Implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2021) <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>.

Generates all necessary C functions allowing the user to work with
    the compiled-code interface of ode() and bvptwp(). The implementation supports
    "forcings" and "events". Also provides functions to symbolically compute
    Jacobians, sensitivity equations and adjoint sensitivities being the basis for
    sensitivity analysis.

Easily automate the following tasks to describe data frames:
		Summarise the distributions, and labelled missings of variables graphically
		and using descriptive statistics.
		For surveys, compute and summarise reliabilities (internal consistencies, 
		retest, multilevel) for psychological scales.
		Combine this information with metadata (such as item labels and labelled 
		values) that is derived from R attributes.
		To do so, the package relies on 'rmarkdown' partials, so you can generate 
		HTML, PDF, and Word documents. 
		Codebooks are also available as tables (CSV, Excel, etc.) and in JSON-LD, so
		that search engines can find your data and index the metadata.
		The metadata are also available at your fingertips via RStudio Addins.