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r-smerc
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Implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering. The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and Kulldorff (1997) <doi:10.1080/03610929708831995>.
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2025-03-25 |
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r-slider
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Provides type-stable rolling window functions over any R data type. Cumulative and expanding windows are also supported. For more advanced usage, an index can be used as a secondary vector that defines how sliding windows are to be created.
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2025-03-25 |
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r-skpr
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Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.
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2025-03-25 |
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r-simstudy
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Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR).
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2025-03-25 |
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r-sirt
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Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), MCMC for hierarchical IRT models and testlet models (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>; Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, <doi:10.1111/j.1745-3984.2011.00143.x>), ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, <doi:10.1177/014662169602000403>), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, <doi:10.1080/00273171.2016.1142856>).
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2025-03-25 |
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r-showtext
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Making it easy to use various types of fonts ('TrueType', 'OpenType', Type 1, web fonts, etc.) in R graphs, and supporting most output formats of R graphics including PNG, PDF and SVG. Text glyphs will be converted into polygons or raster images, hence after the plot has been created, it no longer relies on the font files. No external software such as 'Ghostscript' is needed to use this package.
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2025-03-25 |
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r-shapr
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Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements the method described in Aas, Jullum and Løland (2019) <arXiv:1903.10464>, which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values.
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2025-03-25 |
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r-shinytest2
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Automated unit testing of Shiny applications through a headless 'Chromium' browser.
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2025-03-25 |
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r-sgd
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A fast and flexible set of tools for large scale estimation. It features many stochastic gradient methods, built-in models, visualization tools, automated hyperparameter tuning, model checking, interval estimation, and convergence diagnostics.
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2025-03-25 |
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r-seuratobject
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Defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. Provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, Hao Y, Hao S, et al (2021) <doi:10.1016/j.cell.2021.04.048> and Hao Y, et al (2023) <doi:10.1101/2022.02.24.481684> for more details.
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2025-03-25 |
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r-seurat
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A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
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2025-03-25 |
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r-sensitivity
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A collection of functions for sensitivity analysis of model outputs (factor screening, global sensitivity analysis and robustness analysis), as well as for interpretability of machine learning models. Most of the functions have to be applied on scalar output, but several functions support multi-dimensional outputs.
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2025-03-25 |
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r-seriation
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Infrastructure for ordering objects with an implementation of several seriation/sequencing/ordination techniques to reorder matrices, dissimilarity matrices, and dendrograms. Also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) <doi:10.18637/jss.v025.i03>.
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2025-03-25 |
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r-seqinr
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Exploratory data analysis and data visualization for biological sequence (DNA and protein) data. Seqinr includes utilities for sequence data management under the ACNUC system described in Gouy, M. et al. (1984) Nucleic Acids Res. 12:121-127 <doi:10.1093/nar/12.1Part1.121>.
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2025-03-25 |
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r-sem
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Functions for fitting general linear structural equation models (with observed and latent variables) using the RAM approach, and for fitting structural equations in observed-variable models by two-stage least squares.
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2025-03-25 |
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r-seededlda
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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.
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2025-03-25 |
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r-sdmtmb
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Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'INLA', and the SPDE (Stochastic Partial Differential Equation) approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2022) <doi:10.1101/2022.03.24.485545>.
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2025-03-25 |
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r-sdctable
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Methods for statistical disclosure control in tabular data such as primary and secondary cell suppression as described for example in Hundepol et al. (2012) <doi:10.1002/9781118348239> are covered in this package.
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2025-03-25 |
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r-sde
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Companion package to the book Simulation and Inference for Stochastic Differential Equations With R Examples, ISBN 978-0-387-75838-1, Springer, NY.
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2025-03-25 |
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r-sdchierarchies
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Provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats. These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.
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2025-03-25 |
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r-sdcmicro
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Data from statistical agencies and other institutions are mostly confidential. This package (see also Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v067.i04>) can be used for the generation of anonymized (micro)data, i.e. for the creation of public- and scientific-use files. The theoretical basis for the methods implemented can be found in Templ (2017) <doi:10.1007/978-3-319-50272-4>. Various risk estimation and anonymisation methods are included. Note that the package includes a graphical user interface (Meindl and Templ, 2019 <doi:10.3390/a12090191>) that allows to use various methods of this package.
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2025-03-25 |
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r-sctransform
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A normalization method for single-cell UMI count data using a variance stabilizing transformation. The transformation is based on a negative binomial regression model with regularized parameters. As part of the same regression framework, this package also provides functions for batch correction, and data correction. See Hafemeister and Satija (2019) <doi:10.1186/s13059-019-1874-1>, and Choudhary and Satija (2022) <doi:10.1186/s13059-021-02584-9> for more details.
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2025-03-25 |
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r-sccore
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Core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP <doi:10.21105/joss.00861>, collapsing vertices of each cluster in the graph, and propagating graph labels.
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2025-03-25 |
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r-scattermore
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C-based conversion of large scatterplot data to rasters plus other operations such as data blurring or data alpha blending. Speeds up plotting of data with millions of points.
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2025-03-25 |
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r-sarima
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Functions, classes and methods for time series modelling with ARIMA and related models. The aim of the package is to provide consistent interface for the user. For example, a single function autocorrelations() computes various kinds of theoretical and sample autocorrelations. This is work in progress, see the documentation and vignettes for the current functionality. Function sarima() fits extended multiplicative seasonal ARIMA models with trends, exogenous variables and arbitrary roots on the unit circle, which can be fixed or estimated (for the algebraic basis for this see <arXiv:2208.05055>, a paper on the methodology is being prepared). The suggested package 'FitARMA' can be installed with 'remotes::install_github("cran/FitARMA")' if necessary but is no longer needed in normal use.
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2025-03-25 |
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r-santoku
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A tool for cutting data into intervals. Allows singleton intervals. Always includes the whole range of data by default. Flexible labelling. Convenience functions for cutting by quantiles etc. Handles dates, times, units and other vectors.
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2025-03-25 |
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r-rxode2random
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Provides the random number generation (in parallel) needed for 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) and 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). This split will reduce computational burden of recompiling 'rxode2'.
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2025-03-25 |
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r-rxode2parse
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Provides the parsing needed for 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>). It also provides the 'stan' based advan linear compartment model solutions with gradients (Carpenter et al (2015), <arXiv:1509.07164>) needed in 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). This split will reduce computational burden of recompiling 'rxode2'.
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2025-03-25 |
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r-rxode2
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Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The 'VODE' and 'LSODA' are in the public domain. The information is available in the inst/COPYRIGHTS.
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2025-03-25 |
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r-rxode2ll
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Provides the log-likelihoods with gradients from 'stan' (Carpenter et al (2015), <arXiv:1509.07164>) needed for generalized log-likelihood estimation in 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is split of to reduce computational burden of recompiling 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs the 'nlmixr2' models during estimation.
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2025-03-25 |
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r-rxode2et
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Provides the event table and support functions needed for 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) and 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). This split will reduce computational burden of recompiling 'rxode2'.
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2025-03-25 |
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r-rwiener
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Provides Wiener process distribution functions, namely the Wiener first passage time density, CDF, quantile and random functions. Additionally supplies a modelling function (wdm) and further methods for the resulting object.
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2025-03-25 |
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r-rwave
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A set of R functions which provide an environment for the Time-Frequency analysis of 1-D signals (and especially for the wavelet and Gabor transforms of noisy signals). It was originally written for Splus by Rene Carmona, Bruno Torresani, and Wen L. Hwang, first at the University of California at Irvine and then at Princeton University. Credit should also be given to Andrea Wang whose functions on the dyadic wavelet transform are included. Rwave is based on the book: "Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S", by Rene Carmona, Wen L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press.
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2025-03-25 |
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r-rvinecopulib
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Provides an interface to 'vinecopulib', a C++ library for vine copula modeling. The 'rvinecopulib' package implements the core features of the popular 'VineCopula' package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker and more modern API, improved performances, especially in high dimensions, nonparametric and multi-parameter families, and the ability to model discrete variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only C++ library (currently version 0.6.2). Thus users do not need to install 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' is licensed under the GNU GPL version 3.
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2025-03-25 |
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r-rvg
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Vector Graphics devices for 'Microsoft PowerPoint' and 'Microsoft Excel'. Functions extending package 'officer' are provided to embed 'DrawingML' graphics into 'Microsoft PowerPoint' presentations and 'Microsoft Excel' workbooks.
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2025-03-25 |
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r-rust
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Uses the generalized ratio-of-uniforms (RU) method to simulate from univariate and (low-dimensional) multivariate continuous distributions. The user specifies the log-density, up to an additive constant. The RU algorithm is applied after relocation of mode of the density to zero, and the user can choose a tuning parameter r. For details see Wakefield, Gelfand and Smith (1991) <DOI:10.1007/BF01889987>, Efficient generation of random variates via the ratio-of-uniforms method, Statistics and Computing (1991) 1, 129-133. A Box-Cox variable transformation can be used to make the input density suitable for the RU method and to improve efficiency. In the multivariate case rotation of axes can also be used to improve efficiency. From version 1.2.0 the 'Rcpp' package <https://cran.r-project.org/package=Rcpp> can be used to improve efficiency.
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2025-03-25 |
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r-rugarch
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ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
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2025-03-25 |
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r-ruv
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Implements the 'RUV' (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>. The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms.
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2025-03-25 |
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r-rtop
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Data with irregular spatial support, such as runoff related data or data from administrative units, can with 'rtop' be interpolated to locations without observations with the top-kriging method. A description of the package is given by Skøien et al (2014) <doi:10.1016/j.cageo.2014.02.009>.
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2025-03-25 |
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r-rtexttools
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A machine learning package for automatic text classification that makes it simple for novice users to get started with machine learning, while allowing experienced users to easily experiment with different settings and algorithm combinations. The package includes eight algorithms for ensemble classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks), comprehensive analytics, and thorough documentation.
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2025-03-25 |
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r-rtdists
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Provides response time distributions (density/PDF, distribution function/CDF, quantile function, and random generation): (a) Ratcliff diffusion model (Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C code by Andreas and Jochen Voss and (b) linear ballistic accumulator (LBA; Brown & Heathcote, 2008, <doi:10.1016/j.cogpsych.2007.12.002>) with different distributions underlying the drift rate.
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2025-03-25 |
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r-rstpm2
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R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.
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2025-03-25 |
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r-rstanarm
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Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.
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2025-03-25 |
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r-rssa
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Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series. General description of the methods with many examples can be found in the book Golyandina (2018, <doi:10.1007/978-3-662-57380-8>). See 'citation("Rssa")' for details.
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2025-03-25 |
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r-rspm
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Enables binary package installations on Linux distributions. Provides access to 'RStudio' public repositories at <https://packagemanager.rstudio.com>, and transparent management of system requirements without administrative privileges. Currently supported distributions are 'CentOS' / 'RHEL' 7-9, and several 'RHEL' derivatives ('Rocky Linux', 'AlmaLinux', 'Oracle Linux', and 'Amazon Linux' 2), 'openSUSE' / 'SLES' 15.3-4, and 'Ubuntu' 18.04, 20.04 and 22.04.
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2025-03-25 |
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r-rsomoclu
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Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) <doi:10.18637/jss.v078.i09>.
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2025-03-25 |
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r-rsparse
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Implements many algorithms for statistical learning on sparse matrices - matrix factorizations, matrix completion, elastic net regressions, factorization machines. Also 'rsparse' enhances 'Matrix' package by providing methods for multithreaded <sparse, dense> matrix products and native slicing of the sparse matrices in Compressed Sparse Row (CSR) format. List of the algorithms for regression problems: 1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>) 2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>) List of algorithms for matrix factorization and matrix completion: 1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, <doi:10.1145/1102351.1102441>) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, <arXiv:1410.2596>) 4) Linear-Flow matrix factorization, from 'Practical linear models for large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4) 5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, Socher, Manning (2014, <https://aclanthology.org/D14-1162/>) Package is reasonably fast and memory efficient - it allows to work with large datasets - millions of rows and millions of columns. This is particularly useful for practitioners working on recommender systems.
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2025-03-25 |
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r-rsghb
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Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.
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2025-03-25 |
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r-rrapply
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The minimal 'rrapply'-package contains a single function rrapply(), providing an extended implementation of 'R'-base rapply() by allowing to recursively apply a function to elements of a nested list based on a general condition function and including the possibility to prune or aggregate nested list elements from the result. In addition, special arguments can be supplied to access the name, location, parents and siblings in the nested list of the element under evaluation. The rrapply() function builds upon rapply()'s native 'C' implementation and requires no other package dependencies.
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2025-03-25 |
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r-rseis
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Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms. Includes general time-series tools, plotting, filtering, interactive display.
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2025-03-25 |
