Univariate Diagnostics | Multivariate and Structural Utility | Predictive Utility | Comprehensive Assessment: A Case Study | Scenario and Data | Step 1: Quick Risk Screening with disclosure_report() | Step 2: Comparative Assessment with rumap() | Step 3: Decision | Summary and Discussion | Contributions | Partially vs Fully Synthetic Data | Remediation | Limitations and Recommendations | Future Work | Computational Details | References

Introduction | Main function | Function process_inputs() | Parameter opt | Parameter args | Validation rules | Function process_end() | Parameter packages | Usage example | Session information

Goal of this vignette | Reference | Shared experimental setup | Run all four variants | Inspecting the Pareto fronts numerically | Visual comparison of the four fronts | One panel per variant | All variants superposed | A unified "ground truth" front | Runtime cost | Picking a best-compromise tree per variant | Three-objective example for every variant | Summary

Overview | Reference | Installation | A first end-to-end example | Step 1. Generate a random bi-objective instance | Step 2. Run the NSGA-II solver (base variant) | Step 3. Inspect the global Pareto front | Step 4. Plot the Pareto front | Step 5. Decode and plot the best-compromise tree | Working with three objectives | Reproducibility | Where to go next

Background | Divergence coefficient | Scatter ellipses | Transgression | Classification | When to use this package | References

Introduction | Problem setup | Develop the random forest classifier | Specify the response function and input space | Search region and input scaling | Define the level set of interest | Profiling the response function along some rays | Identifying the high-probability Adelie region of feature space | Slicing the level set | References

Introduction | Example: Circuit failure data | Adaptive selection of rays | Slicing input space | References

Introduction | An example | Specifying the response function and its input space | The fnObj function and class | respfn and ... arguments | feasfn, feasbnds and ... arguments | hasgrad and derivmethod arguments | inptol and resptol arguments | fnSpec class and function | Specifying the search region for a level set | Specifying search rays | Rays and hyper-rectangles | Scaling of input space dimensions | Profiling a response function along rays | Level set boundary search | bdryFromRays(): Fixed set of rays | The search algorithm | lsetSegs objects | bdrySearch(): Adaptive selection of rays | Slices of input space

An example | Main search functions | References

Testing kumquat with the given datasets | Models | Visualizing kumquat with the given datasets

Introduction | A first clustering | Choosing an algorithm | Comparing distance computations | Initialization and reproducibility | Working with the bundled data | Safeguards for degenerate inputs | Citation

Overview | Summary of supported model types | Linear regression | Logistic regression | Poisson regression with an offset | Piecewise exponential survival model via Poisson regression | Native Cox regression | Gamma regression | Negative binomial regression (fixed θ) | Multinomial regression (experimental) | Multi-response Gaussian (experimental) | Limitations

What fbrglm is for | A small binomial example | Choosing lambda | Factor predictors | Missing values | Offsets | Reaching the underlying glmnet objects | Limitations (intentional)

Explodemap-style HHS Fixture | Non-map Panels | CSV Round Trip | Labels, Boxes, And Connectors | Selected Labels And Legend Keys | Movement Context | Shiny

Toy geometry | drag_map_prototype() — full parameter reference | Data | Labels | Label styling | Connectors | Movement context | Initial positions | RStudio addin | Colour palette | Legend | Output file and display | Optional: Static rendering | Running examples

Label Concepts | Info Boxes And Text-only Labels | Connector Lines | Region Movement Plus Label Movement | Legend Filtering And Movement Context | Saving Static Images | Rendering A Spatial Studio Project

Embed A Draggable Plot | Custom Labels | Export A Report Image | Static Only | Spatial Studio | Loading veil | Reusable Shiny Helpers

Purpose | Choosing an estimator | Basic R workflow | Reading the main options | Treatment categories | Partial identification | Covariates | Inference | Translating from Stata | Practical checklist | References

Introduction | Data Preparation | Loading the Data | Constructing the Analysis Sample | Constructing Group Variables | Two-Arm Estimation | Arm 1: Districts with Increasing Education (g* = 1 vs g* = 0) | Arm 2: Districts with Decreasing Education (g* = -1 vs g* = 0) | Aggregating Results | Comparison with Stata | References

Introduction | Simulated Data Example | Basic Estimation | Wald-DID, Wald-TC, and Wald-CIC | Extracting Results Programmatically | Core Stata Parity Check | Key Parity Points | 1. User-Controlled Bootstrap Seed | 2. Design Restrictions | 3. Partial Identification Bounds | 4. Equality Tests | Covariate Adjustment | Parametric Adjustment with modelx | Nonparametric Adjustment with Sieves | LQTE Estimation | Multi-Period Designs | Bootstrap Diagnostics | References

Keeping up with the repositories | Improving packages | Connecting help pages | Reproducibility | Local versions | Risk of being archived

Sample data | 1. Basic usage | 2. Lane ordering with sort_by | 3. Limiting subjects with n_max | 4. Controlling points | 5. Customising point appearance | 6. Controlling line appearance | 7. Discrete color bands with scale_colour_kodom(discretize = TRUE) | 8. Independent size and linewidth — encoding patient covariates | 10. Faceting by a grouping variable | 11. Y-axis subject labels | 12. Full palette reference

Overview | Installation | The Model | Quick Start | The toy_curves Dataset | Fitting a Model | Single $K$ | Automatic $K$ Selection via GCV | Per-Curve $K$ Selection | Fourier Basis | Interpreting the Output | Summary | Coefficient Matrix | Posterior Inclusion Probabilities | Predictions | Plot | Reference | Citation

Multiclass Classification | Binary Classification and Weights | Regression | Robust Regression | Training Controls | Tuning and Cross-Validation | Permutation Importance | Save, Load, and Inspect | Function Map

How audit_script() detects calls | What gets skipped | Single-file vs directory scan | Version resolution | The audit_report object | How risk_score() works | Check 1: "changelog" — the breaking-changes database | Check 2: "seed_check" — missing set.seed() | Check 3: "locale_check" — locale-sensitive operations | Combining checks and filtering | Working with the results | Practical interpretation

The problem they solve — a real scenario | Scenario — The revision drift problem | certify() — creating a baseline | What gets hashed | Choosing what to certify | Tags and the certification store | list_certs() — inspecting the store | check_drift() — comparing against a baseline | Basic usage | The four statuses | Using "latest" | Using drift results programmatically | Recommended workflow | At submission | After reviewer comments | Version control

What belongs in the database | Examples of things that belong | Examples of things that do not belong | The database schema | The version window | Rules for setting to_version | Quick reference | Risk levels | Finding candidates | From NEWS.md files | From your own experience | Writing an entry | Testing your entry | Submitting a pull request | Minimum test for a new entry | Keeping the database current

repro_report() — generating reports | The verdict | Style: "minimal" | Style: "academic" | Style: "pharma" | Format: "md" and "html" | All nine combinations | repro_badge() — status badges | Badge colours | Inserting into README.md | Removing a badge | Full CI pipeline

What is reproducr? | Why this matters — real failure modes | Scenario 1 — The collaborator upgrade problem | Scenario 2 — The server deployment problem | Scenario 3 — The renv false sense of security | Tier 1: Scan and score | Auditing a script | Scoring for risk | Tier 2: Baseline and drift detection | Certifying outputs | Checking for drift | Tier 3: Report and export | The full pipeline

Overview | The CAMEL Framework | Installation | Quick Start | Computing the CAMEL Index | Accessing Detailed Results | Visualizing Results | Data Format | Data Frame Input | Matrix Input | Understanding the Output | Key Metrics | Methodology | References

Introduction | The three lenses | Distance-decay | Zeta diversity | Standardised effect sizes | Simulating a structured community | SES null models | Comparing null models | Practical guidance | References

Introduction | The diversity framework | Simulating data | Hill number accumulation | Alpha, beta, and gamma diversity | Checking and interpreting with uncertainty | Beta, functional, and phylogenetic beta diversity | Phylogenetic and functional accumulation | Rao's quadratic entropy | Coverage-based diversity | Custom diversity metrics | Practical guidance | References

Overview | Algorithm overview | Data setup | Mathematical detail | Asymptotic extrapolation | Model comparison | EVT model | Fitting and fit quality | Tuning | Coverage extrapolation and DAR | Predicting richness at new effort levels | Comparison to alternatives | Practical guidance | References

Introduction | The basic workflow | Accumulation methods | Custom accumulation order | Comparing curves | Extrapolation and prediction | Pre-computing distances | Taste tests | Hill numbers | Beta diversity | Coverage standardization | Richness estimators | Community turnover | Conservation and spatial metrics | S3 methods | Practical guidance | Next Steps | References

Introduction | Methods and theory | Individual-based rarefaction | Sample coverage | Simulating uneven effort | Analytical sample-based alternatives | Coverage-based standardization | Interpolation at fixed coverage | Extrapolation beyond observed | Hill numbers with coverage | Spatial subsampling | Comparison on the same data | Practical guidance | References

Overview | Algorithm overview | Data setup | Non-parametric richness estimators | Abundance-based: Chao1 and ACE | Incidence-based: Chao2 and jackknife | Bootstrap estimator | Comparison table | Mathematical detail | Chao1 | Chao2 | ACE | Jackknife | Bootstrap | iChao1 and iChao2 | Behaviour with sample size | Tuning | Comparison to alternatives | Sample completeness profile | Practical guidance | References

Introduction | Models and theory | Endemism-area concept | Species-fragmented area relationship (SFAR) | Sampling-effort corrected SAR (SESARS) | Moran eigenvector maps | Simulating range-size variation | Endemism-area curves | Per-site metrics for spatial prioritisation | Moran eigenvector maps and spatial partitioning | Wavefront expansion | Spatial subsampling | Practical guidance | References

Available cardinal models | Robust fitting helper | Temperature model | pH model | Water activity model | Inhibitor model | Collecting diagnostics | Practical notes

Fit candidate models | Extract fitted values and residuals | Calculate diagnostics for one model | Compare models

Consistent Use of Natural Logarithm | Converting from log10 to ln

Overview | A dynamic temperature profile | Dynamic growth prediction | Dynamic growth fitting | Dynamic inactivation prediction | Dynamic inactivation fitting | Sensitivity analysis | Practical notes

Loading data | Plotting data | Fitting the Huang full model | Fitting the Fang no lag model | Fitting the Buchanan reduced model

Introduction | Primary growth models | Full growth models | Huang model | Rosso model | Baranyi & Roberts model | Zwietering reparameterised Gompertz model | No stationary phase growth models | Two-phase linear model | No lag phase growth models | Richards model | Fang model | The cardinal parameter model | Cardinal Parameters | Cardinal parameter models available in predmicror | Cardinal parameter model for temperature | Cardinal parameter model for pH | Cardinal parameter model for Aw | Cardinal parameter model for inhibitory substance | References

Available inactivation models | Example data | Fitting candidate models | Diagnostics and model comparison | Fitted values and residuals | Prediction over a time grid | Practical notes

Overview | Omnibus inactivation model | Omnibus growth model | Using parameterised secondary models | Validation metrics | Practical notes

Introduction | Penalized Principal Machines | Principal machine | Penalized estimation | Supported losses and algorithms | Usage | Regression | Binary classification | Choosing lambda by cross-validation | Real-data example: Wisconsin Diagnostic Breast Cancer | References

title: "End-to-end encryption" | Security model | The pieces | Setup: a device with published keys | Sending | Receiving | Persistence | Marking a room encrypted

Requirements | Installation | Usage | Reporting bugs | Keywords | References | Example

Overview | The data | Fitting a simplex regression model | Influence diagnostics | The bootstrap goodness-of-fit test | Half-normal plot with simulated envelope | Convenience plot methods | Next steps

The ammonia application | One-call reproduction | Parameter estimates (Table 5 of the paper) | Goodness-of-fit results (Table 6 of the paper) | Step by step | References

The PBSC application | The data | One-call reproduction | Parameter estimates | Goodness-of-fit results | Step by step | References

Introduction | Data Analysis using DPComb_tests | How the Combination Tests Work | Alternative Input Format | Code and Results | Analysis of Gene 1 using test_case_control_fisher

The idea | Data format | A first analysis | Which way is "better"? | Baseline risk: matched or median control | Per-study results and a forest plot | Random effects and heterogeneity | A threshold-free alternative | A real example: VAS pain after exercise therapy | The Shiny application | Where next

The cut-point approach | The pooling methods | Individual (the default workhorse) | Weighted mean | Unweighted mean and median | Baseline risk: matched or median control | Relative effect measures | Common-language effect size (threshold-free) | The SMD bridge (method = "smd") | Random effects and heterogeneity | Refinements | Assumptions and limitations | Choosing a method | References

Overview | Create Example Data | KPI Summary | Provider Filtering | Provider Summary | Pathway Summary | Mixed-Effects Model | Intraclass Correlation Coefficient | Fixed-Effect Estimates | Adjusted Provider Effects | Adjusted Pathway Predictions | Provider Clustering | Sensitivity Analysis | Diagnostic Utilities | Plots | Export Results | Full Applied Workflow | Summary

Introduction | Installation | Independent observations — glm | Data | Fitting the model | Delta method — conditional standardisation | Delta method — marginal standardisation | Custom reference values | Forest plot | Bootstrap confidence intervals | Clustered / multilevel data — glmer (lme4) | Longitudinal data — GEE via geepack | Complex survey data — svyglm (survey) | Comparing OR and PR | Methodological notes | Conditional standardisation | Marginal standardisation | Delta method | Session information | References

Example 1 — Low Birth Weight (LBW): clustered binary data | Data | Independent GLM (ignoring clustering) | GEE — accounting for within-mother correlation | Mixed model (random intercept per mother) | Example 2 — Thailand Education Study: multilevel data | Independent GLM | Mixed model | Example 3 — Toenail Infection Trial: longitudinal data | GEE | Example 4 — UIS Drug Treatment Study | GLM — independent observations | OR vs PR comparison | Bootstrap CIs | Example 5 — Downer Cow Survival | GLM | Example 6 — Titanic Survival | GLM — OR vs PR | Forest plot | Key comparison for Titanic | Summary | Session information

1. Import CPCe data | 2. Match images | 3. Bare workflow from raw CPCe labels | 4. Optional: read a crosswalk table | 5. Optional: check unmapped labels | 6. Optional: apply label standardization | 7. Validate standardized points | 8. Create standardized ecological summary tables | 9. Create ML point-label CSVs | 10. Extract point patches | 11. Create sparse masks | 12. Create QC overlays | Full workflow wrapper

Installation from GitHub | Simulate Gaussian data | Fit the model | Coefficients and predictions | Diagnostics | Plots | Optional: bandwidth selection

Introduction | 🔐 Anonymizing Personally Identifiable Information (PII) | Example: Masking a Patient Dataset | 📝 Dictating Data Dictionaries | 🧪 Scaffolding Unit Tests

Introduction | 🚀 Bootstrapping the Development Environment | 📦 Auditing & Scanning Dependencies | Auditing DESCRIPTION Dependencies | Scanning Active Session Dependencies | 🧹 Safely Uninstalling Packages | Uninstalling a Single Package | Resetting the User Library | 🛡️ Git Hooks & Safety Pre-flights | 🔄 Managing Function Deprecations | 🚀 Automating Releases

Introduction | 📦 Package Development Workflow | Dependency Management | Scaffolding & Automation | 🛡️ Session Auditing & Reproducibility | State Management | Reproducibility Testing | 🧹 System & Memory Optimization | Memory Cleanup | Safe Processing | 🔐 Data Privacy & Documentation | Anonymization | Documentation | 🌐 Network Utilities | Summary Table

Introduction | 🧹 Interactive Memory Cleanup | Sweeping Large Global Objects | Cleaning Temporary Files & Orphaned Devices | 🛡️ Safeguarding Iterations with the Loop Guardian | 💾 Crash-Resilient Batch Processing (Save & Resume) | ⚡ Scaffolding Parallel Pipelines | 🌐 Resilient and Polite Network Requests

Introduction | 🕵️ Auditing Script Side Effects | ⚠️ Detecting Namespace Masking | 🧪 Clean-Room Simulation | 📸 Session Snapshots for Portability | ⏱️ Performance Benchmarking across Git Branches

TL;DR | How the Python environment is resolved | Option 1: automatic (recommended) | Option 2: a persistent environment with setup_ustats() | Option 3: bring your own environment | Verifying the setup | Computing U-statistics | GPU acceleration | Troubleshooting

Introduction | The built in dictionary | Scoring a text | Listing suggestions | Rewriting a text | Using your own dictionary | Working with files | Limitations | Conclusion

Introduction | Package structure | Installation | Temporal Analysis | Mann-Kendall Trend Test — mk_test() | Sen's Slope Estimator — sens_slope() | Change-Point Detection — change_point_detection() | Seasonal Decomposition — seasonal_decompose_climate() | Rolling Trend Analysis — rolling_trend() | SNHT Homogeneity Test — temporal_homogeneity() | Multiple-Station Trend Significance — trend_significance() | Autocorrelation Analysis — autocorrelation_climate() | Spatial Analysis | Moran's I — morans_i() | Hot-Spot and Cold-Spot Detection — hot_cold_spots() | Spatial Interpolation — spatial_interpolate() | Spatial Trend Field — spatial_trend_field() | Climate Zone Classification — cluster_climate_zones() | Elevation Lapse Rate — elevation_lapse_rate() | Extreme Event Analysis | GEV Distribution — fit_gev() and rgev_sim() | Return Levels — return_period() | Peaks-Over-Threshold — peaks_over_threshold() | Heat Wave Detection — heat_wave_detection() | Cold Spell Detection — cold_spell_detection() | Drought Spell Detection — drought_spell() | Hill Tail-Index Estimator — extreme_value_index() | Climate Indices | Standardised Precipitation Index — spi() | SPEI — spei() | Simplified PDSI — pdsi_simple() | Heat Index — heat_index() | Wind Chill — wind_chill() | Frost Days — frost_days() | Growing Degree Days — growing_degree_days() | Diurnal Temperature Range — diurnal_temp_range() | Detection and Attribution | Signal Detection — detection_attribution() | EOF Fingerprint Analysis — fingerprint_analysis() | Optimal Fingerprint Regression — optimal_fingerprint() | Data Quality and Utilities | Gap Filling — fill_gaps_climate() | Homogenisation — homogenize_series() | Temporal Aggregation — aggregate_climate() | Anomaly Calculation — anomaly_baseline() | Standardisation — standardize_climate() | Comprehensive Summary — climate_summary() | References

Quick start: every workflow, the easy way | The default Notre Dame palette | From one group to ten | The scales in practice | A gallery of plot types | Base R graphics | Statistical and psychometric visualization | Finding a color by name and role | Mixing in a color from outside the palette | Other palettes | Former Notre Dame colors | Colorblind-friendly Notre Dame colors | Brand tints, backgrounds, and sequential ramps | Theming R Markdown reports | Pairing with a light theme | A note on fonts | Related color tools

A complete example app | How the theming works | A brand-colored bslib theme | Brand-colored figures | Reusing the R Markdown stylesheet | All the colors, with details | The default Notre Dame palette ("nd") | The colorblind-friendly ordering ("nd_cvd") | The former Notre Dame colors ("former") | Near-white brand tints and informal backgrounds | The full color catalog

1. Fit | 2. Inspect the fit | 3. The pathway tables | 4. Prune to the reliable tree | 5. Predict | 6. A first tree plot | 7. Trajectory trees: where sequences go, and how predictably | By frequency -- how many sequences walk each path | By predictability -- how confidently the model calls each step | Where to go next

1. The data | 2. Fit | 3. Inspect | 4. The pathway tables | 5. Per-context diagnostics | 6. Prune to the reliable tree | 7. Held-out predictive quality | 8. Bootstrap reliability | 9. Do high and low achievers regulate differently? | Synthesis

The shared dataset | Route A -- wide sequence data, directly | Route B -- a tna transition-network object | Route C -- a Nestimate network object | They agree | The boundary: sequences, never aggregated transitions | Group objects

Setup | 1. Smoothing schemes | 2. Pruning criteria | 3. Cross-validated tuning | 4. Bootstrap pathway reliability | 5. Comparing two cohorts | 6. Tree introspection | 7. Mining contexts and sequences | 8. Imputing gaps | 9. Generating sequences

Setup | 1. The fitted tree, four ways | Horizontal phylogram (default) | Radial dendrogram | Icicle / sunburst | 2. Pathway-centric plots | Next-state heatmap | Divergence lollipop | Per-context distributions | 3. Diagnostic plots | How much memory does one pathway need? | Predictive quality | 4. Forward trajectory trees | 5. Inferential plots | Bootstrap forest plot | Per-pathway resample distributions | Cohort comparison: permutation null | Tuning surface | Group difference map | Recap

Overview | Installation | Quick start: bladder cancer data | Fitting the models | Ghosh-Lin model (Box-Cox, rho = 1) | Proportional odds model (logarithmic, r = 1) | Prediction | Choosing the transformation parameter | Standard errors | S3 methods | References

Introduction | The Gamma Frailty Framework | Installation | Random Number Generation | Plot baseline distributions | Simulating Frailty Survival Data | Fitting Models | No-covariate model (complete data) | Model with covariates (right-censored) | Formula interface | Inference and Model Metrics | VIF and Tolerance | Residuals and Diagnostics | Diagnostic Plots | Core residual plots | Survival and influence plots | Prediction | Model Comparison | Cross-Validation | Censored Data - All Types | Left censoring | Interval censoring | Type-I censoring (fixed time) | Type-II censoring (fixed failure count) | Progressive Type-I censoring (withdrawals at fixed times) | Simulation Study | References

Expected file layout | Provider formats supported | Extraction pipeline | Indexing tables | Master record catalog | Composing with the processing core | Audit helpers | Maintenance | Notes

Input and output | Input | Output | SDOF model | Discretisation via matrix exponential | How PSA / PSV / SD are built | D50 and D100 horizontal spectra | Notes (limitations and corner cases) | References

Synthetic input | Run AT2TS | Intensity measures | Response spectrum | IMF decomposition | Where to go next

What is an IMF? | Input and grouping | Available engines | VMD (method = "vmd", default) | EMD (method = "emd") | EEMD (method = "eemd") | Output | Per-IMF metrics | Reconstruction filters | Composing with AT2TS / VT2TS / DT2TS | References

Expected input | Units | Internal unit conventions | Implemented measures | Long and wide output | Acceleration (ID = "AT") | Velocity (ID = "VT") | Displacement (ID = "DT") | What it does not compute | References

Data formats | Input (wide) | Outputs | Time regularisation | Internal STFT Strategy Selection | STFT audit: auditSTFT() | STFT / OLA filtering: .ffilter() | Integration: .integrate() | Differentiation: .derivate() | Anti-alias resampling: .resample() | Edge tapering: .taperA() | Notes (parameter audit) | References

A note on data | 1. Simple case (Table 4--6) | Conventional and robust metrics (Table 5) | Accuracy-level metrics (Table 6) | 2. Regression with outliers | 3. Time-series case | 4. Framework integration | caret | tidymodels / yardstick | forecast | Session info

Introduction | The nyc_pull_dataset() function | The nyc_any_dataset() function | Rule of Thumb | Real World Example | Mini analysis | Summary | How to Cite

Setup | The Q-sort data | Fitting the model | Diagnostics | Factor loadings | Factor z-scores | Choosing K | Distinguishing, consensus, and membership | Posterior predictive check | Hyperparameters | Reporting and exporting | Theming | Where next | References

How to cite | Software needed to run iSTAY in R | Installing and loading iSTAY | FIVE MAIN FUNCTIONS | Datasets Provided with the 'iSTAY' package | Data Conversion | Single metacommunity dataset | Converting metacommunity data to community data | Single community dataset | Hierarchical structure data | Example 1: Comparing stability profiles for two selected individual plots | Example 2: Assessing diversity-stability relationships based on 76 individual plots | Example 3: Comparing stability profiles for two selected individual populations | Example 4: Assessing diversity-stability relationships based on 462 individual populations | Example 5: Comparing gamma, alpha, and beta stability profiles, and synchrony profiles in two selected communities | Equal-weighted analysis | Biomass-weighted analysis | Example 6: Assessing relationships between diversity and gamma, alpha, and beta stability, as well as synchrony, across 76 communities | Example 7: Comparing gamma, alpha, and beta stability profiles, and synchrony profiles for two selected metacommunities | Example 8: Assessing relationships between diversity and gamma, alpha, and beta stability, as well as synchrony, across 20 metacommunities | Example 9: Plotting stability and synchrony profiles at each hierarchical level | References

Overview | Minimum Detectable Relative Risk (MDRR) | What it checks | Method | Interpretation | Example | Role in blinding | Pre-Exposure Gain | Why it matters | Event-Dependent Observation | Expected Absolute Systematic Error (EASE) | When it runs | Tiered Blinding | Running Diagnostics | Customizing thresholds | Selecting specific diagnostics | Inspecting failures

Setup and Data | Running a Basic Analysis | Understanding Risk Windows | Using Custom Cohorts | Diagnostics and Tiered Blinding | Empirical Calibration | Multi-Analysis Workflow | Working with the Results Database

Research question | Data | Fit the ODA model | Rule and confusion matrix | ESS / PAC / PV interpretation | Monte Carlo and LOO validity | Notes on reproducibility

Research question | Data | Fit the ODA model | Rule and confusion matrix | ESS / PAC / PV interpretation | Monte Carlo and LOO validity | Notes on reproducibility and current scope

Research question | Data | Fit the ODA model | Rule and confusion matrix | ESS / PAC / PV interpretation | Monte Carlo and LOO validity | Notes on reproducibility and current scope

Binary ODA | Classification Tree Analysis | Further reading

Research question | Data | Fit the ODA model | Rule and confusion matrix | ESS / PAC / PV interpretation | Monte Carlo and LOO validity | Notes on reproducibility

Overview | The GKRR method | Model and objective function | Estimation algorithm | Width hyper-parameter $\gamma^2$ | Basic usage | Statistical inference | Sandwich variance estimator (default) | Bootstrap inference | Choosing between sandwich and bootstrap | Diagnostic plots | Comparison with other methods | Real-data applications | Belgium international calls — Y-space outliers | Delivery time — leverage points in multiple regression | Mammals — leverage on the log scale | Session information | References

Initializing an odm object via create_odm_from_cellranger() | Interacting with the odm object | Supported modalities | The cell-wise covariate data frame | Reading an .odm file into R | Initializing an odm object via create_odm_from_r_matrix() | Notes on compression

Load data | Run clustering | Threshold-based evaluation | Compute your own protein ranking | Filtering (optional) | The complete SQRP pipeline: | Preprocessing (optional)

1. Authentication | 2. The One-Stop Solution (formr_api_results) | What does formr_api_results do by default? | Customizing the Fetch | 3. Advanced: The Manual Pipeline | B. Reverse Coding (formr_api_reverse) | C. Aggregation (formr_api_aggregate) | 4. Full Workflow Example | 5. Troubleshooting | "My scale isn't calculating!"

Installation | The "Two Ways of Connecting" | Authentication | 1. Local Setup (One-time) | 1.1 Save your API Credentials | 1.2 Save your regular User Credentials (Classic) | 2. Authenticating in Scripts | 2.1 Thru the API | 2.2 With your User Credentials (Classic) | 3. Authenticating Inside formr Runs | Workflow Examples | Project Management (Push & Pull) | Fetching Results | Token Management & Security

Listing Files | Uploading Files | Single File | Multiple Files or Directories | Deleting Files | Cleaning Up (Delete All)

Prerequisites | Backing up a Study | The Local Development Workflow | 1. Initialize (Pull) | 2. Edit | 3. Push | Watch Mode | Managing Run Settings | Advanced: Run Structure (JSON)

Listing Your Runs | Creating a New Run | Configuring Run Settings | Managing Run Structure (JSON) | Exporting Structure | Importing Structure | Deleting a Run

Listing Sessions | Filter by Status | Pagination | Find Specific Participants | Creating Sessions | Controlling Sessions (Actions) | Use Case 1: Unsticking a User (Move Position) | Use Case 2: Cleaning Data (Toggle Testing) | Use Case 3: Ending Sessions

Listing Your Surveys | Inspecting and Downloading Surveys | Inspect Items in R | Download Survey Source (.xlsx) | Uploading or Updating a Survey | Google Sheets | Deleting a Survey

1. Why Run R Inside Your Study? | 2. Where the API Code Goes | A. Calculate Items (API Entry Point) | B. Inline R in Labels (Display + Fetch) | C. Inline R in Choices (Display) | 3. Your Toolkit | Authentication | Run Context | Fetching Data from Other Surveys | The current() Shorthand | 4. Walkthrough: Participant Counter | Run Structure | Calculate: participant_count | Survey: welcome | 5. Walkthrough: Real-Time Group Norms | Survey: feedback (label) | 6. Walkthrough: Dynamic Group Balancing | Calculate: pick_condition | Showif conditions | 7. Walkthrough: Synchronising with a Waiting Room | Participant experience | 8. Patterns for Robust Code | Next Steps

What sources are available? | Station metadata across all sources | Fetch data - router auto-detects the source | Combine with bind_rows (same schema, all sources) | Annual summary across providers | Explicit source bypasses the router

Overview | The adapter contract | Output schemas | Realtime (sub-daily) - fetch_flows_fn / fetch_levels_fn | Daily - fetch_daily_flows_fn / fetch_daily_levels_fn | Stations - list_stations_meta_fn | How the router works | Built-in adapters | Hydro-Quebec (hydroquebec) | Registration | Writing a new adapter | Step 1 - Implement the internal functions | Using a stored station list when no endpoint exists | Step 2 - Register the adapter | Step 3 - Add tests | What the schema validator will catch

Overview | Model estimation using bayestsm | Gibbs sampler | bayestsm input data structure | Prior assumptions | Basic bayestsm run | Updating previous bayestsm runs | Automatic updating till convergence | Obtaining information criteria after running bayestsm | Posterior summaries after running bayestsm | Posterior summaries of the model parameters | Posterior cumulative transition probability plots (CDFs / CIFs) | Predictive probabilities for single supplied time points | Plotting CIFs | Further topics and functionalities | Metropolis sampler | Slice sampler step size | Specifying a user-defined prior function | Internal scaling of transition times | Sequential instead of parallel processing | References

Overview | Core Quantities | Real Trial-Based CEA Example | Bootstrap Uncertainty | Net Monetary Benefit and CEAC | Deterministic Sensitivity Analysis | Reproducibility Checklist | References

About this vignette | Installation and setup | Import files from multiple sources | Deduplication and source information | Plot heatmap to compare source overlap | Heatmap by number of records | Heatmap by percentage of records | Plot an upset plot to compare source overlap | Bar plots of unique and shared records | Analyzing unique contributions | Record-level table | Search summary table | Exporting for further analysis | In summary

About this vignette | Installation and setup | Import citation files | Assign custom metadata | Deduplicate and create data tables | Review internal duplication | Compare overlap with an upset plot | Review benchmark coverage with a record-level table | Detailed source contribution table | Exporting for further analysis

About this vignette | Installation and setup | Import files from multiple sources | Deduplication and source information | Plot heatmap to compare source overlap | Heatmap by number of records | Heatmap by percentage of records | Plot an upset plot to compare source overlap | Bar plots of unique and shared records | Analyzing unique contributions | Analyze journal titles | Analyze publication years | Exporting for further analysis | In summary

About this vignette | Installation and setup | Import citation files | Assign metadata using all three fields | Deduplicate and create comparison data | Review initial record counts | Visualize overlap between strings | Upset plot by string | Heatmap by string | Compare string contributions | Benchmark coverage by string | Detailed contribution table by string | When to use cite_string vs cite_source

About this vignette | 1. Installation and setup | 2. Import citation files | 3. Assign custom metadata | 4. Deduplicate and create data tables | 5. Review internal duplication | 6. Analyze overlap across sources | Heatmaps | Upset plot | 7. Analyze records across screening phases | 8. Analyze data with tables | Detailed record table | Precision and sensitivity table | 9. Record-level table | 10. Exporting for further analysis

Descripción general | Instalación | Formato de los datos de entrada | Inicio rápido | Entendiendo el resultado | resultado$meta | resultado$lsea | resultado$chains | resultado$plots | resultado$input | Visualización de los gráficos | Exportación de resultados | Uso avanzado | Ejecutar los motores por separado | Cambiar la métrica de ranking de fgsea | Ajustar umbrales de significancia | Filtrar por nivel de confianza de anotación | Interpretación de los resultados | KS vs fgsea — ¿cuál usar? | El DirectionalScore | La métrica pi-valor | Información de la sesión

Overview | Installation | Input data | Quick start | Understanding the output | result$meta | result$lsea | result$chains | result$plots | result$input | Viewing plots | Exporting results | Advanced usage | Running engines separately | Changing the fgsea rank metric | Adjusting significance thresholds | Filtering by annotation confidence rank | Interpreting results | KS vs fgsea — which to trust? | The DirectionalScore | The pi-value rank metric | Session info

The data | A chart without annotations | Calling out a key data point | Showing the change between two points | Format options | Putting it all together | Multiple change annotations | Time series | Customization | Colors | Arrow styling | Label styling | Nudge | Common mistakes | Character columns need factor() | Date-like strings need as.Date() | Use colour, not color | size controls text, not the label box | Real-world example: NBA Finals scoring breakdown | What ggmemo doesn't do

Summary | Description | Getting ready | Preparing the Bias Layer | Function Arguments | Example: Mixed-Direction Composite Bias | Applying Bias to Predictions | Function Arguments | Example: Comparing Applied Biases | Three-Dimensional Example | Save and export

Summary | Description | Getting ready | Loading example data | Building and visualizing ellipsoid niches | Background | Creating a basic ellipsoid | Adjusting ellipsoid covariance | Comparing multiple species niches | Save and import | Working in more than 2 dimensions

Summary | Description | Getting ready | Part 1: Virtual Data | Basic generation | Visualizing virtual data in 2D | Three-dimensional virtual example | Part 2: Spatially-Explicit Occurrence Data | Basic generation in 2D | Effect of the sampling argument | Effect of the method argument | Effect of the strict argument | Three-Dimensional Example | Part 3: Biased Occurrence Data | Basic biased generation in 2D | Effect of the biased strict argument | Three-Dimensional Biased Example | Save and export

Summary | Description | Getting ready | Loading example data | Using predict() | Basic predictions to a data frame | Basic predictions to a SpatRaster | Understanding the output | Mahalanobis distance and the ellipsoid | From distance to suitability: the chi-square and MVN connection | Additional function arguments | All four outputs at once | The role of the confidence level and truncation | Effect of the confidence level on predictions | Visualizing predictions in environmental space | Mahalanobis distance in E-space | Suitability in E-space | Truncated predictions in E-space | Binary suitable vs. unsuitable environments | Visualizing predictions in geographic space | Mahalanobis distance map | Suitability map | Truncated suitability map | Binary potential distribution map | Three-dimensional example | Predicting with virtual data | Two-dimensional virtual data | Three-dimensional virtual data | Save and import

Summary | Description | Getting ready | Loading example data | Simulating random communities | Effect of background density | Effect of proportion arguments | Simulating nested communities | Effect of proportion argument | Effect of bias argument | Simulating niche conservatism in communities | Predictions for communities | Predict to data frames | Predict to SpatRaster | Truncating predictions | Simple community outcomes | Save and import

Summary | Description | Getting ready | Loading example data | plot_ellipsoid() | Ellipsoid boundary only | Background points | Prediction colored by a continuous variable | Truncated predictions and grey outside region | Reversed palette and transparency | Subsampling large datasets | Fixed axis limits for comparing ellipsoids | add_data() and add_ellipsoid() | Layering background, occurrences, and boundary | Coloring occurrence points by a continuous variable | plot_ellipsoid_pairs() | Pairs with background | Pairs with predictions

Reading Data | Threshold selection | Estimation of EVI, scale and T-year levels

Reading Data | Threshold selection | Estimation of spline ML model | High Quantiles

Model Overview | Disease natural history scenarios | Transmission scenarios with interventions | Presymptomatic transmission scenarios with interventions | Transmission in isolation | Model parameters | Further information

Introduction | Model overview | Specifying model components | Offspring distributions | Delay distributions | Event probabilities | Intervention options | Simulation controls | Running outbreak simulations | Visualising results | Summarising outcomes | Simplified COVID-19 contact tracing effectiveness analysis | Define epidemiological parameters | Run the simulation | {ringbp} Use Cases | References

Set up scenario parameter space | Parameter sweep across scenarios | Storing simulations with scenarios | Running scenarios in parallel

The World Economic Outlook | Migrating from imfr: where did IFS go? | Discovering data | Named database wrappers | API keys

Package Description and Functionalities | Installation | Simulating microbiome data | Two ways to obtain parameters for data simulation using MixGaussSim | Dataset | Pre-filtering low abundant taxa | Fold change and Dispersion Estimation | Modeling the distribution of log mean counts | Modelling the distribution of log fold change estimates | Modelling the dispersion estimates | Simulate count microbiome data | Simulate log mean count and log fold change | Simulate counts from the negative binomial distribution | Comparing the distributions between simulated count d of mean count and fold change from simulation with actual data | Estimating Statistical Power for Individual Taxa | Estimate p-values associated with simulated fold changes | Fitting the Generalized Additive Model (GAM) | Contour plot showing power for various combinations of mean abundance and fold change | Sample size calculation | Simulate count data for various sample sizes | Estimate p-values associated to fold changes for each taxa for simulated data per sample size | Fit Generalized Additive Model (GAM) for power estimation | Estimate sample size for a given statistical power, fold change and mean abundance | Appendix | Data description and parameter estimates from actual microbiome datasets | Acknowledgments | References

Installation | Running the app | Plots | Conducting an analysis | Setup | Summarising the data | Frequentist network meta-analysis | Bayesian network meta-analysis | Nodesplitting models | Bayesian network meta-regression with a covariate | Bayesian baseline-risk network meta-regression | Exporting to CINeMA | App data

1. Load both records | 2. Plot the wax records | 3. Claim levels used by assess_claim() | 4. Local effective slope | 5. Invert wax values to precipitation values | 6. Detection threshold and posterior probability of change | 7. Assess a claim | 7b. Magnitude path: rejecting vegetation as the sole explanation | 8. Plot the reconstructions | Takeaway | Notes

Overview | Shared Item Bank | 1. Content Balancing | Concept | Setup | Running a session with content balancing | Inspecting the domain distribution | apply_content_balancing() directly | 2. Exposure Control | 2a. Sympson-Hetter | 2b. Randomesque | Using exposure control functions directly | 3. Shadow CAT | Example 1 -- Greedy shadow (no external solver) | Example 2 -- Content and overlap constraints (no solver) | Example 3 -- LP-based shadow test with lpSolve | 4. Combining Features | Summary | References

Overview | 1. Defining the Item Bank | 2. Running a CD-CAT Session | 3. Extracting Results | 4. Item Selection Criteria | 5. Supported Models | 6. Estimation Methods

Overview | Experimental factors | Evaluation metrics | Setup | Simulation | Results | Full results table | PCCR and ATL by criterion | PCCR and ATL by stopping rule | PCCR heatmap | Summary

Overview | Basic workflow | Step by step | Configuring an LLM provider | Relationship to confoundvis | References

JAGS vs. NIMBLE vs. Stan | Parallel processing and number of chains | References

Load libraries | Introduction | Data layout | Running a first analysis | Running the analysis with JAGS | Going further | References

Benjamin Gompertz and the Gompertz distribution | The Gompertz distribution with NIMBLE and JAGS | Mathematical notation | References

References

Glossary of main functions | Quick-start checklist | Wide input (strict and explicit) | Comparison with generic workflows

Overview | Measuring dependence with distance correlation | Robust transformations | Independent component analysis with dcorICA() | Serial dependence diagnostics | Normalized HSIC | Overview over Functions | References

Introduction | Installation | Quick Start Example | Load Demo Data | Run Analysis | View Results | Understanding the Output | Advanced Features | Choosing a Weighting Method | Outcome Model | Cross-fitting | Bootstrap Options | Adjust Bootstrap Sample Size | Fast Point Estimates (No Bootstrap) | Parallel Bootstrap | Diagnostic Warnings | Low ESS Warning | Extreme Weights Warning | Positivity Violations | Common Workflows | Standard Analysis Workflow | Method Comparison Workflow | Sensitivity Analysis Workflow | Working with Weights Only | Getting Help

What this tutorial shows | Main idea | 1. Load packages and example data | 2. Introduce the learning and prediction wrappers | Learning step: emulator_learn() | Prediction step: emulator_predict() | 3. Fit the emulator and predict new PDE outputs | What is returned? | 4. Visualize the true PDE solution | 5. Compare FFBS emulation with the PDE solution | 6. Check predictive uncertainty | Practical tips | Summary | References

Package Structure | Regularized Estimator | Quick Start | Installation | Synthetic Data | Example | Real Dataset | Gene Network Construction | Variable Screening | Training Phase | Pathway Analysis | Testing Phase | Session Info

Overview | Data | Fitting the Model with PMLE | Results | Dependence between relapse and death | Regression coefficients for relapse (non-terminal event) | Regression coefficients for death (terminal event) | Comparison: PMLE vs Simultaneous MLE | Reference

Overview | The Texas data | Method 1: bin_means() with known cutpoints | Method 2: bin_means() with cutpoints estimated from data | Method 3: HETOP MLE on a 50-district subsample | Method 4: HETOP Bayes on the full dataset | Summary | References

Quick Start | Background | Methods at a Glance | The aa_init() Function | Supplying Your Own Initialization | Comparing Initializer Biases | Increasing the Number of Archetypes | When Initialization Matters | Nonlinear Geometries | Conclusions and Recommendations | Initialization Caveats | Method Reference | random | dirichlet | Furthest First ("furthest_first") | k-means++ ("kmeans_pp") | Furthest Sum ("furthest_sum") | Batched Coreset-Style Initialization | AA++ ("aa_pp") | Batched AA++ | Hull-outmost ("hull_outmost") | Session Information | References

What is Archetypal Analysis? | Relationship to other methods | The yaap package | A first example: the toy dataset | The archetypes result object | Visualizing the fit | Archetype positions in feature space | Working with "real" data: Fisher's iris | Naming archetypes from their defining observations | Archetype feature profiles | Sample compositions | Predicting reconstructions and compositions for new observations | Practical considerations | Choosing K | Scaling | Multiple restarts | Convergence | Variants of Canonical AA and their implementation in yaap | Relaxing the convex hull constraint with delta | Robust fitting | Missing data | Algorithm details | Algorithmic approaches: NNLS vs PGD | PGD Algorithm | Key arguments | Loss tracking | Conclusion | Limitations and challenges | Summary | Session info | References

One simplex, several geometries | Metric Gaussian AA | Recipe: known measurement-error metric | Functional AA | Kernel AA | Probabilistic AA | Recipe: binary responses | Recipe: multinomial counts | Directional AA | Practical checklist | References

Overview | Using Archetypes in Recipes | Comparing step_archetypes() with step_pca() | Machine Learning Note | Declaring Tunable Parameters

title: "Spatial Queries"output: rmarkdown::html_vignettevignette: >%\VignetteIndexEntry{Introduction to arcpullr}%\VignetteEncoding{UTF-8}%\VignetteEngine{knitr::rmarkdown}editor_options:markdown:wrap: 72 | URL's for examples | get watershed layer for Cook Creek | get_layer_by_point | get_layer_by_polygon | get_layer_by_envelope | Combining Spatial and SQL Queries | Spatial Relationship | Lookup Tables | The valid_sp_rel Function

Introduction | Installation | Example 1: Real Data (mpdta) | Example 2: Simulation --- Homogeneous Effects (TWFE Should Win) | Example 3: Simulation --- Substantial Heterogeneous Effects (ETWFE Should Win) | Example 4: Simulation --- Very Mild Heterogeneity with Small Sample | Monte Carlo Validation | Appendix | Cochran's Q Test | The Heterogeneity Fraction ($I^2$) | Empirical Bayes Shrinkage | Confidence Intervals on the Event Study Plot

Introduction | Methodology | Unsupervised models | Preparing the data | Semi-supervised models | Encoding partial supervision | Fitting the models | Additional details | The $\Gamma$ parameter in possibilistic models | Retrieving intermediate results

Introduction | What is mc2d? | What is Two-Dimensional Monte-Carlo Simulation (briefly)? | A basic example | One-Dimensional Monte-Carlo Simulation | Two-Dimensional Monte-Carlo Simulation | Basic Principles and Functions | Preliminary Step | The mcnode Object | mcnode Object Structure | The mcstoc function | The mcdata function | Operations on an mcnode | The mcprobtree function | Other functions for constructing an mcnode | Specifying a correlation between mcnodes | The mc Object | The mc function | The mcmodel and evalmcmod functions | The mcmodelcut and evalmccut functions | Analysing an mc Object | The summary function | The hist function | The mcratio function | Other Functions and mc Objects | Multivariate Nodes | Multivariate Nodes for Multivariate Distributions | Multivariate Nodes as a Third Dimension for Multiple Options | Multivariate Nodes as a Third Dimension for Multiple Contaminants | Another Example: A QRA of Waterborne Cryptosporidiosis in France | Tap Water Consumption Model | The Dose-Response Model | The Model | As a Conclusion

Including Variability | Specifying Variability Distributions | Initial Contamination | Growth Parameters | Time-Temperature Profiles | Serving Size | Applying the Model | Final Estimate | Including (a Part of the) Uncertainty | Specifying Uncertainty | Prevalence

Overview | Installation | Dependencies | API Requirements | Launching the App | Interface Overview | Sidebar | Main Panel Tabs | Available Models | Elicitation Models | Stan Code Generation Models | Supported Distribution Families | Next Steps

Setup | Step 1 — Define the Clinical Context | Step 2 — Specify the Model and Parameters | Step 3 — Configure Experts | Step 4 — Elicit Priors | Step 5 — Review Expert Responses | Generating Stan Code from an Expert Response | Step 6 — Inspect the Prior Summary | Step 7 — Pool Expert Opinions | Step 8 (Optional) — Delphi Iterative Elicitation | How it works | Running a Delphi session | Exporting Results | Stan Model | Downstream Validation | Troubleshooting

Description | Installation | Use

1 Data | 2 Quick start: tie-oriented model | 3 Quick start: actor-oriented model | 4 Modeling choices | 4.1 Timing: interval vs. ordinal | 4.2 Directionality | 4.3 Risk set variations | Full risk set (default) | Active risk set | Manual risk set | 4.4 Typed events | consider_type = "ignore" | consider_type = "separate" | consider_type = "interact" | Estimation with typed events | 4.5 Endogenous effects, exogenous effects, and scaling | Endogenous effects | Exogenous effects | Interactions between endogenous and exogenous effects | 4.6 Memory | 4.7 Case-control sampling | 5 Estimation | 5.1 Maximum likelihood estimation | 5.2 Bayesian estimation with HMC | 6 Diagnostics | 6.1 Tie model diagnostics | Waiting-time diagnostics (plot 1) | Schoenfeld residuals (plot 2) | Recall (plot 3) | 6.2 Actor model diagnostics | 6.3 HMC diagnostics | 7 Summary

Overview | 1. Load DICErClust | 2. Download the UCI Heart Failure dataset | 3. Feature engineering | 4. Stratified train / test split | 5. Serialise data in DICErClust format | 6. Configure DICEr | Training stages explained | 7. Train the model | 8. Load the best checkpoint | 9. Evaluate cluster quality | Dynamic cluster labelling | Cluster outcome summary | AUC | Chi-squared test | 10. Figures | Figure 1 — Cluster outcome bar chart | Figure 2 — ROC curve | Summary of results | References

What is DICErClust? | Installation | Data format | Quick start | Loading results | Hyperparameters at a glance | Output directory structure | Full worked example

Connecting to DataRobot | Data | Lift Chart | ROC Curve Data | Threshold operations | Word Cloud

Overview | Example datasets | 1. Reading dendrometer and climate data | read.dendrometer() | read.climate() | 2. Looking for jumps or gaps in dendrometer data | reso_dm() | jump.locator() and i.jump.locator() | dm.na.interpolation() and its plot methods | 3. Resample and truncate the dataset | dendro.resample() | dendro.truncate() | 4. Smoothing | smooth_dm() | 5. Daily approach and daily climate functions | daily.data() and plot.daily_output() | dm_daily_clim() and dm_join_daily_clim() | dm_add_climate() for daily outputs | 6. Stem-cycle approach and associated functions | phase.sc() and plot.SC_output() | dm_subdaily_clim(), dm_join_subdaily_clim(), dm_join_phase_clim(), and dm_add_climate() | 7. Zero-growth approach and associated functions | phase.zg(), twd.maxima(), and plot.ZG_output() | Climate joins and climate-phase plots | 8. Superposed epoch and associated functions | dm_event_times(), dm_epoch_extract(), dm_epoch_test(), and method dispatch | 9. Event-based climate functions | 10. Growth fit and evaluation | dm.fit.gompertz() | dm.growth.fit(), plot.dm_growth_fit(), and print/summary methods | dm.growth.fit.double() and dm.growth.evaluate() | 11. Detrending and associated functions | dm_standardize() and plot.dm_standardized() | 12. Moving correlations and associated functions | 13. Harsh climate using clim.twd() and associated functions | 14. Network interpolation | network.interpolation() and plot.network_interpolation() | 15. Wavelet analysis and associated functions | dm_wavelet(), summary.dm_wavelet(), and plot.dm_wavelet() | dm_wavelet_reconstruct() and its methods | dm_wavelet_coherence() and plot.dm_wavelet_coherence() | Closing note | For suggestions, comments and questions please contact : [email protected]

What's new in dendRoAnalyst 0.1.6 | 1. Core phase engines are now more modular | 2. Dedicated plotting methods for phase outputs | 3. Expanded data import, checking, and quality control | 4. New and upgraded gap-handling tools | 5. Data conditioning tools are broader and clearer | 6. Daily summaries are now easier to compute and inspect | 7. Climate joining helpers support both daily and subdaily workflows | 8. Event-based phase and climate tools were added | 9. Growth modelling is more flexible and easier to evaluate | 10. Detrending is now better linked to growth fitting | 11. Superposed epoch analysis now has a fuller toolkit | 12. Wavelet tools were added for time-frequency analysis | 13. Moving climate–growth correlations were upgraded | 14. Harsh-climate analysis is more complete | 15. Package design now emphasizes compute-first, plot-second workflows | Heads-up on namespaces | In short

Overview | 1. Unconstrained parametrisation | 2. Model fitting | 3. Admissible interval and boundary conditions | 4. Optimal cutoff | 5. Local identifiability diagnostic | 6. Asymptotic variance and Wald confidence interval | 7. ROC curve and AUC | 8. Reproducing Table 3 (Scenario SN1, n = 200)

Introduction | Data | One Learner Library For All Causes | One Learner Library Per Cause

Methods | Implementation overview | Method choice | Implementation details

Introduction | Workflow | Examples for case a): We already know which method to apply | Examples for case b): We are looking for a method to apply

Introduction | Example Dataset | Fit Initial Cox Model | Model-Free Tipping Point Analysis | Step-by-step walk through of the analyses using model-free imputation | Visualize Pooled Kaplan--Meier Curves | Visualize Tipping Point Graph | Summarize tipping point via Analysis Results Dataset (ARD) | Assess plausibility | Model-Based Tipping Point Analysis | Summary via Analysis Results Dataset (ARD) | Jump-to-Reference (J2R) as a Special Case of Model-based Imputation | Inspect the data | Fit the original Cox model | Run model-based imputation | J2R Results | References

Getting started | 1. Overview UME data workflow | All of these tasks can be executed in just two steps: | Formula assignment and calculation of evaluation parameters | Formula filtering (subsetting) and normalization | Alternatively, the workflow can be performed in single steps: | 2. Visualization and statistics | 3. Re-calibration of peaklists | 4. UME core data objects | Mass Peak List | Isotopic masses | Molecular formula library | Using External UME Formula Libraries | Create your own molecular formula library | Molecular formula data | 5. What else can you do with ume? | Calculate standard parameters | Converting molecular formulas to a table and vice versa. | Isotopes | Create isotope formulas for a parent formula | Create isotope pattern for a molecular formula | 6. Package content and documentation | Which version is installed and loaded? | What is new? | 7. UME installation

General | Usage | Arguments | Dating Considerations | Output | Output Functions | Negative Correlations? | Model | Iterative Validity Estimation | Example | Bootstrapping | References

Inverse probability weighted estimators | Plotting survival and hazard estimates | Assumption checking and model diagnostics | Testing quasi-independent truncation with covariates | Sensitivity analysis for positivity violations | Stratified Cox models | Time-varying covariates

Doubly truncated data | Nonparametric maximum likelihood estimation | Testing for event time differences across multiple groups | Assumptions required for the NPMLE | Testing for quasi-independent truncation | Testing for ignorable sampling bias

Abstract | Interpreting the magnitude of agreement coeeficients | References:

Abstract | Computing Agreement Coefficients | Computing agreement Coefficients from Contingency tables | Computing agreement coefficients from the distribution of raters by subject & category | Computing agreement coefficients from raw ratings | References:

Abstract | The different weights | Weighted Agreement Coefficients | Weighting ratings from a contingency table | Weighting for a dataset of raw ratings | Weighting when input data is the distribution of raters by subject and rating | References:

Introduction | The selectSpecies( ) function takes the following arguments: | The plotProbs() function takes the following arguments: | Part 1. Examples using one trait with known structure (evenly spaced integers) | Example 1: Derive species assemblage with the following trait profile: | Example 2: Derive species assemblage with the following trait profile: | Example 3: Derive species assemblage with the following trait profile: | Example 4: Derive species assemblage with the following trait profile: | Part 2. Examples using two traits with known structure (evenly spaced integers) | Example 5: Derive species assemblage with the following trait profile: | Example 6: Derive species assemblage with the following trait profile: | Part 3. An example using a real dataset | Example 7: Derive species assemblage for a California serpentine grassland with the following trait profile:

Installation | References | Use of RoME function | Use of RoMEcc function | Graphical interface

1. Introduction | 2. Data Generation | 3. Probability Sampling Designs | 3.1 Simple Random Sampling | 3.2 Stratified Sampling | 3.3 Cluster Sampling | 3.4 Probability Proportional to Size (PPS) | 4. Non-Probability Sampling | 5. Estimation Methods | 5.1 Mean and Total Estimation | 5.2 Ratio and Regression Estimators | 5.3 Horvitz–Thompson Estimator | 5.4 Stratified Estimator | 6. NRM-Specific Applications | 6.1 Biomass and Carbon Estimation | 6.2 Soil Loss Estimation | 7. Sampling Efficiency | 8. Spatial Sampling (Optional) | 9. Recommended Workflow | 10. Conclusion | References

Introduction | Computation method | Definition of a mathematical excursion | Toy examples | A study case | Optimal excursion, the local score | Sub optimal excursions | With a reverse lecture of the protein | What about the excursion realising the local score

Citation | Introduction | In brief | Case of I.I.D. integer score sequence | Generating an I.I.D. score sequence for this example | Graphical representation via the Lindley process | Finding the maximal sum subsequence and all strictly positive sum subsequences | Calculating p-value of local score | Case of markov integer score sequence | Generating a markov score sequence for this example | Case of various dependent structure sequence via Monte-Carlo approach | Generating a complex dependent structure score sequence for this example | Calculating p-value of local score by Monte Carlo simulation | Case of $i$th excursion (sequential order) | Calculating $p$-value of $i$th excursion | Local Score computation methods | A first example: function ``localScoreC()'' | Example with real scores | Example of alphabetical sequence associated to a scoring function | $p$-Value computation methods | Simulating computation: functions "monteCarlo()" | A mixed method: functions "karlinMonteCarlo()" | Exact method for integer scores: function ``daudin()'' | How to use the exact method for real scores | Approximate method of Karlin et al.: function ``karlin()'' | An improved approximate method: function ``mcc()'' | An automatic method: function ``automatic_analysis()'' | Markovian model of the sequence : function exact_mc() | Other Functions | Lindley Process: to visualize optimal and suboptimal segments | Record times: gives the record times of a sequence | Score Loading Function | Empirical distribution: function "scoreSequences2probabilityVector()" | Case study | Medium sequence | Local score computation and parameter model setings | Parameter model setings | Exact method | Approximated method | Improved approximation | Monte Carlo | Result and time computation comparison | Short sequence | Results | Large sequence | Several sequences | A larger example with a SCOP data base | File Formats | Sequence Files | Score Files | Transition Matrix Files

What is an hl-rhotrix? | Creating hl-rhotrices | Dimension 2 | Dimension 4 | Dimension 6 | Determinant | Adjoint | Inverse | Eigenvalues | Full summary | Rhomboidal layout (ggplot2) | References

Description | How to install | Example 1 | Example 2 | Boxplots | Example 3 | Example 4 | Approximation of the true causal measure | Plot | Computation of the Confidence Interval for Win Ratio | References

Introduction | Key Concepts | Installation | The Example Dataset | Step 1: Validate Your Data | Step 2: Define Variable Configuration | Verify scoring curves | Step 3: Score All Variables | Step 4: Select the Minimum Data Set (MDS) | Step 5: Compute SQI Using All Six Methods | Linear Scoring | Regression-Based | PCA-Based | Fuzzy Logic | Entropy Weighting | TOPSIS | Compare All Methods | Step 6: Visualisation | Score Heatmap | SQI Bar Chart | Radar Profile | PCA Biplot | Step 7: Statistical Analysis | ANOVA | Sensitivity Analysis | Sensitivity Tornado Chart | Recommended Workflow Diagram | References

Prerequisites | Recipe 1: Random slope for a lagged predictor | Recipe 2: Bivariate lag sketch (two variables) | Recipe 3: Multivariate outcomes with mvbind (sketch) | See also

Decision axes | Comparison table | Short pointers to other vignettes | See also

When to use ild_ctsem() | Minimal workflow | Diagnostics and plots | Guardrails and reporting | Notes

See also

Simulate and prepare | Inspect | Within-person centering and lags | Fit a model | Diagnostics and plots | MSM-style weights (IPTW and IPCW) — optional | Reproducibility

Estimands: population, partial pooling, and no pooling | ild_heterogeneity() | Diagnostics bundle and plots | Stratified descriptive comparison | See also

Motivation | What ild_kfas() assumes today | irregular_time and guardrails | Workflow sketch | Where to go next

Why missingness matters in intensive longitudinal data | Types of missingness (useful labels) | Descriptive profiling: ild_missing_pattern() and heatmaps | Person-level compliance: ild_missing_compliance() | When to use ild_missing_model() and ild_missing_bias() | Complete-case vs mixed models (careful wording) | Cohort-level and hazard summaries | One entry point: ild_missingness_report() | MNAR as sensitivity (no single fix) | IPW and causal tools as one sensitivity route | Other templates (not evaluated here) | What tidyILD does not do (and where to look) | See also

Why this vignette exists | Identification assumptions | Estimand-first + history-builder workflow (v1) | Recovery harness | Inference caveats and strict mode | Notes on v1 scope

1. Fit model(s) | 2. Tidy fixed-effects table | 3. Fitted vs observed | 4. Residual diagnostics: ACF and Q-Q | With AR1 (nlme) | Time-varying effects (TVEM)

Simulation size and precision | What ild_simulate() encodes | Fixed-effect recovery with ild_power(..., return_sims = TRUE) | From recovery to power | Variance components (illustrative snapshot) | AR(1) in the DGP vs residual correlation in the fit | Bayesian and state-space extensions | Cross-backend validation harness (optional) | Limitations and scope | See also

Contract: what tidyILD owns vs what it does not | Decision table | Handoff pattern: export after prepare, center, and lag | Code stubs (not evaluated) | dynamite (multivariate dynamic models) | PGEE (penalized GEE / high-dimensional longitudinal) | lavaan / blavaan (DSEM) | See also

What is a state-space model? | When use this instead of mixed-model residual correlation? | Filtered vs smoothed states | Minimal example | What the backend does not yet do | See also

Three axes before you fit anything | Decision flow (conceptual) | Feature map | Minimal examples | Further reading

Ingesting a tsibble with ild_prepare() | What provenance is kept | How to inspect | Round-trip with ild_as_tsibble() | Limitations and policy

Role of visual inspection | Map: question → function → bundle section (if any) | Example: spaghetti, predicted trajectories, facet by cluster | Recipe: facet panels without a dedicated helper | Partial effects for _wp and _bp (external packages) | See also

Within-between decomposition | Irregular spacing and lags | Spacing classification

--- 1. SETUP: LOAD LIBRARIES --- | ------------------------------------------------------------------- | --- 2. LOAD AND PREPARE TRAINING DATA --- | --- 3. PREPROCESS THE TEXT --- | Use the pre_process() function from our package to clean the raw text. | This step is done externally to the main pipeline, allowing you to reuse | the same cleaned text for multiple different models or analyses in the future. | --- 4. RUN THE MAIN TRAINING PIPELINE --- | This is the core of the package. We call the main pipeline() function | to handle the train/test split, vectorization, model training, and evaluation. | =================================================================== | --- 5. EVALUATE THE RESULTS | Get the AUC, ROC and Accuracy at Decile Threshold | --- 6. PREDICTION ON NEW, UNSEEN DATA --- | The training is complete. The 'result' object now contains our trained | model and all the necessary "artifacts" for prediction.

Introduction | Financial appraisal | Benefit-cost ratio | Net present value | Internal rate of return | Payback period | Marginal rate of return (CIMMYT method) | Modified BCR | Soil loss economic valuation | USLE-based soil loss cost | Nutrient replacement cost | Water resource valuation | Social indicators | Risk analysis | Sensitivity analysis | Switching value | Monte Carlo simulation | Benchmark datasets | State-wise BCR benchmarks | USLE parameters for Indian soils | Rainfall erosivity | SWC unit cost norms (PMKSY-WDC 2015) | Full pipeline and report | References

Recommended DIA-NN export settings | Runnable toy example (no DIA-NN files required) | Headline stability at 1% | Tail support and stability versus threshold | Local boundary support | Threshold elasticity (list sensitivity to changing alpha) | Equal-chance plausibility by q-band | Real DIA-NN parquet workflow | Interpretation notes

Runnable toy example (no external files required) | Headline stability at 1% | Tail support and stability versus threshold | Local boundary support and elasticity | Equal-chance plausibility by q-band | Application to Spectronaut

Runnable toy example (no external files required) | Headline stability at 1% | Tail support and stability versus threshold | Local boundary support and elasticity | Equal-chance plausibility by q-band | Real mzIdentML workflow (.mzid) | Optional: pseudo-p-values from the decoy score tail | Interpretation notes / common pitfalls

Runnable toy example (no external files required) | Headline stability at 1% | Decoy tail support and stability proxy | Local boundary support and elasticity | Equal-chance plausibility by q-band | Real MaxQuant workflow (msms.txt) | Optional: other way to get p-value / pseudo-p-value diagnostics from MaxQuant scores | Interpretation notes

Runnable toy example (no external files required) | Headline stability at 1% | Decoy tail support and stability proxy | Local boundary support and elasticity | Equal-chance plausibility by q-band (internal check) | PEP reliability and expected errors (ΣPEP) | Real mokapot workflow (targets + decoys text exports) | Interpretation notes

Introduction | Main function of the package | References

Overview | Typical workflow | Example dataset | Computing OJIP parameters | Key parameters calculated | Multivariate visualization | Heatmaps | 3D plots | Biolyzer export workflow | FluorPen workflow | Validation resources | Shiny interface | Summary | References

What is climatehealth? | Installation | From CRAN | From GitHub (latest development version) | Optional dependencies | Package workflow | Your first analysis: temperature and mortality | Adding covariates | Pooling across regions with meta-analysis | The six indicators | Air pollution | Wildfires | Mental health (suicides and heat) | Water-borne diseases (diarrhoea) | Vector-borne diseases (malaria) | Descriptive statistics | Saving outputs | Error handling | Next steps

Overview | Object flow | End-to-end workflow example (2 H3 resolutions) | Required input structure | Assumptions | The strata classification | Why multiscale feature evaluation is needed | Summary

1. Introduction | 2. Getting started | 2.1. Installation | 2.2. Load required packages and data | 2.3. Prepare Variables and Function parameters | 2.4. Run the main algorithm | 2.5. Other results | 2.5.1. Naive approach | 2.5.2. Oracular results | 3. Play with results | Appendix. Understanding the Algorithm Internally | Preliminary Step: Data Checks and Cross-Fitting Folds | Step 1: Estimate and Save Nuisance Parameters | Step 2: Policy optimization

plotrr: Functions for making visual exploratory data analysis with nested data easier. | Functions that illustrate the relationship between variables within groups/units | bivarplots | dotplots | violinplots | Functions that evaluate correlations between measures within groups/units | Other visualization functions | Other functions | References

Introduction | Setup | Step 1: Data Preparation | Step 2: Genomic Feature Annotation | Step 3: Integration Site Analysis | Common Integration Sites (CIS) | Chromosome Distribution | Gene Set Overlap | Step 4: Longitudinal Analysis (PMD) | Step 5: Clonal Dominance Analysis | Step 6: Visualization | Treemap | Region Counts | Ideogram

Introduction | Example 1: Binary Data | Data | Full Workflow Call | Load and Inspect Precomputed Results | Parallel Analysis | Bootstrap Stability Diagnostics | ALSI Computation | Category Projections | Example 2: Ordinal Data | Ordinal ALSI Computation | Example 3: Continuous Data | Continuous ALSI Computation and Domain Contributions | Comparison with SEPA Plane-Wise Summaries | Session Information

Overview | 1. Built-in datasets | 2. Data validation | 3. Tillage analysis | 3.1 Summarise tillage operations | 3.2 Tillage depth trend | 3.3 Compaction detection | 4. Erosion analysis | 4.1 Track erosion depth | 4.2 Estimate soil loss | 4.3 Compare fields | 5. Visualisation | 5.1 Tillage depth timeline | 5.2 Erosion depth trend | 5.3 Organic matter trend | 5.4 Tillage vs erosion comparison | 6. Full analysis pipeline | References

1. Introduction to the n-TARP clustering method | 1.1 Installing the package and running the core n-TARP function | 1.1 Getting the optimal solution from the nTARP function | 1.2 Using the results of nTARP as a classifier | 1.3 Checking the clusterability of the dataset | 1.4 The last outputs of the nTARP function | 2. Extending nTARP to form multiple clusters

Introduction | What is I30? | What is EI30? | Basic Usage | Loading the Package | Example Data | Calculate I30 | Calculate EI30 | Working with Different Kinetic Energy Equations | Processing Multiple Storm Events | Data Validation | Visualization | Rainfall Pattern | Intensity Profile | Advanced Usage | Using Explicit Time Intervals | Custom Event Identification | Typical Workflow | References

Key Terms and Definitions | Rainfall Intensity (I) | Maximum 30-minute Intensity (I30) | Kinetic Energy (E) | Erosivity Index (EI30) | Erosivity | Breakpoint Rainfall Data | USLE — Universal Soil Loss Equation | RUSLE — Revised Universal Soil Loss Equation | Hydrological Analysis | References

Load the package and example data | Convert to a binary data frame | Observed variance components | Statistical Power analysis | Bootstrap confidence intervals | Resample observations | Iteration variance components | Extract confidence intervals | Bias and accelerated corrected confidence intervals | Jackknife confidence intervals | Plotting confidence intervals | Bar plot | Box plot

Load the package and example data | Convert to a binary data frame | Observed variance components | Statistical Power analysis | Bootstrap confidence intervals | Resample observations | Iteration variance components | Extract confidence intervals | Bias and accelerated corrected confidence intervals | Jackknife confidence intervals | Plotting confidence intervals | Bar plot | Box plot

Load the package and example data | Convert to a binary data frame | Observed variance components | Statistical Power analysis | Bootstrap confidence intervals | Resample observations | Iteration variance components | Extract confidence intervals | Bias and accelerated corrected confidence intervals | Jackknife confidence intervals | Plotting confidence intervals | Bar plot | Box plot

Overview | Quick Start | 1. Define the Multistate Structure | 2. Simulate Data | 3. Prepare Multistate Data | 4. Aalen-Johansen Nonparametric Baseline | 5. Fit Random Forest Model | 6. Model Summary | 7. Feature Importance | 8. Predict for New Patients | 9. Diagnostics | 10. Transition Diagram | Methodology | Product-Integral Framework | Aalen-Johansen Estimator (Nonparametric Baseline) | Random Forest Multistate Approach | Diagnostics | References

Introduction | Data Aquisition | Quick temperature overview | Heat Index computation | Visualisation

1. Prepare data | 2. Center and lag | 3. Fit model | 4. Run diagnostics | 5. Inspect ild_history() | 6. Generate ild_methods() | 7. Run ild_report() | 8. Export provenance | 9. Compare two pipelines

Promoting dominant function patterns | Demoting non-dominant function patterns | Robust to change in low expected count | Lung transplant surgery type and outcome | A contingency table showing similar testing results | How to cite this document | References

Multi-objective Markov decision processes | Transition probabilities and rewards | Decision rules and policies | Value functions and preference patterns | An example | References

TLDR | Example report preview | Overview | Bundled example files | Read raw header lines | Parse a VCF header | Inspect parsed content | Inference from the header | Structural variant example | Generate an HTML report | Bundled example HTML report | Summary

VectorSurv | Data Retrieval | Write Data to file | Sample Data | Data Processing | Subsetting | Filtering and subsetting in 'dplyr' | Grouping and Summarising | Pivoting | Calculations | Abundance | Abundance Anomaly (comparison to 5 year average) | Infection Rate | Vector Index | Tables | Styling Dataframes with 'kable' | Data using 'datatables'

Contents | Loading data | Distribution of baseline p-values for continuous variables | Matches of baseline summary statistics | Matches of summary statistics in different cohorts | Comparing means between groups | Distribution of a single categorical variable | Distributions of numbers of participants in trial groups | Distributions of all categorical variables | Comparing reported and calculated p-values for categorical variables | Distributions of p-values for categorical variables | Distribution of final digits | Graph maker | Final comment

The Data | Forest Plots using gg_forest(). | Using Individual geom_ Functions. | Subgroup Analysis

Introduction | Global parameters of Ravages | Simulations based on allelic frequencies and GRR | Calculation of frequencies in each group of individuals | Simulation of genotypes | Simulations based on haplotypes | Power calculation

Introduction | Global parameters of Ravages | Example of analysis using LCT data | Defining genomic regions | Rare variant selection | Rare variant association tests | Genetic score for burden tests | CAST | WSS | Other genetic scores | Regressions | SKAT | RAVA-FIRST (RAre Variant Analysis using Functionally-InfoRmed STeps) | Data management

Introduction | Methods | Results | Discussion | Appendix: R code

Introduction | Overview | Key Features | Priority-Elastic Net | Example 1: Gaussian Family with Simulated Data | Example 2: Cox Family with Simulated Data | Example 3: Binomial Family with Simulated Glioma Data | Example 4: Multinomial Family with Simulated Data | Advanced Features | Block-wise Penalization | Handling Missing Data | Cross-Validation and Model Selection | Using the Shiny App for Threshold Optimization | Utility Functions | Extracting Coefficients | Making Predictions | Priority-Adaptive Elastic Net | Example 1: Gaussian Model | Example 2: Cox Model | Example 3: Binomial Model | Example 4: Multinomial Model | Conclusion

Introduction | TDA Built-in Visualizations of the Rips Diagram | Mercator Visualizations of the Underlying Data and Distance Matrix | Dimension Zero | Using iGraph | Community Structure | Visualizing Features | Significance | Zero-Cycles (Connected Components) | One-Cycles (Loops) | Two-Cycles (Voids)

Introduction | TDA Built-in Visualizations of the Rips Diagram | Mercator Visualizations of the Underlying Data and Distance Matrix | Dimension Zero | Using iGraph | Community Structure | Visualizing Features | Significance | Zero-Cycles (Connected Components) | One-Cycles (Loops) | Two-Cycles (Voids)

Introduction | TDA Built-in Visualizations of the Rips Diagram | Mercator Visualizations of the Underlying Data and Distance Matrix | Dimension Zero | Using iGraph | Community Structure | Visualizing Features

Overview | Data Input Format | Main Functions: Block Partitioning, Sire Imputation and Phasing of a Half-Sib Family | Auxiliary Functions | Parallel Data Analysis (para) | Visualisation | Diagnostic Tools | Pedigree Reconstruction | Imputation | Quick Guide | How to Cite the hsphase

SeqFeatR discovers feature - sequence associations | The core of SeqFeatR: Fisher's exact test | Graphical output | Input: sequences and features | Multiple comparison correction | Hints | Advanced Feature: Bayes Factor in SeqFeatR | Advanced Feature: Discovering associations between mutation tuples and features | Tartan plot: visual comparison of different associations of features and sequence position tuples

Introduction

Introduction | Loading the package | Sample data set | Preprocessing | Selecting variables and observations | Standardization of taxon names | Genus to species conversion | Data pooling | Biodiversity measures | Measures of species abundance | Total abundance | Abundance | Measures of species richness | Species richness | Margalef's index of diversity | Rygg's index of diversity | Hurlbert's $\mathrm{E}(S_n)$ | Measures of heterogeneity/evenness | Simpson's Measure of Concentration | Hurlbert's Probability of Interspecific Encounter (PIE) | Shannon's Index | Hill's Diversity Numbers | Measures of species sensitivity | AZTI Marine Biotic Index (AMBI) | Infaunal Trophic Index (ITI) | Calculating multiple biodiversity measures in one go | Advanced topics | Number of pool runs and species richness | References

Overview | Example data | Basic Archipelago plot | Using colour themes | Fully customised plot | Output files | Summary

Introduction | Study-level bias specification and prior simulation | Study-level posterior inference | Probability of exceeding a clinically or policy meaningful threshold | Evidence synthesis via posterior mixture models | Relationship to the Shiny application | References

Overview | Key Features | When to Use MV-FMR | Installation | Example: Joint Estimation of Two Exposures | Step 1: Simulate Data | Step 2: Generate Outcome | Step 3: Functional Principal Component Analysis | Step 4: Joint Multivariable Estimation | Step 5: Visualize Time-Varying Effects | Step 6: Extract Coefficients | Step 7: Performance Metrics | Comparison: Joint vs. Separate Estimation | Performance Comparison | Instrument Strength Diagnostics | Binary Outcomes | Next Steps | Learn More | Citation | Session Info

Overview | When to Use U-FMR | Installation | Example: Single Exposure Analysis | Step 1: Simulate Data | Step 2: Generate Outcome | Step 3: FPCA for Single Exposure | Step 4: Univariable Estimation | Step 5: Visualize Time-Varying Effect | Step 6: Extract Results | Step 7: Performance Metrics | Binary Outcomes | Advanced Topics | Available Effect Models | Bootstrap Inference | Two-Sample Design | Next Steps | Learn More | Citation | Session Info

License | Description | Installation | CRAN | Latest development version | Usage | A simple example | Use a real-world dataset from TCGA | Cancer-associated genes | Highlight genes of interest | Use an ExpressionSet object and add significance asterisks | Large dataset | Full map | Filtering | Filter by tree branch height | Filter by correlation threshold | Filter by noise level and hierarchy

Overview | Why Quantile-on-Quantile Regression? | The Original Application: Oil Prices and Stock Returns | Installation | Quick Start | Basic Usage | Summary Statistics | Visualization | 3D Surface Plot | Available Color Scales | Heatmaps | Contour Plot | Quantile Correlation | Detailed Example: Simulating Asymmetric Relationships | Working with Results | Extracting Results | Exporting Results | Customizing Quantile Grids | Methodology Details | The QQ Model | Estimation | Comparison with Standard Methods | OLS vs Quantile Regression vs QQ | When to Use QQ Regression | References | Session Info

Background and introduction | The network class | Using the network class | C-language API | Final comments

Goal GORIC(A) | GORIC(A) output | GORIC(A) values | GORIC(A) weights and ratios | Hypotheses sets | Interpretation output | General | One informative hypothesis | vs Complement | Example | vs Unconstrained | Multiple informative hypotheses | Complement as failsafe | Unconstrained as failsafe | No failsafe | Note: Hypothesis specification | Special cases | Equal fit | Overlapping hypotheses | Example: Subset true | Example: Non-overlapping part true | Follow-up exploratory analysis for non-overlapping part | Support for boundary | Example: $H_1$ versus its complement | Follow-up exploratory analysis for boundary | Example: $H_1$, $H_2$, and the unconstrained | Notes | Just below maxmimum fit | Equality restriction (=) | About-equality restrictions | Not highest fit | Example: Correct hypothesis | Example: Incorrect hypothesis | Note on sample size | Remarks Bayesian model selection | Example: Prior sensitivity bain | Example: Support for incorrect equality | N = 100 | N = 1000 | bain | GORIC | N = 10000

Introduction | GORIC(A) weights benchmarks | How to use benchmarks | Labelling | Use minimum effect | Sensitivity analysis | Defaults | General R code | Examples | Example 1 (ANOVA): $H_1$ vs its complement | Footnote | Example 2 (ANOVA): Overlapping hypotheses | Log-likelihood benchmarks | Example 3 (ANOVA): Border is true | Higher sample size | Example 1 (ANOVA) Ctd.

Packages | Example 1: Confirmatory Factor Analysis | Example 2: Multiple Group CFA | Measurement invariance | Hypothesis evaluation using GORICA | Example 3: Structural equation modeling (SEM) | Example 4: Multilevel SEM | Example 5: linear growth model with a time-varying covariate | Example 6: Mediation

Examples with R code | Example 1: Relationship previous experience and trust | Data preparation | Data for evSyn | Hypotheses | GORICA Evidence Synthesis | Example 2: Comparing surgical and non-surgical treatments for periodontal disease | Background Information & Data | Set 1 | Set 2 | Set 3 | Specifying sets of hypotheses in evSyn | Types of input for evSyn | using Estimates and Covariance Matrix | using GORIC(A) Values | using GORIC(A) Weights | Note | using Log-likelihood and Penalty Values

Overview | Example data set 1 | Input | Data files | Input options | Calculations and visualizations | Visualizing the sequences | EHH | EHHS | "Genome-wide" scan | Furcations and haplotype length | Example data set 2 | References

About the package | Background | Changes between versions 2.X and 3.X | Example files | Input files | R objects | Terminology | Overview | Data input | Haplotype data file | Marker information file | Loading data files: the function data2haplohh | Example 1: reading haplotype file in standard format | Example 2: reading haplotype file in transposed format (SHAPEIT2--like) | Example 3: reading haplotype file in fastPHASE output format | Example 4: reading vcf files | Example 5: reading ms output | Subset data | Computing EHH, EHHS and their "integrals" iHH and iES | Definition and computation | The (allele-specific) Extended Haplotype Homozygosity (EHH) | The integrated EHH (iHH) | The (site-specific) Extended Haplotype Homozygosity (EHHS) | The integrated EHHS (iES) | The function calc_ehh() | The function calc_ehhs() | The function scan_hh() | Computing iHS, Rsb and XP-EHH | The iHS within-population statistic | Definition | The function ihh2ihs() | The Rsb pairwise population statistic | Definition | The function ines2rsb() | The XP-EHH pairwise population statistic | Definition | The function ies2xpehh() | Delineating regions with "outliers": the function calc_candidate_regions() | Visualization of the statistics | Un-standardized iHS: the function freqbinplot() | Rsb vs. XP-EHH comparison | Distribution of standardized values: the function distribplot() | Genome wide score plots: the function manhattanplot() | Genome wide score plots: the function manhattan() of package qqman | Visualization of the haplotype structure | The function plot.haplohh() | The functions calc_furcation() and plot.furcation() | The functions calc_haplen() and plot.haplen() | Data considerations | Multi-allelic markers | Dealing with gaps | Dealing with missing data | Dealing with multiple markers at the same position | Dealing with unphased data | Dealing with unpolarized data | Differences to the program hapbin | About estimating homozygosity | References

Overview | Installation | Required files | Allele reference panels | The header-translation table | Configuration file | Step-by-step guide to run a QC | Step 1: make sure the package is installed correctly | Step 2: check R environment | Step 3: download the standard allele-frequency reference datasets | Step 4: get the header-translation table | Step 5: get the configuration file | Step 6: modify the parameters in the configuration file | Step 7: run the QC function | Test run

Introduction | Installing the Package | Analysis | Loading the Package | Input Data | Fitting Lactation Curve Models and Generating Ensembles | Available Weighting Methods | Visualizing Model Ranking | Plotting Actual vs. Predicted Daily Milk Yield | Customizing Model Selection | Evaluating Model Ranks | Customizing Model Parameters | Calculating Resilience Indicators | Imputation of missing records | Identification of milk loss events and estimation of resilience indicators

Overview | Background | Univariate Example | Using Distribution Functions | Multivariate Example

Conditional autoregressive (CAR) models | ICAR model | Proper CAR model | Disconnected graphs and isolated nodes | Scaling (Sørbye–Rue)

Overview | Step 1: Obtain road data for a city | Step 2: Clean, validate, and inspect geometries | Step 3: Write the dataset to GeoJSON (raw source) | Step 4A: Save as .rds (recommended for user projects) | Step 4B: Convert GeoJSON to .rda (recommended for R packages) | Step 5: Use the dataset in trafficCAR | Organizing multiple city datasets | Example directory layout (package development) | Example data-raw script | Using external GeoJSON files directly | Summary

Overview | Fetch by place name | Fetch by bounding box | Filtering the OSM query

Load data and build segment adjacency | Simulate an outcome and fit a CAR model | Prepare a plotting-ready fit object | Plot observed vs fitted values | Plot MCMC diagnostics | Plot spatial predictions | Tips for applied workflows

Purpose | CAR and ICAR precision matrices | A small graph example | Proper CAR | ICAR | Sampling from a sparse precision matrix | Zero-mean sampling | Nonzero mean (linear term) | Notes on ICAR sampling | A road-like graph example | Option 2: Synthetic road-like graph (fallback) | CAR/ICAR sampling on the road graph | Summary

Residual diagnostics | Moran’s I on residuals | Posterior predictive checks | Practical workflow | Limitations

Introduction | Load the package and example data | Build a road network graph | Spatial weights from the adjacency matrix | CAR and ICAR precision matrices | Sampling from a CAR precision matrix | Interactive road visualizations | Takeaways | Further directions

Space-Time Permutation example: NYC fever data | Poisson spatio-temporal example: New Mexico brain tumor data | Bernoulli purely spatial example: North Humberside leukemia and lymphoma

Overview | Description | Basic object | The cost function | The output function | Termination | Notes about optimization methods | Kelley's stagnation detection | O'Neill's factorial optimality test | Method of Spendley et al. | Method of Nelder and Mead | Box's complex algorithm | User-defined algorithm | User-defined termination | Specialized functions | fminsearch | Direct grid search | Examples | Example 1: Basic use | Example 2: Customized use | Example 3: Optimization with bound constraints | Example 4: Optimization with nonlinear inequality constraints | Example 5: Passing data to the cost function | Example 6: Direct grid search | Dependencies of fminsearch | References

Introduction | Preliminary notice | What's new? | How to obtain scaRabee | Installation and dependencies | Credits | Reporting bugs | Analysis types | Simulation | Estimation | Direct grid search | Getting started | Creation of a new analysis folder | Creation of new models in an on-going analysis | Editing of the data file | Editing of the parameter file | Editing of the model file | $ANALYSIS | $DERIVED | $IC | $SCALE | $LAGS | $ODE | $DDE | $OUTPUT | $VARIANCE | $SECONDARY | Editing of the master scaRabee script | Execution of the master scaRabee script | Scope of analysis | Design information | Solvers of differential equations | Implementation of dosing history for model defined with differential equations | Implementation of dosing history for model defined with algebraic equations | Analysis examples | Example 1: Simulation of a model defined with algebraic equations at the population level | Example 2: Simulation of inputs into a model defined with ordinary differential equations | Example 3: Simulation of a model defined with ordinary differential equations at the population level | Example 4: Simulation of a model defined with delay differential equations at the population level | Example 5: Estimation of a model defined with algebraic equations at the | Example 6: Simulation of a model defined with ordinary differential equations at the subject level | Example 7: Estimation of a model defined with algebraic equations at the subject level | Example 8: Direct grid search for a model defined with delay differential equations | Network of scaRabee functions | References

Overview | Motivating example: a clinical trial with multiple endpoints | A. one sample mean: primary endpoint | B. two sample comparison: secondary endpoint | C. binary clinical endpoint: safety outcome | D. continuous biomarker association | A unified evidential summary | Reporting multiple endpoints | Interpretation | Scope | References

Probability Weighted Iterative Generalised Least Squares for Two-level Multilevel Model and Two-level Multivariate Multilevel Model | 1. Introduction | 2. Estimation method for multilevel complex survey data | 2.1 PWIGLS estimation for two-level random coefficients models | 2.2 Estimation of fixed effects | 2.3 Estimation of Random effects | 2.4 Initial estimates | 2.5 Variances | 2.6 Residuals | 2.7 Scaled weights | 3. PWIGLS Estimation for multivariate multilevel models | 4. Functions | 5. References

Load Package | Generalized True Random Effects Model | GTRE Results

Background | Installation | System requirements | Running the code | Downloading all the PubChem we need | note that running this line of code may occupy your R session for 2-3 few hours | Building metabolite (CID) - lowest common ancestor (LCA) relationships | note that running this line of code may will take about an hour of computer time, with 8 threads, 64 GB RAM | Creating PubChem BIO metabolome database. | note that running this line of code may will take about one hour of computer time, with 8 threads, 64 GB RAM | Creating taxon-specific metabolite databases | running this line of code may will take about a minute of computer time, with 8 threads, 64 GB RAM | Meta-metabolomes | Taxon-scored metabolite database structure | Filtering your database. | Exporting your custom database | The future | Workflow in brief

Description | Installation | System requirements | Install and load | Quick start | 1) Calibrate the tuning parameters for eNAP prior | 2) Construct NAP priors | NAP | mNAP with a fixed weight of 0.5 | eNAP without direct data $D_{E,C_2}$ provided | eNAP with direct data $D_{E,C_2}$ provided | 3) Simulate operating characteristics | 4) Calculate posterior | Core functions | JAGS model specification | Tips & diagnostics | Citing | Maintainer

Introduction | Safety data set | Original Format | ADaM format | Calculate Summary Statistics | Original Data Format | ADaM data format | Convert to XML | Output | Manual Upload

About mrangr | Basic workflow | Installing the package | Input maps | Species interactions | Community initialisation | Running the simulation | Visualisation | Over space | Over time | Virtual ecologist | Summary

Motivation | Basic usage | Alternative combinations of inputs | Infeasible systems | Discussion | References

Introduction and overview | What problem does S3VS solve? | Model families supported | S3VS in one picture | Use of package functions with examples | Installation | The main function S3VS() | Example 1: Linear model | Example 2: Binary classification model | Example 3: Survival model | Advanced usage: building blocks and customization | Choosing leading variables: get_leadvars*() | Constructing leading sets: get_leadsets() | Selection within a set: VS_method*() | Aggregating selections: select_vars() | Removing variables: remove_vars() | Response updating: update_y*() | Stopping rule: looprun() | Practical guidance and troubleshooting | Choosing method_sel and method_rem | Highly correlated predictors: | Computational tips for AFT: | Reproducibility

Load packages | Prepare data | Train a recforest model | Predict on new data

Introduction | Installation (GitHub) | Load the package | Example

Overview | Example

The Name of the Game | Representations | Trying it Out | Using GMP Rational Arithmetic | Linear Programming | Redundant Row Elimination | Faces | Image and Preimage

Introduction | Maximum entropy bootstrap | Applications | Concluding remarks

Overview | Distance Metrics | Example

Introduction | Installation | Retrieving data structure from GPML file | Edges | Nodes | Groups | Anchors | Converting Pathways into igraph objects | Bundling the Process | Finding cycles

Introduction | Running the Pipeline | Running the Full Pipeline | Analyzing your results | Additional Options

Overview | Load OmicNetR | 1. Generate example data | 2. Align samples | 3. Fit sparse CCA | 4. Convert loadings to an interaction network | 5. Visualize results | Bipartite network | Feature importance circle plot | Correlation heatmap | Notes for CRAN

Overview | 1. Load the example dataset | 2. Build the binary evidence matrix | 3. Run quantbayes core | 4. Evidence sufficiency plots | Global posterior distribution | Overlay of top candidate densities | Evidence matrix | Observed evidence proportions | Posterior credible intervals | Combined panel | 5. Highlighting variants of interest | 6. File based input | 7. Saving output tables | 8. Saving plots | 9. Customising plot themes | 10. Flagship overlay plot | Core results example | Clinical genetics example | Report text (printed automatically) | Summary

Binary Classifier | Multiclass-classifier

Overview | Prerequisite | Inputs | Required information for running revert | BAM file preparation | Outputs | Examples | Acknowledgements

Motivation | A glimpse on Multi-Stage Clonal Expansion Models | General cautions on fitting incidence data with Multi-Stage Clonal Expansion Models | A fitting approach | References

Introduction | Function Dependencies | Part 1: Generating Decision Tables | Creating Design Objects | Generating Decision Tables for Individual Designs | Comparing Decision Tables Across Designs | Saving Decision Table Plots | Other Designs | Part 2: Running Simulations | Setting Up Simulations | Running Simulations for Individual Designs | Multiple Scenario Simulations | Using Pre-defined Scenarios | Comparing Operating Characteristics | Saving Operating Characteristic Plots | Modifying Simulation Parameters

ODBC Concepts | Basic Usage | Writing to a Database | Data types | Schemas and Catalogs | Internationalization Issues | Excel Drivers | DBMS-specific tidbits | Installation | Specifying DSNs | Internals

0. Introduction to gpyramid package | 1. Set up | 2. Prepare data | 2.1 Gene data | 2.2 Position data | 2.3 Preprosessing | Generate haplotype dataframe from row data | Generate recombination probability matrix from raw data | 3. Find parent sets from candidate lines (cultivars) | 4. Calculate the number of necessary individuals and generations | 4.1 Fig 4a (Servin et al., 2004) | 4.2 Fig 4b (Servin et al., 2004) | 4.3 Fig 4c (Servin et al., 2004)

1. Set up | 2. Prepare data | 2.1 Gene data | 2.2 Position data | 2.3 Preprosessing | Generate haplotype dataframe from row data | Generate recombination probability matrix from raw data | 3. Find parent sets from candidate lines (cultivars) | 4. Calculate the number of necessary individuals and generations | 4.1 Calculate cost of all the crossing schemes | 4.2 Plot cost of each crossing scheme | 4.3 Select the most cost-effective crossing strategy | 4.4 Another example

History | Example 1. Nonlinear curves using kmeans+silhouette and Ball+BIC clustering with a fixed number of clusters | Example 2. Nonlinear curves and patterns using kmeans+silhouette and Ball+BIC clustering with a range for the number of clusters | Example 3. Using the partition around medoids clustering method | Example 4 Random patterns using kmeans+silhouette and Ball+BIC clustering with a range for the number of clusters | Example 5. Multi-cluster random patterns using kmeans+silhouette and Ball+BIC clustering with a range for the number of clusters | Example 6. Exclusive or. | Example 7. Three well separated clusters | Example 8. Four spheres with varying centers and radii | Example 9. A small dense sphere overlapping a large sphere

Introduction | Loading data | Preparation of functions inputs | InputsModel object | RunOptions object | InputsCrit object | CalibOptions object | Criteria | Calibration | Control | Simulation | Simulation run | Results preview | Efficiency criterion | Features diagram | References

Introduction | Scope | Standard Least Squares (SLS) Bayesian inference | MCMC algorithm for Bayesian inference | Estimation of the best-fit parameters as a starting point | Running 3 chains for convergence assessment | MCMC diagnostics and visualisation tools | Exploring further possibilities

Introduction | Scope | Definition of the necessary function | Local optimization | Global optimization | Differential Evolution | Particle Swarm | MA-LS-Chains | Results | Multiobjective optimization | caRamel

Introduction | Scope | Model description | Calibration of the upstream subcatchment | Calibration of the downstream subcatchment | Creation of the InputsModel objects | Calibration with upstream flow observations | Calibration with upstream simulated flow | Calibration with upstream simulated flow and parameter regularisation | Discussion | Identification of Velocity parameter | Value of the performance criteria with theoretical calibration | Parameters and performance of each subcatchment for all calibrations | References

URL's for examples | the mke_county polygon is available as an exported object in arcpullr | Querying Spatially via ArcGIS Feature Service | Querying via SQL | Raster Layers | Plotting Layers

URL's for examples | WI Landcover Type URL | WI Leaf-off Aerial Imagery URL | the wis_poly polygon is available as an exported object in arcpullr | Image Layers

Load Packages | Prepare data | Compute power values through Bootstrapping | Instead of using default Wilcoxon test | PoweREST_gene | PoweREST_subset | Fit the power surface using smoothing splines | Visualize the power surface | Create interactive visualization result | Fit local power surface with XGBoost | Visualize the local power surface

Introduction | Quick example: Efficacy and acceptability of 12 Antidepressants | Input dataset | Two types of layouts: row-column and upper-left lower-right | SUCRA values | Save the plot | Plotting parameters | Different types of outcomes | Ordering treatments | Controlling symbols | Changing plate (circle) size | Changing text size | Abbreviating long treatment names | Changing colors to indicate treatment rankings | Changing colors for texts and plate circles | Changing background colors for upper and lower diagonal parts | Adding the title | Additional examples | NMA results with missing values | Large NMA results (number of treatments is 22)

Chick Weights | UCB Admissions | References

Chick Weights | Ornstein Data | Multi-panel Display: UCBAdmissions

Chick Weights | Ornstein Data

statisR Package version 1.0 | Oldemar Rodríguez R. | Installing the package | CRAN | STATIS Method | How to read a Table from a CSV file? | Principal Functions | statis | plot.statis.circle | plot.statis.plane | Example 1: Article on Wine Evaluation by Experts | Apply statis without any selection and save the result | Plot Correlation Circle of all the tables | Plot Correlation Circle of all variables evolution | Plot Principal Plane of Average Individuals | Plot Principal Plane of the Evolution of Individuals | Apply statis with specific selections and save the result | Selecting tables 1 and 3 | Selecting rows 1 and 5 | Selecting rows 3 and 4 | Selecting tables (1,3) and rows (1,4) | Example 2: Tarcoles River Basin of Costa Rica | Read csv files and load data | Statis without selections | Selecting only table 2 | Selecting only row 3 | Selecting table 2 and row 3 | STATIS-DUAL Method | statis.dual | plot.statis.dual.circle | plot.statis.dual.trajectories | select.super.variables | Example 1: Sugarcane in Costa Rica | Read csv files and apply STATIS DUAL | Use plot.statis.dual.circle to get the Interstructure graph | Use plot.statis.dual.circle to get the Correlation Circle for all variables | Use plot.statis.dual.circle to get the Correlation Circle for the selected variables | Use plot.statis.dual.trajectories to get the trajectories graph for the selected variables | Use plot.statis.dual.trajectories to get the trajectories graph for all variables | Example 2: airquality (base R) ⇒ K = 5 = months | Data | Apply STATIS DUAL | Interstructure graph by Month | Correlation Circle for all variables | Correlation Circle with Selected Variables | Variable Trajectories by Month | Trajectory of All Variables

Introduction | Computation of moments | The trunmnt package | Summary

Introduction | Installation instructions | Food composition datasets | Dietary assessment tools | Preparing the input | Nutrient calculator | Visualization tools | Generate plot | Adjust font size | Shiny app | References

Overview | Installation | Input Preparation | Step by step guide to running the package | Annotation steps of the top GWAS variants | Package functions: | demo_annotation() | getConfigFile() | run_annotation() | findProxy() | findPairwiseLD() | findGenomicPos() | findRSID() | run_stringdb_annotation()

Installation | Citation | Example | 1. Preprocessing | 2. Analysis

1. Select a wrapper | 2. Check arguments | 2.1. Code string | Package name | Data name | Target variable(s) | Level variable(s) | 2.2. Input data | 2.3. Test results or named list results | 3. Create an instance | 4. Modify the instance | 5. Include the instance into the overarching data_analysis instance | 6. Write JSON-LD | Example I: group comparison | Example II: algorithm evaluation | Example III: an all-in-one wrapper for anova

1. Load a DTR schema | 2. Create an instance | Fields | Most common types of input | Strings | Numeric | Data frames and tuples | More than one input in a field | Nested structure | General remarks on writing an instance | 3. Convert the instance into JSON-LD format | 4. Example: reporting data analysis with dtreg | Choose parts of analysis to address and schemata to load | Prepare data frames with test results | Find out which schemata parts can be reused | Write all instances with the information specified above | Write the data_analysis instance and convert into JSON-LD format | Additional comments

Reference | Shiny | Note

Load required packages for the vignette | We explicitly reference fmi:: functions for clarity | Apply a mean_shift of 1.0 to FPC 1 for Group B | Check data structure

Unscaled version | Scaled version | Unscaled version with nutrient dynamic

ACKNOWLEDGEMENT | INTRODUCTION | MODEL | EXAMPLE | REFERENCES

1. System requirements | 2. Overview of CSTools structure | 3. Data storage recommendations | 4. CST_Load example | 5. CST_Start example | Managing big datasets and memory issues

Weather regime analysis | 1- Required packages | 2- Retrive data from files | 3- Daily anomalies based on a smoothed climatology | 4- Weather regimes in observations | 5- Visualisation of the observed weather regimes | 6- Visualisation of the observed regime persistence | 7- Weather regimes in the predictions | 8- Visualisation of the predicted weather regimes