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r-roxygen2
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Generate your Rd documentation, 'NAMESPACE' file, and collation field using specially formatted comments. Writing documentation in-line with code makes it easier to keep your documentation up-to-date as your requirements change. 'Roxygen2' is inspired by the 'Doxygen' system for C++.
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2025-03-25 |
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r-robustlmm
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A method to fit linear mixed effects models robustly. Robustness is achieved by modification of the scoring equations combined with the Design Adaptive Scale approach.
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2025-03-25 |
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r-purrrlyr
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Some functions at the intersection of 'dplyr' and 'purrr' that formerly lived in 'purrr'.
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2025-03-25 |
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r-nmf
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Provides a framework to perform Non-negative Matrix Factorization (NMF). The package implements a set of already published algorithms and seeding methods, and provides a framework to test, develop and plug new/custom algorithms. Most of the built-in algorithms have been optimized in C++, and the main interface function provides an easy way of performing parallel computations on multicore machines.
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2025-03-25 |
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r-networksis
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Tools to simulate bipartite networks/graphs with the degrees of the nodes fixed and specified. 'networksis' is part of the 'statnet' suite of packages for network analysis.
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2025-03-25 |
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r-mlr
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Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.
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2025-03-25 |
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r-joinerml
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Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project is funded by the Medical Research Council (Grant number MR/M013227/1).
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2025-03-25 |
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r-ggforce
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The aim of 'ggplot2' is to aid in visual data investigations. This focus has led to a lack of facilities for composing specialised plots. 'ggforce' aims to be a collection of mainly new stats and geoms that fills this gap. All additional functionality is aimed to come through the official extension system so using 'ggforce' should be a stable experience.
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2025-03-25 |
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r-degreenet
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Likelihood-based inference for skewed count distributions used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis.
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2025-03-25 |
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r-vcdextra
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Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data and the 'gnm' package for Generalized Nonlinear Models. In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and adds a 3D version in 'mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of 'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer.
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2025-03-25 |
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r-tufte
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Provides R Markdown output formats to use Tufte styles for PDF and HTML output.
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2025-03-25 |
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r-tensorflow
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Interface to 'TensorFlow' <https://www.tensorflow.org/>, an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, server, or mobile device with a single 'API'. 'TensorFlow' was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
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2025-03-25 |
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r-rticles
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A suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.
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2025-03-25 |
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r-rematch2
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Wrappers on 'regexpr' and 'gregexpr' to return the match results in tidy data frames.
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2025-03-25 |
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r-rarpack
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Previously an R wrapper of the 'ARPACK' library <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the R package 'RSpectra', an R interface to the 'Spectra' library <http://yixuan.cos.name/spectra/> for solving large scale eigenvalue/vector problems. The current version of 'rARPACK' simply imports and exports the functions provided by 'RSpectra'. New users of 'rARPACK' are advised to switch to the 'RSpectra' package.
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2025-03-25 |
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r-psychotree
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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.
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2025-03-25 |
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r-pre
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Derives prediction rule ensembles (PREs). Largely follows the procedure for deriving PREs as described in Friedman & Popescu (2008; <DOI:10.1214/07-AOAS148>), with adjustments and improvements. The main function pre() derives prediction rule ensembles consisting of rules and/or linear terms for continuous, binary, count, multinomial, and multivariate continuous responses. Function gpe() derives generalized prediction ensembles, consisting of rules, hinge and linear functions of the predictor variables.
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2025-03-25 |
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r-plumber
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Gives the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions.
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2025-03-25 |
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r-pinp
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A 'PNAS'-alike style for 'rmarkdown', derived from the 'Proceedings of the National Academy of Sciences of the United States of America' ('PNAS', see <https://www.pnas.org>) 'LaTeX' style, and adapted for use with 'markdown' and 'pandoc'.
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2025-03-25 |
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r-mlmrev
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Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.
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2025-03-25 |
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r-memss
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Data sets and sample analyses from Pinheiro and Bates, "Mixed-effects Models in S and S-PLUS" (Springer, 2000).
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2025-03-25 |
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r-gamm4
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Estimate generalized additive mixed models via a version of function gamm() from 'mgcv', using 'lme4' for estimation.
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2025-03-25 |
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r-umap
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Uniform manifold approximation and projection is a technique for dimension reduction. The algorithm was described by McInnes and Healy (2018) in <arXiv:1802.03426>. This package provides an interface for two implementations. One is written from scratch, including components for nearest-neighbor search and for embedding. The second implementation is a wrapper for 'python' package 'umap-learn' (requires separate installation, see vignette for more details).
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2025-03-25 |
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r-tam
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Includes marginal maximum likelihood estimation and joint maximum likelihood estimation for unidimensional and multidimensional item response models. The package functionality covers the Rasch model, 2PL model, 3PL model, generalized partial credit model, multi-faceted Rasch model, nominal item response model, structured latent class model, mixture distribution IRT models, and located latent class models. Latent regression models and plausible value imputation are also supported. For details see Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>, Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>, Formann, 1982 <doi:10.1002/bimj.4710240209>, Formann, 1992 <doi:10.1080/01621459.1992.10475229>.
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2025-03-25 |
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r-np
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Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <http://www.nserc-crsng.gc.ca>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <http://www.sshrc-crsh.gc.ca>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <http://www.sharcnet.ca>).
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2025-03-25 |
