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>.

Provided data containing an outcome variable, compositional variables and additional covariates (optional); linearly regress the outcome variable on an isometric log ratio (ilr) transformation of the linearly dependent compositional variables. The package provides predictions (with confidence intervals) in the change (delta) in the outcome/response variable based on the multiple linear regression model and evenly spaced reallocations of the compositional values. The compositional data analysis approach implemented is outlined in Dumuid et al. (2017a) <doi:10.1177/0962280217710835> and Dumuid et al. (2017b) <doi:10.1177/0962280217737805>.

Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>.