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r-aleplot
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Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model.
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2025-03-25 |
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r-airgrteaching
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Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables.
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2025-03-25 |
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r-airr
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Schema definitions and read, write and validation tools for data formatted in accordance with the AIRR Data Representation schemas defined by the AIRR Community <http://docs.airr-community.org>.
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2025-03-25 |
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r-aiccmodavg
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Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes. The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes. The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities. Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs', 'rjags', and 'jagsUI' classes. Functions also implement model selection using BIC. Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.
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2025-03-25 |
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r-airgriwrm
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Semi-distributed Precipitation-Runoff Modelling based on 'airGR' package models integrating human infrastructures and their managements.
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2025-03-25 |
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r-aid
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public |
Performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data.
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2025-03-25 |
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r-agua
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Create and evaluate models using 'tidymodels' and 'h2o' <https://h2o.ai/>. The package enables users to specify 'h2o' as an engine for several modeling methods.
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2025-03-25 |
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r-agricolae
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Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.
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2025-03-25 |
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r-agror
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Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi:10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas).
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2025-03-25 |
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r-admiraldev
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Utility functions to check data, variables and conditions for functions used in 'admiral' and 'admiral' extension packages. Additional utility helper functions to assist developers with maintaining documentation, testing and general upkeep of 'admiral' and 'admiral' extension packages.
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2025-03-25 |
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r-admiral
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A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>).
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2025-03-25 |
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r-activity
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Provides functions to express clock time data relative to anchor points (typically solar); fit kernel density functions to animal activity time data; plot activity distributions; quantify overall levels of activity; statistically compare activity metrics through bootstrapping; evaluate variation in linear variables with time (or other circular variables).
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2025-03-25 |
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r-adabag
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It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done. Since version 2.0 the function margins() is available to calculate the margins for these classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as a function of the number of iterations. In addition, the ensembles can be pruned using the option 'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of each class for observations can be obtained. Version 3.1 modifies the relative importance measure to take into account the gain of the Gini index given by a variable in each tree and the weights of these trees. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guo and Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on unlabeled data. Version 4.2 includes the parallel computation option for some of the functions. Version 5.0 includes the Boosting and Bagging algorithms for label ranking (Albano, Sciandra and Plaia, 2023).
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2025-03-25 |
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r-activepathways
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Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The first version of the package is part of the following publication: Integrative Pathway Enrichment Analysis of Multivariate Omics Data. Paczkowska M^, Barenboim J^, Sintupisut N, Fox NS, Zhu H, Abd-Rabbo D, Mee MW, Boutros PC, PCAWG Drivers and Functional Interpretation Working Group; Reimand J, PCAWG Consortium. Nature Communications (2020) <doi:10.1038/s41467-019-13983-9>.
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2025-03-25 |
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r-accessibility
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A set of fast and convenient functions to calculate multiple transport accessibility measures. Given a pre-computed travel cost matrix and a land use dataset (containing the location of jobs, healthcare and population, for example), the package allows one to calculate active and passive accessibility levels using multiple accessibility measures, such as: cumulative opportunities (using either travel cost cutoffs or intervals), minimum travel cost to closest N number of activities, gravity-based (with different decay functions) and different floating catchment area methods.
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2025-03-25 |
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r-acid
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Functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level.
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2025-03-25 |
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r-academictwitter
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Package to query the Twitter Academic Research Product Track, providing access to full-archive search and other v2 API endpoints. Functions are written with academic research in mind. They provide flexibility in how the user wishes to store collected data, and encourage regular storage of data to mitigate loss when collecting large volumes of tweets. They also provide workarounds to manage and reshape the format in which data is provided on the client side.
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2025-03-25 |
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r-abcanalysis
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For a given data set, the package provides a novel method of computing precise limits to acquire subsets which are easily interpreted. Closely related to the Lorenz curve, the ABC curve visualizes the data by graphically representing the cumulative distribution function. Based on an ABC analysis the algorithm calculates, with the help of the ABC curve, the optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few"), subset B comprising values where the yield equals to the effort required to obtain it, and the subset C comprising of non-profitable values, i.e., the smallest data sets ("the trivial many"). Package is based on "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data", PLoS One. Ultsch. A., Lotsch J. (2015) <DOI:10.1371/journal.pone.0129767>.
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2025-03-25 |
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r-abd
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The abd package contains data sets and sample code for The Analysis of Biological Data by Michael Whitlock and Dolph Schluter (2009; Roberts & Company Publishers).
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2025-03-25 |
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r-abc
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Implements several ABC algorithms for performing parameter estimation, model selection, and goodness-of-fit. Cross-validation tools are also available for measuring the accuracy of ABC estimates, and to calculate the misclassification probabilities of different models.
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2025-03-25 |
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r-a3
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Supplies tools for tabulating and analyzing the results of predictive models. The methods employed are applicable to virtually any predictive model and make comparisons between different methodologies straightforward.
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2025-03-25 |
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r-yardstick
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Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).
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2025-03-25 |
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r-wordspace
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An interactive laboratory for research on distributional semantic models ('DSM', see <https://en.wikipedia.org/wiki/Distributional_semantics> for more information).
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2025-03-25 |
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r-wrs2
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A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.
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2025-03-25 |
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r-word2vec
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Learn vector representations of words by continuous bag of words and skip-gram implementations of the 'word2vec' algorithm. The techniques are detailed in the paper "Distributed Representations of Words and Phrases and their Compositionality" by Mikolov et al. (2013), available at <arXiv:1310.4546>.
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2025-03-25 |
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r-winch
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Obtain the native stack trace and fuse it with R's stack trace for easier debugging of R packages with native code.
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2025-03-25 |
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r-weightsvm
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Functions for subject/instance weighted support vector machines (SVM). It uses a modified version of 'libsvm' and is compatible with package 'e1071'. It also allows user defined kernel matrix.
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2025-03-25 |
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r-webfakes
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Create a web app that makes it easier to test web clients without using the internet. It includes a web app framework with path matching, parameters and templates. Can parse various 'HTTP' request bodies. Can send 'JSON' data or files from the disk. Includes a web app that implements the 'httpbin.org' web service.
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2025-03-25 |
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r-weights
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Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests. Also now includes some software for quickly recoding survey data and plotting estimates from interaction terms in regressions (and multiply imputed regressions) both with and without weights. NOTE: Weighted partial correlation calculations pulled to address a bug.
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2025-03-25 |
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r-warp
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Tooling to group dates by a variety of periods including: yearly, monthly, by second, by week of the month, and more. The groups are defined in such a way that they also represent the distance between dates in terms of the period. This extracts valuable information that can be used in further calculations that rely on a specific temporal spacing between observations.
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2025-03-25 |
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r-warbler
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Functions aiming to facilitate the analysis of the structure of animal acoustic signals in 'R'. 'warbleR' makes use of the basic sound analysis tools from the package 'seewave', and offers new tools for acoustic structure analysis. The main features of the package are the use of loops to apply tasks through acoustic signals referenced in a selection (annotation) table and the production of spectrograms in image files that allow to organize data and verify acoustic analyzes. The package offers functions to explore, organize and manipulate multiple sound files, explore and download 'Xeno-Canto' recordings, detect signals automatically, create spectrograms of complete recordings or individual signals, run different measures of acoustic signal structure, evaluate the performance of measurement methods, catalog signals, characterize different structural levels in acoustic signals, run statistical analysis of duet coordination and consolidate databases and annotation tables, among others.
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2025-03-25 |
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r-vim
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New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
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2025-03-25 |
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r-vinecopula
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Provides tools for the statistical analysis of regular vine copula models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and Dissman et al. (2013) <doi:10.1016/j.csda.2012.08.010>. The package includes tools for parameter estimation, model selection, simulation, goodness-of-fit tests, and visualization. Tools for estimation, selection and exploratory data analysis of bivariate copula models are also provided.
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2025-03-25 |
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r-vdiffr
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An extension to the 'testthat' package that makes it easy to add graphical unit tests. It provides a Shiny application to manage the test cases.
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2025-03-25 |
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r-vegan
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Ordination methods, diversity analysis and other functions for community and vegetation ecologists.
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2025-03-25 |
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r-vcfr
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Facilitates easy manipulation of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices of data. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file (*.vcf.gz). It also may be converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and familiar R software.
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2025-03-25 |
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r-validate
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Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, Chapter 6 and the JSS paper (2021) <doi:10.18637/jss.v097.i10>.
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2025-03-25 |
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r-ubl
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Provides a set of functions that can be used to obtain better predictive performance on cost-sensitive and cost/benefits tasks (for both regression and classification). This includes re-sampling approaches that modify the original data set biasing it towards the user preferences.
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2025-03-25 |
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r-tweenr
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In order to create smooth animation between states of data, tweening is necessary. This package provides a range of functions for creating tweened data that can be used as basis for animation. Furthermore it adds a number of vectorized interpolaters for common R data types such as numeric, date and colour.
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2025-03-25 |
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r-twang
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Provides functions for propensity score estimating and weighting, nonresponse weighting, and diagnosis of the weights.
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2025-03-25 |
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r-truncatednormal
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A collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) <doi:10.1111/rssb.12162> and Botev and L'Ecuyer (2015) <doi:10.1109/WSC.2015.7408180>.
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2025-03-25 |
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r-trend
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The analysis of environmental data often requires the detection of trends and change-points. This package includes tests for trend detection (Cox-Stuart Trend Test, Mann-Kendall Trend Test, (correlated) Hirsch-Slack Test, partial Mann-Kendall Trend Test, multivariate (multisite) Mann-Kendall Trend Test, (Seasonal) Sen's slope, partial Pearson and Spearman correlation trend test), change-point detection (Lanzante's test procedures, Pettitt's test, Buishand Range Test, Buishand U Test, Standard Normal Homogeinity Test), detection of non-randomness (Wallis-Moore Phase Frequency Test, Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test) and the two sample Robust Rank-Order Distributional Test.
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2025-03-25 |
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r-treedist
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Implements measures of tree similarity, including information-based generalized Robinson-Foulds distances (Phylogenetic Information Distance, Clustering Information Distance, Matching Split Information Distance; Smith 2020) <doi:10.1093/bioinformatics/btaa614>; Jaccard-Robinson-Foulds distances (Bocker et al. 2013) <doi:10.1007/978-3-642-40453-5_13>, including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>; the Matching Split Distance (Bogdanowicz & Giaro 2012) <doi:10.1109/TCBB.2011.48>; Maximum Agreement Subtree distances; the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. (1996) <doi:10.1007/3-540-61332-3_168>. Includes tools for visualizing mappings of tree space (Smith 2022) <doi:10.1093/sysbio/syab100>, for calculating the median of sets of trees, and for computing the information content of trees and splits.
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2025-03-25 |
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r-tree.interpreter
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An R re-implementation of the 'treeinterpreter' package on PyPI <https://pypi.org/project/treeinterpreter/>. Each prediction can be decomposed as 'prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) <arXiv:1906.10845>.
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2025-03-25 |
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r-traminer
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Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
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2025-03-25 |
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r-tram
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Formula-based user-interfaces to specific transformation models implemented in package 'mlt'. Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to transformation models for clustered data is provided (Barbanti and Hothorn, 2022, <DOI:10.1093/biostatistics/kxac048>). Multivariate conditional transformation models (Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) can be fitted as well.
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2025-03-25 |
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r-transformr
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In order to smoothly animate the transformation of polygons and paths, many aspects needs to be taken into account, such as differing number of control points, changing center of rotation, etc. The 'transformr' package provides an extensive framework for manipulating the shapes of polygons and paths and can be seen as the spatial brother to the 'tweenr' package.
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2025-03-25 |
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r-torch
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Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <arXiv:1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.
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2025-03-25 |
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r-tmvtnorm
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Random number generation for the truncated multivariate normal and Student t distribution. Computes probabilities, quantiles and densities, including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
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2025-03-25 |
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r-tlrmvnmvt
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Implementation of the classic Genz algorithm and a novel tile-low-rank algorithm for computing relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities. References used for this package: Foley, James, Andries van Dam, Steven Feiner, and John Hughes. "Computer Graphics: Principle and Practice". Addison-Wesley Publishing Company. Reading, Massachusetts (1987, ISBN:0-201-84840-6 1); Genz, A., "Numerical computation of multivariate normal probabilities," Journal of Computational and Graphical Statistics, 1, 141-149 (1992) <doi:10.1080/10618600.1992.10477010>; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student- t Probabilities," Statistics and Computing, 31.1, 1-16 (2021) <doi:10.1007/s11222-020-09978-y>; Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student-t Probabilities with Low-Rank Methods in R," Journal of Statistical Software, 101.4, 1-25 (2022) <doi:10.18637/jss.v101.i04>.
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2025-03-25 |
