Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.

This package implements a computational framework for a pattern-based, zoneless analysis, and visualization of (ethno)racial topography (Dmowska, Stepinski, and Nowosad (2020) <doi:10.1016/j.apgeog.2020.102239>). It is a reimagined approach for analyzing residential segregation and racial diversity based on the concept of landscapeâ used in the domain of landscape ecology.

Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the validate package. The core algorithms have recently been moved to the lintools package, refer to lintools for a more basic interface and access to a version of the algorithm that works with sparse matrices.

This package provides functions and examples for testing hypothesis about the population mean and variance on samples drawn by r-size biased sampling schemes.

Set of tools to manipulate the JDemetra+ workspaces. Based on the RJDemetra package (which interfaces with version 2 of the JDemetra+ (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially recommended to the members of the European Statistical System (ESS) and the European System of Central Banks). This package provides access to additional workspace manipulation functions such as metadata manipulation, raw paths and wrangling of several workspaces simultaneously. These additional functionalities are useful as part of a CVS data production chain.

This package implements the Simulating Optimal FUNctioning framework for site-scale simulations of ecosystem processes, including model calibration. It contains Fortran 90 modules for the P-model (Stocker et al. (2020) <doi:10.5194/gmd-13-1545-2020>), SPLASH (Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>) and BiomeE (Weng et al. (2015) <doi:10.5194/bg-12-2655-2015>).

The A() function calculates the A statistic, a nonparametric measure of effect size for two independent groups thatâ s also known as the probability of superiority (Ruscio, 2008), along with its standard error and a confidence interval constructed using bootstrap methods (Ruscio & Mullen, 2012). Optional arguments can be specified to calculate variants of the A statistic developed for other research designs (e.g., related samples, more than two independent groups or related samples; Ruscio & Gera, 2013). <DOI: 10.1037/1082-989X.13.1.19>. <DOI: 10.1080/00273171.2012.658329>. <DOI: 10.1080/00273171.2012.738184>.

This package provides tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data (Goolsby et al. 2017). Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.

It is devoted to the IVIVC linear level A with numerical deconvolution method. The latter is working for inequal and incompatible timepoints between impulse and response curves. A numerical convolution method is also available. Application domains include pharamaceutical industry QA/QC and R&D together with academic research.

This package provides an infrastructure for handling multiple R Markdown reports, including automated curation and time-stamping of outputs, parameterisation and provision of helper functions to manage dependencies.

Tskit enables efficient storage, manipulation, and analysis of ancestral recombination graphs (ARGs) using succinct tree sequence encoding. The tree sequence encoding of an ARG is described in Wong et al. (2024) <doi:10.1093/genetics/iyae100>, while `tskit` project is described in Jeffrey et al. (2026) <doi:10.48550/arXiv.2602.09649>. See also <https://tskit.dev> for project news, documentation, and tutorials. Tskit provides Python', C', and Rust application programming interfaces (APIs). The Python API can be called from R via the reticulate package to load and analyse tree sequences as described at <https://tskit.dev/tutorials/tskitr.html>. RcppTskit provides R access to the tskit C API for cases where the reticulate option is not optimal; for example, high-performance or low-level work with tree sequences. Currently, RcppTskit provides a limited set of R functions because the Python API and reticulate already covers most needs.

Simplify the process of extracting and processing Clinical Practice Research Datalink (CPRD) data in order to build datasets ready for statistical analysis. This process is difficult in R', as the raw data is very large and cannot be read into the R workspace. rcprd utilises RSQLite to create SQLite databases which are stored on the hard disk. These are then queried to extract the required information for a cohort of interest, and create datasets ready for statistical analysis. The processes follow closely that from the rEHR package, see Springate et al., (2017) <doi:10.1371/journal.pone.0171784>.

This package implements distance based probability models for ranking data. The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance, Weighted-tau distance and Weighted Kendall distance. Phi-component model and mixture models are also supported.

Read Acoustic HAC format.

Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cut-point is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a three-layer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning.

This package provides functions and datasets required for the ST 370 course at North Carolina State University.

This package provides a method generate() is implemented in this package for the random generation of vector time series according to models obtained by RMAWGEN', vars or other packages. This package was created to generalize the algorithms of the RMAWGEN package for the analysis and generation of any environmental vector time series.

Plots multiple run charts, finds successive signals of improvement, and revises medians when each signal occurs. Finds runs above, below, or on both sides of the median, and returns a plot and a data.table summarising original medians and any revisions, for all groups within the supplied data.

Build regular expressions piece by piece using human readable code. This package is designed for interactive use. For package development, use the rebus.* dependencies.

Get your data (forms, structures, answers) from Coletum <https://coletum.com> to handle and analyse.

Connection to the Redis (or Valkey') key/value store using the C-language client library hiredis (included as a fallback) with MsgPack encoding provided via RcppMsgPack headers. It now also includes the pub/sub functions from the rredis package.

R Commander plug-in to demonstrate various actuarial and financial risks. It includes valuation of bonds and stocks, portfolio optimization, classical ruin theory, demography and epidemic.

Computationally efficient tool for performing variable selection and obtaining robust estimates, which implements robust variable selection procedure proposed by Wang, X., Jiang, Y., Wang, S., Zhang, H. (2013) <doi:10.1080/01621459.2013.766613>. Users can enjoy the near optimal, consistent, and oracle properties of the procedures.

This package provides a set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.