A minimum set of functions to perform compositional data analysis
    using the log-ratio approach introduced by John Aitchison (1982). Main functions
    have been implemented in c++ for better performance.

Provides a collection of easy-to-use functions for creating 
    visualizations of compositional data using 'ggplot2'. Includes support 
    for common plotting techniques in compositional data analysis.

Functions for microbiome data analysis that take into account its compositional nature. Performs variable selection through penalized regression for both, cross-sectional and longitudinal studies, and for binary and continuous outcomes.

In the context of high-throughput genetic data,
    CoDaCoRe identifies a set of sparse biomarkers that are
    predictive of a response variable of interest (Gordon-Rodriguez 
    et al., 2021) <doi:10.1093/bioinformatics/btab645>. More 
    generally, CoDaCoRe can be applied to any regression problem 
    where the independent variable is Compositional (CoDa), to 
    derive a set of scale-invariant log-ratios (ILR or SLR) that 
    are maximally associated to a dependent variable.

Provides methods for interpreting CoDa (Compositional Data) regression models along the lines of "Pairwise share ratio interpretations of compositional regression models" (Dargel and Thomas-Agnan 2024) <doi:10.1016/j.csda.2024.107945>.
    The new methods include variation scenarios, elasticities, elasticity differences and share ratio elasticities.
    These tools are independent of log-ratio transformations and allow an interpretation in the original space of shares.
    'CoDaImpact' is designed to be used with the 'compositions' package and its ecosystem.

Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2022) <doi:10.1111/biom.13465>
    for transformation-free linear regression for compositional outcomes and predictors.

Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martí et al (2018, ISBN:978-84-09-07541-6), Creus-Martí et al (2021) <doi:10.1155/2021/9951817> and Creus-Martí et al (2022) <doi:10.1155/2022/4907527>.