Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.

A Tcl/Tk GUI for some basic functions in the 'ade4' package.

The meaning of adea is "alternate DEA". This package is devoted to provide the alternative method of DEA described in the paper entitled "Stepwise Selection of Variables in DEA Using Contribution Load", by F. Fernandez-Palacin, M. A. Lopez-Sanchez and M. Munoz-Marquez. Pesquisa Operacional 38 (1), pg. 1-24, 2018. <doi:10.1590/0101-7438.2018.038.01.0031>.
    A full functional on-line and interactive version is available at
    <https://knuth.uca.es/shiny/DEA/>.

Toolset for the exploration of genetic and genomic
    data. Adegenet provides formal (S4) classes for storing and handling
    various genetic data, including genetic markers with varying ploidy
    and hierarchical population structure ('genind' class), alleles counts
    by populations ('genpop'), and genome-wide SNP data ('genlight'). It
    also implements original multivariate methods (DAPC, sPCA), graphics,
    statistical tests, simulation tools, distance and similarity measures,
    and several spatial methods. A range of both empirical and simulated
    datasets is also provided to illustrate various methods.

Graphical functionalities for the representation of multivariate data. It is a complete re-implementation of the functions available in the 'ade4' package.

A collection of tools for the estimation of animals home range.

A collection of tools for the analysis of habitat selection.

A collection of tools for the analysis of animal movements.

A collection of tools to deal with raster maps.

Extremely efficient procedures for fitting the entire group lasso and group elastic net regularization path for GLMs, multinomial, the Cox model and multi-task Gaussian models. Similar to the R package 'glmnet' in scope of models, and in computational speed.  This package provides  R bindings to the C++ code underlying the corresponding Python package 'adelie'. These bindings offer a general purpose group elastic net solver, 
    a wide range of matrix classes that can exploit special structure 
    to allow large-scale inputs, and an assortment of 
    generalized linear model classes for fitting various types of data. 
    The package is an implementation of Yang, J. and Hastie, T. (2024) <doi:10.48550/arXiv.2405.08631>.