The companion package provides all original data sets and functions that are used in the book "Model-Based Clustering and Classification for Data Science" by Charles Bouveyron, Gilles Celeux, T. Brendan Murphy and Adrian E. Raftery (2019, ISBN:9781108644181).

Data-driven approach for Exploratory Factor Analysis (EFA) that uses Model Implied Instrumental Variables (MIIVs). The method starts with a one factor model and arrives at a suggested model with enhanced interpretability that allows cross-loadings and correlated errors.

This package provides methods for determining optimum plot size and shape in field experiments using Fairfield-Smith's variance law approach. It will evaluate field variability, determine optimum plot size and shape and study fertility trends across the field.

This package provides a collection of miscellaneous functions for passive acoustics. Much of the content here is adapted to R from code written by other people. If you have any ideas of functions to add, please contact Taiki Sakai.

This package provides tools to process legacy format summary redistricting data files produced by the United States Census Bureau pursuant to P.L. 94-171. These files are generally available earlier but are difficult to work with as-is.

Data from All the World's Primates relational SQL database and other tabular datasets are made available via drivers and connection functions. Additionally we provide several functions and examples to facilitate the merging and aggregation of these tabular inputs.

Execute multi-step SQL workflows by leveraging specially formatted comments to define and control execution. This enables users to mix queries, commands, and metadata within a single script. Results are returned as named objects for use in downstream workflows.

This package performs random-effect multiple interval mapping (REMIM) in full-sib families of autopolyploid species based on restricted maximum likelihood (REML) estimation and score statistics, as described in Pereira et al. (2020) <doi:10.1534/genetics.120.303080>.

This package implements the methodological developments found in Hermes (2025) <doi:10.48550/arXiv.2503.02786>, and allows for the statistical modeling of data consisting of multiple users that provide an ordinal rating for one or multiple items.

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.

We provide functions for estimation and inference of locally-stationary time series using the sieve methods and bootstrapping procedure. In addition, it also contains functions to generate Daubechies and Coiflet wavelet by Cascade algorithm and to process data visualization.

Generate objects that simulate survival times. Random values for the distributions are generated using the method described by Bender (2003) <https://epub.ub.uni-muenchen.de/id/eprint/1716> and Leemis (1987) in Operations Research, 35(6), 892â 894.

Core parts of the C API of R are wrapped in a C++ namespace via a set of inline functions giving a tidier representation of the underlying data structures and functionality using a header-only implementation without additional dependencies.

This package implements D-vine quantile regression models with parametric or nonparametric pair-copulas. See Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and Schallhorn et al. (2017) <doi:10.48550/arXiv.1705.08310>.

This package provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers.

This package implements transcript quantification import from Salmon and alevin with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility.

This package provides an integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. Currently only Affymetrix oligonucleotide analysis is supported.

PSIplot is an R package for generating plots of percent spliced-in (PSI) values of alternatively-spliced exons that were computed by vast-tools, an RNA-Seq pipeline for alternative splicing analysis. The plots are generated using ggplot2.

This package enables you to estimate the p-values for predictors x against target variable y in Lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic.

Webshot makes it easy to take screenshots of web pages from within R. It can also run Shiny applications locally and take screenshots of the application; and it can render and screenshot static as well as interactive R Markdown documents.

This package provides a collection of functions to compute the standardized effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). The computation algorithms have been optimized to allow efficient computation even with very large data sets.

The lattice package provides a powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements.

This package provides functionality to assert conditions that have to be met so that errors in data used in analysis pipelines can fail quickly. It is similar to stopifnot() but more powerful, friendly, and easier for use in pipelines.

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.