cran r-universe repositoryhttps://cran.r-universe.devPackage updated in crancranlike-server 0.17.1https://github.com/cran.png?size=400cran r-universe repositoryhttps://cran.r-universe.devFri, 12 Apr 2024 02:37:19 GMT[cran] traveltimeR 1.2.0frontend@traveltime.com (TravelTime)'Travel Time' API
<https://docs.traveltime.com/api/overview/introduction> helps
users find locations by journey time rather than using ‘as the
crow flies’ distance. Time-based searching gives users more
opportunities for personalisation and delivers a more relevant
search.https://github.com/r-universe/cran/actions/runs/8656564309Fri, 12 Apr 2024 02:37:19 GMTtraveltimeR1.2.0successCRANhttps://github.com/cran/traveltimeR[cran] text2map 0.2.0dss219@lehigh.edu (Dustin Stoltz)This is a collection of functions optimized for working
with with various kinds of text matrices. Focusing on the text
matrix as the primary object - represented either as a base R
dense matrix or a 'Matrix' package sparse matrix - allows for a
consistent and intuitive interface that stays close to the
underlying mathematical foundation of computational text
analysis. In particular, the package includes functions for
working with word embeddings, text networks, and document-term
matrices. Methods developed in Stoltz and Taylor (2019)
<doi:10.1007/s42001-019-00048-6>, Taylor and Stoltz (2020)
<doi:10.1007/s42001-020-00075-8>, Taylor and Stoltz (2020)
<doi:10.15195/v7.a23>, and Stoltz and Taylor (2021)
<doi:10.1016/j.poetic.2021.101567>.https://github.com/r-universe/cran/actions/runs/8656562715Fri, 12 Apr 2024 02:37:17 GMTtext2map0.2.0successCRANhttps://github.com/cran/text2map[cran] stevedata 1.2.0steve@svmiller.com (Steve Miller)This is a collection of various kinds of data with broad
uses for teaching. My students, and academics like me who teach
the same topics I teach, should find this useful if their
teaching workflow is also built around the R programming
language. The applications are multiple but mostly cluster on
topics of statistical methodology, international relations, and
political economy.https://github.com/r-universe/cran/actions/runs/8656561226Fri, 12 Apr 2024 02:37:14 GMTstevedata1.2.0successCRANhttps://github.com/cran/stevedata[cran] sgboost 0.1.0fabian.obster@unibw.de (Fabian Obster)Sparse-group boosting to be used in conjunction with the
'mboost' for modeling grouped data. Applicable to all
sparse-group lasso type problems where within-group and
between-group sparsity is desired. Interprets and visualizes
individual variables and groups.https://github.com/r-universe/cran/actions/runs/8656560571Fri, 12 Apr 2024 02:37:10 GMTsgboost0.1.0successCRANhttps://github.com/cran/sgboostsgboost.Rmdsgboost.htmlA sparse-group boosing Tutorial in R2024-04-12 02:37:102024-04-12 02:37:10[cran] seededlda 1.2.1watanabe.kohei@gmail.com (Kohei Watanabe)Seeded Sequential LDA can classify sentences of texts into
pre-define topics with a small number of seed words (Watanabe &
Baturo, 2023) <doi:10.1177/08944393231178605>. Implements
Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and
Sequential LDA (Du et al., 2012)
<doi:10.1007/s10115-011-0425-1> with the distributed LDA
algorithm (Newman, et al., 2009) for parallel computing.https://github.com/r-universe/cran/actions/runs/8656559204Fri, 12 Apr 2024 02:37:08 GMTseededlda1.2.1successCRANhttps://github.com/cran/seededlda[cran] rhub 2.0.0csardi.gabor@gmail.com (Gábor Csárdi)R-hub v2 uses GitHub Actions to run 'R CMD check' and
similar package checks. The 'rhub' package helps you set up
R-hub v2 for your R package, and start running checks.https://github.com/r-universe/cran/actions/runs/8656557593Fri, 12 Apr 2024 02:37:05 GMTrhub2.0.0successCRANhttps://github.com/cran/rhub[cran] renv 1.0.7kevin@rstudio.com (Kevin Ushey)A dependency management toolkit for R. Using 'renv', you
can create and manage project-local R libraries, save the state
of these libraries to a 'lockfile', and later restore your
library as required. Together, these tools can help make your
projects more isolated, portable, and reproducible.https://github.com/r-universe/cran/actions/runs/8656556333Fri, 12 Apr 2024 02:37:02 GMTrenv1.0.7successCRANhttps://github.com/cran/renvfaq.Rmdfaq.htmlFrequently asked questions2019-10-25 15:00:022023-07-07 20:50:02package-install.Rmdpackage-install.htmlInstalling packages2023-07-07 20:50:022024-04-11 05:30:06renv.Rmdrenv.htmlIntroduction to renv2019-10-25 15:00:022024-02-22 02:30:39packages.Rmdpackages.htmlPackage development2019-12-05 19:50:052024-04-11 05:30:06package-sources.Rmdpackage-sources.htmlPackage sources2023-07-07 20:50:022023-07-07 20:50:02packrat.Rmdpackrat.htmlpackrat vs. renv2023-07-07 20:50:022023-07-07 20:50:02profiles.Rmdprofiles.htmlProject profiles2021-02-24 19:00:032024-02-29 02:31:31python.Rmdpython.htmlUsing Python with renv2019-10-25 15:00:022023-07-07 20:50:02ci.Rmdci.htmlUsing renv with continuous integration2019-10-25 15:00:022024-02-29 02:31:31docker.Rmddocker.htmlUsing renv with Docker2019-10-25 15:00:022023-08-10 18:31:12rsconnect.Rmdrsconnect.htmlUsing renv with Posit Connect2021-01-07 17:10:082023-07-07 20:50:02[cran] r5r 2.0rafa.pereira.br@gmail.com (Rafael H. M. Pereira)Rapid realistic routing on multimodal transport networks
(walk, bike, public transport and car) using 'R5', the Rapid
Realistic Routing on Real-world and Reimagined networks engine
<https://github.com/conveyal/r5>. The package allows users to
generate detailed routing analysis or calculate travel time
matrices using seamless parallel computing on top of the R5
Java machine. While R5 is developed by Conveyal, the package
r5r is independently developed by a team at the Institute for
Applied Economic Research (Ipea) with contributions from
collaborators. Apart from the documentation in this package,
users will find additional information on R5 documentation at
<https://docs.conveyal.com/>. Although we try to keep new
releases of r5r in synchrony with R5, the development of R5
follows Conveyal's independent update process. Hence, users
should confirm the R5 version implied by the Conveyal user
manual (see <https://docs.conveyal.com/changelog>) corresponds
with the R5 version that r5r depends on. This version of r5r
depends on R5 v7.1.https://github.com/r-universe/cran/actions/runs/8656555675Fri, 12 Apr 2024 02:36:57 GMTr5r2.0successCRANhttps://github.com/cran/r5raccessibility.Rmdaccessibility.htmlAccessibility2023-08-08 08:30:582023-08-08 08:30:58fare_structure.Rmdfare_structure.htmlAccounting for monetary costs2023-01-27 15:50:022024-04-12 02:36:57faq.Rmdfaq.htmlFAQ - Frequently Asked Questions2023-08-08 08:30:582023-08-08 08:30:58r5r.Rmdr5r.htmlIntro to r5r: Rapid Realistic Routing with R5 in R2023-01-27 15:50:022024-04-12 02:36:57isochrones.Rmdisochrones.htmlIsochrones2023-08-08 08:30:582023-08-08 08:30:58pareto_frontier.Rmdpareto_frontier.htmlTrade-offs between travel time and monetary cost2023-01-27 15:50:022023-08-08 08:30:58travel_time_matrix.Rmdtravel_time_matrix.htmlTravel time matrices2023-01-27 15:50:022024-04-12 02:36:57detailed_itineraries.Rmddetailed_itineraries.htmlTrip planning with detailed_itineraries()2023-01-27 15:50:022024-04-12 02:36:57time_window.Rmdtime_window.htmlUsing the time_window parameter2023-01-27 15:50:022024-04-12 02:36:57[cran] quanteda.textmodels 0.9.7kbenoit@lse.ac.uk (Kenneth Benoit)Scaling models and classifiers for sparse matrix objects
representing textual data in the form of a document-feature
matrix. Includes original implementations of 'Laver',
'Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>,
'Wordscores' model, the Perry and 'Benoit' (2017)
<doi:10.48550/arXiv.1710.08963> class affinity scaling model,
and the 'Slapin' and 'Proksch' (2008)
<doi:10.1111/j.1540-5907.2008.00338.x> 'wordfish' model, as
well as methods for correspondence analysis, latent semantic
analysis, and fast Naive Bayes and linear 'SVMs' specially
designed for sparse textual data.https://github.com/r-universe/cran/actions/runs/8656554631Fri, 12 Apr 2024 02:36:54 GMTquanteda.textmodels0.9.7successCRANhttps://github.com/cran/quanteda.textmodelstextmodel_performance.Rmdtextmodel_performance.htmltextmodel Performance Comparisons2020-03-13 09:00:112024-04-12 02:36:54[cran] psychotree 0.16-1Achim.Zeileis@R-project.org (Achim Zeileis)Recursive partitioning based on psychometric models,
employing the general MOB algorithm (from package partykit) to
obtain Bradley-Terry trees, Rasch trees, rating scale and
partial credit trees, and MPT trees, trees for 1PL, 2PL, 3PL
and 4PL models and generalized partial credit models.https://github.com/r-universe/cran/actions/runs/8656553590Fri, 12 Apr 2024 02:36:50 GMTpsychotree0.16-1successCRANhttps://github.com/cran/psychotreeraschtree.Rnwraschtree.pdfUsing the raschtree Function for Detecting Differential Item Functioning in the Rasch Model2012-10-232022-05-06 08:40:02[cran] offsetreg 1.1.0mattrmattrs@gmail.com (Matt Heaphy)Extend the 'tidymodels' ecosystem
<https://www.tidymodels.org/> to enable the creation of
predictive models with offset terms. Models with offsets are
most useful when working with count data or when fitting an
adjustment model on top of an existing model with a prior
expectation. The former situation is common in insurance where
data is often weighted by exposures. The latter is common in
life insurance where industry mortality tables are often used
as a starting point for setting assumptions.https://github.com/r-universe/cran/actions/runs/8656552972Fri, 12 Apr 2024 02:36:48 GMToffsetreg1.1.0successCRANhttps://github.com/cran/offsetregusage.Rmdusage.htmlWhen to use offsetreg2024-04-12 02:36:482024-04-12 02:36:48[cran] mirai 0.13.2charlie.gao@shikokuchuo.net (Charlie Gao)Lightweight parallel code execution and distributed
computing. Designed for simplicity, a 'mirai' evaluates an R
expression asynchronously, on local or network resources,
resolving automatically upon completion. State of the art
networking and concurrency via 'nanonext' and 'NNG' (Nanomsg
Next Gen) offers reliable and efficient scheduling over fast
inter-process communications or TCP/IP secured by TLS.https://github.com/r-universe/cran/actions/runs/8656552352Fri, 12 Apr 2024 02:36:46 GMTmirai0.13.2successCRANhttps://github.com/cran/miraimirai.Rmdmirai.htmlmirai - Minimalist Async Evaluation Framework for R2023-11-04 21:30:482024-04-12 02:36:46parallel.Rmdparallel.htmlmirai - Parallel Integration2024-01-13 02:39:182024-04-12 02:36:46plumber.Rmdplumber.htmlmirai - Plumber Integration2024-01-13 02:39:182024-04-12 02:36:46promises.Rmdpromises.htmlmirai - Promises Integration2024-01-13 02:39:182024-04-12 02:36:46shiny.Rmdshiny.htmlmirai - Shiny Integration2024-01-13 02:39:182024-04-12 02:36:46torch.Rmdtorch.htmlmirai - Torch Integration2024-01-13 02:39:182024-04-12 02:36:46[cran] mapmisc 2.0.9patrick.brown@utoronto.ca (Patrick Brown)Provides a minimal, light-weight set of tools for
producing nice looking maps in R, with support for map
projections. See Brown (2016) <doi:10.32614/RJ-2016-005>.https://github.com/r-universe/cran/actions/runs/8656551476Fri, 12 Apr 2024 02:36:43 GMTmapmisc2.0.9failureCRANhttps://github.com/cran/mapmiscnorth.Rmdnorth.htmlMapping Hans Island2016-05-21 00:13:462024-04-12 02:36:43mapmisc.Rnwmapmisc.pdfOverview of mapping with mapmisc2017-08-14 15:28:212023-09-13 08:31:51perspective.Rmdperspective.htmlSpace Station Map Projections2017-08-14 15:28:212024-04-12 02:36:43[cran] gpboost 1.4.0fabiosigrist@gmail.com (Fabio Sigrist)An R package that allows for combining tree-boosting with
Gaussian process and mixed effects models. It also allows for
independently doing tree-boosting as well as inference and
prediction for Gaussian process and mixed effects models. See
<https://github.com/fabsig/GPBoost> for more information on the
software and Sigrist (2022, JMLR)
<https://www.jmlr.org/papers/v23/20-322.html> and Sigrist
(2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more
information on the methodology.https://github.com/r-universe/cran/actions/runs/8656550795Fri, 12 Apr 2024 02:36:39 GMTgpboost1.4.0successCRANhttps://github.com/cran/gpboost[cran] goldfish 1.6.8ualvaro@ethz.ch (Alvaro Uzaheta)Tools for fitting statistical network models to dynamic
network data. Can be used for fitting both dynamic network
actor models ('DyNAMs') and relational event models ('REMs').
Stadtfeld, Hollway, and Block (2017a)
<doi:10.1177/0081175017709295>, Stadtfeld, Hollway, and Block
(2017b) <doi:10.1177/0081175017733457>, Stadtfeld and Block
(2017) <doi:10.15195/v4.a14>, Hoffman et al. (2020)
<doi:10.1017/nws.2020.3>.https://github.com/r-universe/cran/actions/runs/8656549942Fri, 12 Apr 2024 02:36:34 GMTgoldfish1.6.8successCRANhttps://github.com/cran/goldfishdynami-example.Rmddynami-example.htmlDyNAM-i: an example script2022-08-24 07:30:062024-04-12 02:36:34teaching1.Rmdteaching1.htmlDyNAM: How to start2022-08-24 07:30:062024-04-12 02:36:34goldfishEffects.RmdgoldfishEffects.htmlgoldfish Effects2022-08-24 07:30:062024-04-12 02:36:34teaching2.Rmdteaching2.htmlThe Coordination Model and Extensions2022-08-24 07:30:062024-04-12 02:36:34[cran] gasanalyzer 0.4.0thalecress+p@gmail.com (Danny Tholen)Provides functions to import data from several instruments
commonly used by plant physiologists to measure characteristics
related to photosynthesis. It provides a standardized list of
variable names, and several sets of equations to calculate
additional variables based on the measurements. These
equations have been described by von Caemmerer and Farquhar
(1981) <doi:10.1007/BF00384257>, Busch et al. (2020)
<doi:10.1038/s41477-020-0606-6> and Márquez et al. (2021)
<doi:10.1038/s41477-021-00861-w>. In addition, this package
facilitates performing sensitivity analyses on variables or
assumptions used in the calculations.https://github.com/r-universe/cran/actions/runs/8656548872Fri, 12 Apr 2024 02:36:31 GMTgasanalyzer0.4.0successCRANhttps://github.com/cran/gasanalyzergasanalyzer.Rmdgasanalyzer.htmlgasanalyzer2024-01-11 02:41:192024-04-12 02:36:31[cran] fastai 2.2.2turqut.a.314@gmail.com (Turgut Abdullayev)The 'fastai' <https://docs.fast.ai/index.html> library
simplifies training fast and accurate neural networks using
modern best practices. It is based on research in to deep
learning best practices undertaken at 'fast.ai', including 'out
of the box' support for vision, text, tabular, audio, time
series, and collaborative filtering models.https://github.com/r-universe/cran/actions/runs/8656548120Fri, 12 Apr 2024 02:36:27 GMTfastai2.2.2successCRANhttps://github.com/cran/fastaiaudio.Rmdaudio.htmlAudio Classification2020-11-11 08:10:022024-04-12 02:36:27basic_img_class.Rmdbasic_img_class.htmlBasic Image Classification2020-11-11 08:10:022024-04-12 02:36:27Basic_Tabular.RmdBasic_Tabular.htmlBasic Tabular2020-11-11 08:10:022024-04-12 02:36:27bayes_opt.Rmdbayes_opt.htmlBayesian Optimisation2021-03-06 22:50:112024-04-12 02:36:27callbacks.Rmdcallbacks.htmlCallbacks2020-11-11 08:10:022024-04-12 02:36:27custom_img.Rmdcustom_img.htmlCustom Image Classification2020-11-11 08:10:022024-04-12 02:36:27data_aug.Rmddata_aug.htmlData augmentation2020-11-11 08:10:022024-04-12 02:36:27gpt.Rmdgpt.htmlGPT22020-11-11 08:10:022024-04-12 02:36:27head_pose.Rmdhead_pose.htmlHead pose2020-11-11 08:10:022024-04-12 02:36:27low.Rmdlow.htmlLow-level ops2020-11-12 07:50:032024-04-12 02:36:27medical_dcm.Rmdmedical_dcm.htmlMedical image2020-11-11 08:10:022024-04-12 02:36:27catalyst.Rmdcatalyst.htmlMigrating from Catalyst2020-11-11 08:10:022024-04-12 02:36:27migrating_ignite.Rmdmigrating_ignite.htmlMigrating from Ignite2020-11-11 08:10:022024-04-12 02:36:27lightning.Rmdlightning.htmlMigrating from Lightning2020-11-11 08:10:022024-04-12 02:36:27migrating_pytorch.Rmdmigrating_pytorch.htmlMigrating from Pytorch2020-11-11 08:10:022024-04-12 02:36:27multilabel.Rmdmultilabel.htmlMultilabel classification2020-12-09 12:40:032024-04-12 02:36:27obj_detect.Rmdobj_detect.htmlObject detection2020-11-11 08:10:022024-04-12 02:36:27optimizer.Rmdoptimizer.htmlOptimizer2020-11-11 08:10:022024-04-12 02:36:27question_answering.Rmdquestion_answering.htmlQuestion-Answering2020-12-09 12:40:032024-04-12 02:36:27textclassification.Rmdtextclassification.htmlRoBERTa2020-12-09 12:40:032024-04-12 02:36:27audio2tf.Rmdaudio2tf.htmlSpeech Recognition2020-11-11 08:10:022024-04-12 02:36:27super_res_gan.Rmdsuper_res_gan.htmlSuper-Resolution GAN2020-11-11 08:10:022024-04-12 02:36:27textsummarize.Rmdtextsummarize.htmlText-summarization2020-12-09 12:40:032024-04-12 02:36:27time_series.Rmdtime_series.htmlTime-Series2020-11-11 08:10:022024-04-12 02:36:27[cran] evsim 1.5.0marc.canigueral@udg.edu (Marc Cañigueral)Simulation of Electric Vehicles charging sessions using
Gaussian models, together with time-series power demand
calculations. The simulation methodology is published in
Cañigueral et al. (2023, ISBN:0957-4174)
<doi:10.1016/j.eswa.2023.120318>.https://github.com/r-universe/cran/actions/runs/8656547316Fri, 12 Apr 2024 02:36:23 GMTevsim1.5.0successCRANhttps://github.com/cran/evsim[cran] butcher 0.3.4julia.silge@posit.co (Julia Silge)Provides a set of S3 generics to axe components of fitted
model objects and help reduce the size of model objects saved
to disk.https://github.com/r-universe/cran/actions/runs/8656546385Fri, 12 Apr 2024 02:36:20 GMTbutcher0.3.4successCRANhttps://github.com/cran/butcheradding-models-to-butcher.Rmdadding-models-to-butcher.htmlAdding models to butcher2019-08-09 14:30:022024-04-12 02:36:20available-axe-methods.Rmdavailable-axe-methods.htmlAvailable axe methods2019-08-09 14:30:022024-04-12 02:36:20butcher.Rmdbutcher.htmlbutcher2019-08-09 14:30:022024-04-12 02:36:20[cran] asremlPlus 4.4.32chris.brien@adelaide.edu.au (Chris Brien)Assists in automating the selection of terms to include in
mixed models when 'asreml' is used to fit the models.
Procedures are available for choosing models that conform to
the hierarchy or marginality principle, for fitting and
choosing between two-dimensional spatial models using
correlation, natural cubic smoothing spline and P-spline
models. A history of the fitting of a sequence of models is
kept in a data frame. Also used to compute functions and
contrasts of, to investigate differences between and to plot
predictions obtained using any model fitting function. The
content falls into the following natural groupings: (i) Data,
(ii) Model modification functions, (iii) Model selection and
description functions, (iv) Model diagnostics and simulation
functions, (v) Prediction production and presentation
functions, (vi) Response transformation functions, (vii) Object
manipulation functions, and (viii) Miscellaneous functions (for
further details see 'asremlPlus-package' in help). The 'asreml'
package provides a computationally efficient algorithm for
fitting a wide range of linear mixed models using Residual
Maximum Likelihood. It is a commercial package and a license
for it can be purchased from 'VSNi' <https://vsni.co.uk/> as
'asreml-R', who will supply a zip file for local
installation/updating (see <https://asreml.kb.vsni.co.uk/>). It
is not needed for functions that are methods for 'alldiffs' and
'data.frame' objects. The package 'asremPlus' can also be
installed from <http://chris.brien.name/rpackages/>.https://github.com/r-universe/cran/actions/runs/8656545413Fri, 12 Apr 2024 02:36:17 GMTasremlPlus4.4.32successCRANhttps://github.com/cran/asremlPlusasremlPlus-manual.pdf.asisasremlPlus-manual.pdfasremlPlus-manual2019-03-04 10:50:072019-03-04 10:50:07Ladybird.asreml.pdf.asisLadybird.asreml.pdfLadybird: a predictions example using asreml and asremlPlus2020-03-16 15:50:022020-03-16 15:50:02Ladybird.lm.pdf.asisLadybird.lm.pdfLadybird: a predictions example using lm and asremlPlus2020-03-16 15:50:022020-03-16 15:50:02Wheat.analysis.pdf.asisWheat.analysis.pdfWheat: a full analysis of an experiment with spatial variation2020-03-16 15:50:022020-03-16 15:50:02Wheat.infoCriteria.pdf.asisWheat.infoCriteria.pdfWheat: using information criteria2020-03-16 15:50:022020-03-16 15:50:02WheatSpatialModels.pdf.asisWheatSpatialModels.pdfWheatSpatialModels: a full analysis of an experiment that includes choosing local spatial variation models2023-06-13 07:20:052023-06-13 07:20:05[cran] alien 1.0.1hezibuba@mail.tau.ac.il (Yehezkel Buba)Easily estimate the introduction rates of alien species
given first records data. It specializes in addressing the role
of sampling on the pattern of discoveries, thus providing
better estimates than using Generalized Linear Models which
assume perfect immediate detection of newly introduced species.https://github.com/r-universe/cran/actions/runs/8656544429Fri, 12 Apr 2024 02:36:11 GMTalien1.0.1successCRANhttps://github.com/cran/aliennative_discoveries.Rmdnative_discoveries.htmlBayesian model example - Native discoveries2024-04-12 02:36:112024-04-12 02:36:11simulations.Rmdsimulations.htmlSimulations2024-04-12 02:36:112024-04-12 02:36:11[cran] SticsRFiles 1.3.0patrice.lecharpentier@inrae.fr (Patrice Lecharpentier)Manipulating input and output files of the 'STICS' crop
model. Files are either 'JavaSTICS' XML files or text files
used by the model 'fortran' executable. Most basic
functionalities are reading or writing parameter names and
values in both XML or text input files, and getting data from
output files. Advanced functionalities include XML files
generation from XML templates and/or spreadsheets, or text
files generation from XML files by using 'xslt' transformation.https://github.com/r-universe/cran/actions/runs/8656543710Fri, 12 Apr 2024 02:36:09 GMTSticsRFiles1.3.0successCRANhttps://github.com/cran/SticsRFilesGenerating_Stics_text_files.RmdGenerating_Stics_text_files.htmlGenerating STICS text files from XML files2023-07-12 03:36:202024-02-24 02:27:40Generating_Stics_XML_files.RmdGenerating_Stics_XML_files.htmlGenerating STICS XML files from tabulated data2023-07-12 03:36:202024-04-12 02:36:09Manipulating_Stics_text_files.RmdManipulating_Stics_text_files.htmlManipulating STICS text files2023-07-12 03:36:202023-07-12 03:36:20Manipulating_Stics_XML_files.RmdManipulating_Stics_XML_files.htmlManipulating STICS XML files2023-07-12 03:36:202024-02-24 02:27:40SticsRFiles.RmdSticsRFiles.htmlSticsRFiles2023-07-12 03:36:202024-02-24 02:27:40Upgrading_STICS_XML_files.RmdUpgrading_STICS_XML_files.htmlUpgrading STICS XML files2023-07-12 03:36:202024-02-24 02:27:40[cran] SimDesign 2.15rphilip.chalmers@gmail.com (Phil Chalmers)Provides tools to safely and efficiently organize and
execute Monte Carlo simulation experiments in R. The package
controls the structure and back-end of Monte Carlo simulation
experiments by utilizing a generate-analyse-summarise workflow.
The workflow safeguards against common simulation coding
issues, such as automatically re-simulating non-convergent
results, prevents inadvertently overwriting simulation files,
catches error and warning messages during execution, implicitly
supports parallel processing with high-quality random number
generation, and provides tools for managing high-performance
computing (HPC) array jobs submitted to schedulers such as
SLURM. For a pedagogical introduction to the package see Sigal
and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a
more in-depth overview of the package and its design philosophy
see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.https://github.com/r-universe/cran/actions/runs/8656542802Fri, 12 Apr 2024 02:36:06 GMTSimDesign2.15successCRANhttps://github.com/cran/SimDesignFixed_obj_fun.RmdFixed_obj_fun.htmlExporting objects and functions from the workspace2017-10-29 22:15:102020-11-07 04:30:02HPC-computing.RmdHPC-computing.htmlHPC clusters array jobs(e.g., via Slurm)2024-04-12 02:36:062024-04-12 02:36:06SimDesign-intro.RmdSimDesign-intro.htmlIntroduction to the SimDesign package2017-10-29 22:15:102024-04-12 02:36:06Catch_errors.RmdCatch_errors.htmlManaging warning and error messages2017-10-29 22:15:102024-01-10 02:40:09MultipleAnalyses.RmdMultipleAnalyses.htmlMultiple analysis functions2021-08-13 18:40:022024-01-10 02:40:09Parallel-computing.RmdParallel-computing.htmlParallel computing information2018-05-20 02:51:312024-04-12 02:36:06Saving-results.RmdSaving-results.htmlSaving simulation results and state2017-10-29 22:15:102024-04-12 02:36:06[cran] SBI 0.1.0david.petroff@zks.uni-leipzig.de (David Petroff)Computes a simple blinding index for randomized controlled
trials introduced in the paper "A simple blinding index for
randomized controlled trials" by Petroff, Bacak, Dagres, Dilk
and Wachter, which has been submitted for publication.https://github.com/r-universe/cran/actions/runs/8656542081Fri, 12 Apr 2024 02:36:01 GMTSBI0.1.0successCRANhttps://github.com/cran/SBI[cran] PatientProfiles 0.8.0marti.catalasabate@ndorms.ox.ac.uk (Marti Catala)Identify the characteristics of patients in data mapped to
the Observational Medical Outcomes Partnership (OMOP) common
data model.https://github.com/r-universe/cran/actions/runs/8656541424Fri, 12 Apr 2024 02:35:59 GMTPatientProfiles0.8.0successCRANhttps://github.com/cran/PatientProfilesdemographics.rmddemographics.htmldemographics2024-04-12 02:35:592024-04-12 02:35:59[cran] KEPTED 0.1.1yqt5219@psu.edu (Yin Tang)Provides an implementation of a kernel-embedding of
probability test for elliptical distribution. This is an
asymptotic test for elliptical distribution under general
alternatives, and the location and shape parameters are assumed
to be unknown. Some side-products are posted, including the
transformation between rectangular and polar coordinates and
two product-type kernel functions. See Tang and Li (2024)
<arXiv:2306.10594> for details.https://github.com/r-universe/cran/actions/runs/8656540484Fri, 12 Apr 2024 02:35:56 GMTKEPTED0.1.1successCRANhttps://github.com/cran/KEPTEDKEPTED.RmdKEPTED.htmlKEPTED2024-03-28 02:34:022024-03-28 02:34:02[cran] GetTDData 1.5.5marceloperlin@gmail.com (Marcelo Perlin)Downloads and aggregates data for Brazilian government
issued bonds directly from the website of Tesouro Direto
<https://www.tesourodireto.com.br/>.https://github.com/r-universe/cran/actions/runs/8656539749Fri, 12 Apr 2024 02:35:54 GMTGetTDData1.5.5successCRANhttps://github.com/cran/GetTDData[cran] GeoModels 2.0.1moreno.bevilacqua89@gmail.com (Moreno Bevilacqua)Functions for Gaussian and Non Gaussian (bivariate)
spatial and spatio-temporal data analysis are provided for a)
(fast) simulation of random fields, b) inference for random
fields using standard likelihood and a likelihood approximation
method called weighted composite likelihood based on pairs and
b) prediction using (local) best linear unbiased prediction.
Weighted composite likelihood can be very efficient for
estimating massive datasets. Both regression and spatial
(temporal) dependence analysis can be jointly performed.
Flexible covariance models for spatial and spatial-temporal
data on Euclidean domains and spheres are provided. There are
also many useful functions for plotting and performing
diagnostic analysis. Different non Gaussian random fields can
be considered in the analysis. Among them, random fields with
marginal distributions such as Skew-Gaussian, Student-t,
Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian,
Binomial, Negative Binomial and Poisson. See the URL for the
papers associated with this package, as for instance,
Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>,
Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>,
Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>,
Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et.
al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022)
<doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al.
(2023) <doi:10.1080/01621459.2022.2140053>, and a large class
of examples and tutorials.https://github.com/r-universe/cran/actions/runs/8656539027Fri, 12 Apr 2024 02:35:52 GMTGeoModels2.0.1successCRANhttps://github.com/cran/GeoModels[cran] GeDS 0.2.1Emilio.Saenz-Guillen@bayes.city.ac.uk (Emilio S. Guillen)Spline Regression, Generalized Additive Models, and
Component-wise Gradient Boosting, utilizing Geometrically
Designed (GeD) Splines. GeDS regression is a non-parametric
method inspired by geometric principles, for fitting spline
regression models with variable knots in one or two independent
variables. It efficiently estimates the number of knots and
their positions, as well as the spline order, assuming the
response variable follows a distribution from the exponential
family. GeDS models integrate the broader category of
Generalized (Non-)Linear Models, offering a flexible approach
to modeling complex relationships. A description of the method
can be found in Kaishev et al. (2016)
<doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023)
<doi:10.1016/j.amc.2022.127493>. Further extending its
capabilities, GeDS's implementation includes Generalized
Additive Models (GAM) and Functional Gradient Boosting (FGB),
enabling versatile multivariate predictor modeling, as
discussed in the forthcoming work of Dimitrova et al. (2024).https://github.com/r-universe/cran/actions/runs/8656538203Fri, 12 Apr 2024 02:35:48 GMTGeDS0.2.1successCRANhttps://github.com/cran/GeDS[cran] Directional 6.6mtsagris@uoc.gr (Michail Tsagris)A collection of functions for directional data (including
massive data, with millions of observations) analysis.
Hypothesis testing, discriminant and regression analysis, MLE
of distributions and more are included. The standard textbook
for such data is the "Directional Statistics" by Mardia, K. V.
and Jupp, P. E. (2000). Other references include a) Phillip J.
Paine, Simon P. Preston Michail Tsagris and Andrew T. A. Wood
(2018). "An elliptically symmetric angular Gaussian
distribution". Statistics and Computing 28(3): 689-697.
<doi:10.1007/s11222-017-9756-4>. b) Tsagris M. and Alenazi A.
(2019). "Comparison of discriminant analysis methods on the
sphere". Communications in Statistics: Case Studies, Data
Analysis and Applications 5(4):467--491.
<doi:10.1080/23737484.2019.1684854>. c) P. J. Paine, S. P.
Preston, M. Tsagris and Andrew T. A. Wood (2020). "Spherical
regression models with general covariates and anisotropic
errors". Statistics and Computing 30(1): 153--165.
<doi:10.1007/s11222-019-09872-2>. d) Tsagris M. and Alenazi A.
(2024). "An investigation of hypothesis testing procedures for
circular and spherical mean vectors". Communications in
Statistics-Simulation and Computation, 53(3): 1387--1408.
<doi:10.1080/03610918.2022.2045499>. e) Tsagris M. and Alzeley
O. (2023). "Circular and spherical projected Cauchy
distributions: A Novel Framework for Circular and Directional
Data Modeling". <doi:10.48550/arXiv.2302.02468>.https://github.com/r-universe/cran/actions/runs/8656537168Fri, 12 Apr 2024 02:35:44 GMTDirectional6.6successCRANhttps://github.com/cran/Directional[cran] DanielBiostatistics10th 0.2.0tingtingzhan@gmail.com (Tingting Zhan)Functions to accompany Wayne W. Daniel's Biostatistics: A
Foundation for Analysis in the Health Sciences, Tenth Edition.https://github.com/r-universe/cran/actions/runs/8656536445Fri, 12 Apr 2024 02:35:42 GMTDanielBiostatistics10th0.2.0successCRANhttps://github.com/cran/DanielBiostatistics10th[cran] AcceptReject 0.1.0pedro.rafael.marinho@gmail.com (Pedro Rafael D. Marinho)Provides a function that implements the
acceptance-rejection method in an optimized manner to generate
pseudo-random observations for discrete or continuous random
variables. The function is optimized to work in parallel on
Unix-based operating systems and performs well on Windows
systems. The acceptance-rejection method implemented optimizes
the probability of generating observations from the desired
random variable, by simply providing the probability function
or probability density function, in the discrete and continuous
cases, respectively. Implementation is based on references
CASELLA, George at al. (2004)
<https://www.jstor.org/stable/4356322>, NEAL, Radford M. (2003)
<https://www.jstor.org/stable/3448413> and Bishop, Christopher
M. (2006, ISBN: 978-0387310732).https://github.com/r-universe/cran/actions/runs/8656535560Fri, 12 Apr 2024 02:35:38 GMTAcceptReject0.1.0successCRANhttps://github.com/cran/AcceptRejectaccept_reject.Rmdaccept_reject.htmlAcceptance and rejection method2024-04-12 02:35:382024-04-12 02:35:38[cran] matrixStats 1.3.0henrikb@braju.com (Henrik Bengtsson)High-performing functions operating on rows and columns of
matrices, e.g. col / rowMedians(), col / rowRanks(), and col /
rowSds(). Functions optimized per data type and for subsetted
calculations such that both memory usage and processing time is
minimized. There are also optimized vector-based methods, e.g.
binMeans(), madDiff() and weightedMedian().https://github.com/r-universe/cran/actions/runs/8650774340Thu, 11 Apr 2024 16:33:30 GMTmatrixStats1.3.0successCRANhttps://github.com/cran/matrixStatsmatrixStats-methods.md.rspmatrixStats-methods.htmlmatrixStats: Summary of functions2015-01-202019-09-07 15:50:15[cran] ape 5.8Emmanuel.Paradis@ird.fr (Emmanuel Paradis)Functions for reading, writing, plotting, and manipulating
phylogenetic trees, analyses of comparative data in a
phylogenetic framework, ancestral character analyses, analyses
of diversification and macroevolution, computing distances from
DNA sequences, reading and writing nucleotide sequences as well
as importing from BioConductor, and several tools such as
Mantel's test, generalized skyline plots, graphical exploration
of phylogenetic data (alex, trex, kronoviz), estimation of
absolute evolutionary rates and clock-like trees using mean
path lengths and penalized likelihood, dating trees with
non-contemporaneous sequences, translating DNA into AA
sequences, and assessing sequence alignments. Phylogeny
estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and
triangle methods, and several methods handling incomplete
distance matrices (NJ*, BIONJ*, MVR*, and the corresponding
triangle method). Some functions call external applications
(PhyML, Clustal, T-Coffee, Muscle) whose results are returned
into R.https://github.com/r-universe/cran/actions/runs/8650773110Thu, 11 Apr 2024 16:33:28 GMTape5.8successCRANhttps://github.com/cran/apeDrawingPhylogenies.RnwDrawingPhylogenies.pdfDrawing Phylogenies2021-04-25 07:20:022024-04-11 16:33:28MoranI.RnwMoranI.pdfMoran's I2013-07-19 00:00:002021-04-25 07:20:02RandomTopologies.RnwRandomTopologies.pdfRandom Topology2021-04-25 07:20:022024-04-11 16:33:28[cran] yyjsonr 0.1.20mikefc@coolbutuseless.com (Mike Cheng)A fast 'JSON' parser, generator and validator which
converts 'JSON', 'NDJSON' (Newline Delimited 'JSON') and
'GeoJSON' (Geographic 'JSON') data to/from R objects. The
standard R data types are supported (e.g. logical, numeric,
integer) with configurable handling of NULL and NA values. Data
frames, atomic vectors and lists are all supported as data
containers translated to/from 'JSON'. 'GeoJSON' data is read
in as 'simple features' objects. This implementation wraps the
'yyjson' 'C' library which is available from
<https://github.com/ibireme/yyjson>.https://github.com/r-universe/cran/actions/runs/8641108830Thu, 11 Apr 2024 02:36:55 GMTyyjsonr0.1.20successCRANhttps://github.com/cran/yyjsonrjsonlite-comparison.Rmdjsonlite-comparison.htmlComparison to jsonlite parsing2024-01-18 02:41:372024-01-18 02:41:37from_json_options.Rmdfrom_json_options.htmlConfiguration Options for Parsing from JSON2024-01-18 02:41:372024-01-18 02:41:37to_json_options.Rmdto_json_options.htmlConfiguration Options for Serializing to JSON2024-01-18 02:41:372024-01-18 02:41:37[cran] umx 4.20.0timothy.c.bates@gmail.com (Timothy C. Bates)Quickly create, run, and report structural equation
models, and twin models. See '?umx' for help, and
umx_open_CRAN_page("umx") for NEWS. Timothy C. Bates, Michael
C. Neale, Hermine H. Maes, (2019). umx: A library for
Structural Equation and Twin Modelling in R. Twin Research and
Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.https://github.com/r-universe/cran/actions/runs/8641107837Thu, 11 Apr 2024 02:36:51 GMTumx4.20.0successCRANhttps://github.com/cran/umx[cran] tidytext 0.4.2julia.silge@gmail.com (Julia Silge)Using tidy data principles can make many text mining tasks
easier, more effective, and consistent with tools already in
wide use. Much of the infrastructure needed for text mining
with tidy data frames already exists in packages like 'dplyr',
'broom', 'tidyr', and 'ggplot2'. In this package, we provide
functions and supporting data sets to allow conversion of text
to and from tidy formats, and to switch seamlessly between tidy
tools and existing text mining packages.https://github.com/r-universe/cran/actions/runs/8641106537Thu, 11 Apr 2024 02:36:41 GMTtidytext0.4.2successCRANhttps://github.com/cran/tidytexttidying_casting.Rmdtidying_casting.htmlConverting to and from Document-Term Matrix and Corpus objects2016-04-28 11:50:082024-04-11 02:36:41tidytext.Rmdtidytext.htmlIntroduction to tidytext2016-04-28 11:50:082024-04-11 02:36:41tf_idf.Rmdtf_idf.htmlTidy Term Frequency and Inverse Document Frequency (tf-idf)2016-06-25 19:07:562024-04-11 02:36:41[cran] surveyPrev 1.0.0qdong14@ucsc.edu (Qianyu Dong)Provides a pipeline to perform small area estimation and
prevalence mapping of binary indicators using health and
demographic survey data, described in Fuglstad et al. (2022)
<doi:10.48550/arXiv.2110.09576> and Wakefield et al. (2020)
<doi:10.1111/insr.12400>.https://github.com/r-universe/cran/actions/runs/8641105375Thu, 11 Apr 2024 02:36:33 GMTsurveyPrev1.0.0successCRANhttps://github.com/cran/surveyPrevvignette-data-preparation.html.asisvignette-data-preparation.htmlCreate customized indicators2024-04-11 02:36:332024-04-11 02:36:33vignette-main.html.asisvignette-main.htmlPrevalence mapping using DHS data2024-04-11 02:36:332024-04-11 02:36:33[cran] sparseCov 0.0.1cflorajiang@g.ucla.edu (Chenxin Jiang)A sparse covariance estimator based on different
thresholding operators.https://github.com/r-universe/cran/actions/runs/8641104497Thu, 11 Apr 2024 02:36:25 GMTsparseCov0.0.1successCRANhttps://github.com/cran/sparseCov[cran] shinyMatrix 0.8.0andreas.neudecker@inwt-statistics.de (Andreas Neudecker)Implements a custom matrix input field.https://github.com/r-universe/cran/actions/runs/8641104029Thu, 11 Apr 2024 02:36:23 GMTshinyMatrix0.8.0successCRANhttps://github.com/cran/shinyMatrix[cran] rtpcr 1.0.2gh.mirzaghaderi@uok.ac.ir (Ghader Mirzaghaderi)Various methods are employed for statistical analysis and
graphical presentation of real-time PCR (quantitative PCR or
qPCR) data. 'rtpcr' handles amplification efficiency
calculation, statistical analysis and graphical representation
of real-time PCR data based on up to two reference genes. By
accounting for amplification efficiency values, 'rtpcr' was
developed using a general calculation method described by
Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5>, covering
both the Livak and Pfaffl methods. Based on the experimental
conditions, the functions of the 'rtpcr' package use t-test
(for experiments with a two-level factor) or analysis of
variance (for cases where more than two levels or factors
exist) to calculate the fold change or relative expression. The
functions also provide standard deviations and confidence
limits for means and apply statistical mean comparisons. To
facilitate using 'rtpcr', different datasets have been employed
in the examples and the outputs are explained. An outstanding
feature of 'rtpcr' package is providing publication-ready bar
plots with various controlling arguments for experiments with
up to three different factors. The 'rtpcr' package is
user-friendly and easy to work with and provides an applicable
resource for analyzing real-time PCR data.https://github.com/r-universe/cran/actions/runs/8641102116Thu, 11 Apr 2024 02:36:08 GMTrtpcr1.0.2successCRANhttps://github.com/cran/rtpcrvignette.Rmdvignette.htmlSending Messages With Gmailr2024-04-03 07:14:132024-04-11 02:36:08[cran] quadform 0.0-1hankin.robin@gmail.com (Robin K. S. Hankin)A range of quadratic forms are evaluated, using efficient
methods. Unnecessary transposes are not performed. Complex
values are handled consistently.https://github.com/r-universe/cran/actions/runs/8641101456Thu, 11 Apr 2024 02:36:04 GMTquadform0.0-1successCRANhttps://github.com/cran/quadform[cran] prismatic 1.1.2emilhhvitfeldt@gmail.com (Emil Hvitfeldt)Manipulate and visualize colors in a intuitive,
low-dependency and functional way.https://github.com/r-universe/cran/actions/runs/8641100851Thu, 11 Apr 2024 02:36:01 GMTprismatic1.1.2successCRANhttps://github.com/cran/prismatic[cran] pressuRe 0.2.4scott.telfer@gmail.com (Scott Telfer)Allows biomechanical pressure data from a range of systems
to be imported and processed in a reproducible manner.
Automatic and manual tools are included to let the user define
regions (masks) to be analyzed. Also includes functions for
visualizing and animating pressure data. Example methods are
described in Shi et al., (2022)
<doi:10.1038/s41598-022-19814-0>, Lee et al., (2014)
<doi:10.1186/1757-1146-7-18>, van der Zward et al., (2014)
<doi:10.1186/1757-1146-7-20>, Najafi et al., (2010)
<doi:10.1016/j.gaitpost.2009.09.003>, Cavanagh and Rodgers
(1987) <doi:10.1016/0021-9290(87)90255-7>.https://github.com/r-universe/cran/actions/runs/8641100011Thu, 11 Apr 2024 02:35:57 GMTpressuRe0.2.4successCRANhttps://github.com/cran/pressuRe[cran] precommit 0.4.2lorenz.walthert@icloud.com (Lorenz Walthert)Useful git hooks for R building on top of the
multi-language framework 'pre-commit' for hook management. This
package provides git hooks for common tasks like formatting
files with 'styler' or spell checking as well as wrapper
functions to access the 'pre-commit' executable.https://github.com/r-universe/cran/actions/runs/8641099037Thu, 11 Apr 2024 02:35:53 GMTprecommit0.4.2successCRANhttps://github.com/cran/precommitavailable-hooks.Rmdavailable-hooks.htmlavailable-hooks2020-06-13 12:20:022024-04-01 02:39:36ci.Rmdci.htmlContinuous Integration2021-12-01 09:10:022022-06-15 06:20:02FAQ.RmdFAQ.htmlFAQ2020-06-13 12:20:022022-06-15 06:20:02hook-order.Rmdhook-order.htmlhook-order2020-06-13 12:20:022024-04-01 02:39:36precommit.Rmdprecommit.htmlprecommit2022-05-20 06:30:142022-06-15 06:20:02testing.Rmdtesting.htmltesting2021-12-01 09:10:022022-05-20 06:30:14why-use-hooks.Rmdwhy-use-hooks.htmlwhy-use-hooks2020-06-13 12:20:022022-06-15 06:20:02[cran] pedgene 3.9sinnwell.jason@mayo.edu (Jason Sinnwell)Gene-level variant association tests with disease status
for pedigree data: kernel and burden association statistics.https://github.com/r-universe/cran/actions/runs/8641098538Thu, 11 Apr 2024 02:35:49 GMTpedgene3.9successCRANhttps://github.com/cran/pedgenepedgene.Rnwpedgene.pdfpedgene_manual2013-11-082022-10-18 06:30:02[cran] pctax 0.1.1pengchen2001@zju.edu.cn (Chen Peng)Provides a comprehensive suite of tools for analyzing
omics data. It includes functionalities for alpha diversity
analysis, beta diversity analysis, differential abundance
analysis, community assembly analysis, visualization of
phylogenetic tree, and functional enrichment analysis. With a
progressive approach, the package offers a range of analysis
methods to explore and understand the complex communities. It
is designed to support researchers and practitioners in
conducting in-depth and professional omics data analysis.https://github.com/r-universe/cran/actions/runs/8641097909Thu, 11 Apr 2024 02:35:45 GMTpctax0.1.1successCRANhttps://github.com/cran/pctaxpctax.Rmdpctax.htmlpctax2024-02-28 02:32:282024-04-11 02:35:45[cran] ntranova 0.0.1mtanan200988@gmail.com (Mohamad Taher Anan)Dealing with neutrosophic data of the form N=D+I(where N
is a Neutrosophic number ,D is the determinant part of the
number and I is the indeterminacy part) using the neutrosophic
two way anova test keeps the type I error low. This algorithm
calculates the fisher statistics when we have a neutrosophic
data, also tests two hypothesizes, first is to test differences
between treatments, and second is to test differences between
sectors. For more information see Miari, Mahmoud; Anan, Mohamad
Taher; Zeina, Mohamed Bisher(2022)
<https://www.americaspg.com/articleinfo/21/show/1058>.https://github.com/r-universe/cran/actions/runs/8640721879Thu, 11 Apr 2024 02:35:41 GMTntranova0.0.1successCRANhttps://github.com/cran/ntranova[cran] mlr3misc 0.15.0michellang@gmail.com (Michel Lang)Frequently used helper functions and assertions used in
'mlr3' and its companion packages. Comes with helper functions
for functional programming, for printing, to work with
'data.table', as well as some generally useful 'R6' classes.
This package also supersedes the package 'BBmisc'.https://github.com/r-universe/cran/actions/runs/8641097056Thu, 11 Apr 2024 02:35:39 GMTmlr3misc0.15.0successCRANhttps://github.com/cran/mlr3misc[cran] mlr3filters 0.8.0michellang@gmail.com (Michel Lang)Extends 'mlr3' with filter methods for feature selection.
Besides standalone filter methods built-in methods of any
machine-learning algorithm are supported. Partial scoring of
multivariate filter methods is supported.https://github.com/r-universe/cran/actions/runs/8641096120Thu, 11 Apr 2024 02:35:36 GMTmlr3filters0.8.0successCRANhttps://github.com/cran/mlr3filters