Analysis, imputation, and multiple imputation of count data using covariates. LORI uses a log-linear Poisson model where main row and column effects, as well as effects of known covariates and interaction terms can be fitted. The estimation procedure is based on the convex optimization of the Poisson loss penalized by a Lasso type penalty and a nuclear norm. LORI returns estimates of main effects, covariate effects and interactions, as well as an imputed count table. The package also contains a multiple imputation procedure. The methods are described in Robin, Josse, Moulines and Sardy (2019) <doi:10.1016/j.jmva.2019.04.004>.

Provides a robust collection of functions tailored for microbial ecology analysis, encompassing both data analysis and visualization. It introduces an encapsulation feature that streamlines the process into a summary object. With the initial configuration of this summary object, users can execute a wide range of analyses with a single line of code, requiring only two essential parameters for setup. The package delivers comprehensive outputs including analysis objects, statistical outcomes, and visualization-ready data, enhancing the efficiency of research workflows. Designed with user-friendliness in mind, it caters to both novices and seasoned researchers, offering an intuitive interface coupled with adaptable customization options to meet diverse analytical needs.

Functions for simulating missing morphometric
	data randomly, with taxonomic bias and with anatomical bias. LOST also 
	includes functions for estimating linear and geometric morphometric data.

Provides a simple mechanism to specify a symmetric block
    diagonal matrices (often used for covariance matrices).  This is based
    on the domain specific language implemented in 'nlmixr2' but expanded
    to create matrices in R generally instead of specifying parts of
    matrices to estimate. It has expanded to include some matrix manipulation
    functions that are generally useful for 'rxode2' and 'nlmixr2'.