Estimate sample sizes needed to capture target levels of
    genetic diversity from a population (multivariate allele frequencies)
    for applications like germplasm conservation and breeding efforts.
    Compares bootstrap samples to a full population using linear
    regression, employing the R-squared value to represent the proportion
    of diversity captured. Iteratively increases sample size until a
    user-defined target R-squared is met. Offers a parallelized R
    implementation of a previously developed 'python' method. All ploidy
    levels are supported. For more details, see Sandercock et al. (2024) <doi:10.1073/pnas.2403505121>.

Efficient phylogenetic analyses on massive phylogenies comprising up to millions of tips. Functions include pruning, rerooting, calculation of most-recent common ancestors, calculating distances from the tree root and calculating pairwise distances. Calculation of phylogenetic signal and mean trait depth (trait conservatism), ancestral state reconstruction and hidden character prediction of discrete characters, simulating and fitting models of trait evolution, fitting and simulating diversification models, dating trees, comparing trees, and reading/writing trees in Newick format. Citation: Louca, Stilianos and Doebeli, Michael (2017) <doi:10.1093/bioinformatics/btx701>.

Performs analysis of categorical-variable with missing values. Implements methods from Schafer, JL, Analysis of Incomplete Multivariate Data, Chapman and Hall.

Unifying an inconsistently coded categorical variable between two different time points in accordance with a mapping table. 
    The main rule is to replicate the observation if it could be assigned to a few categories. 
    Then using frequencies or statistical methods to approximate the probabilities of being assigned to each of them. 
    This procedure was invented and implemented in the paper by Nasinski, Majchrowska, and Broniatowska (2020) <doi:10.24425/cejeme.2020.134747>.

Package contains functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran's Q test, McNemar's test, and Penalty-Lift analysis are provided; for details, see Meyners, Castura & Carr (2013) <doi:10.1016/j.foodqual.2013.06.010>. Cluster analysis can be performed using b-cluster analysis, then evaluated using various measures; for details, see Castura, Meyners, Varela & Næs (2022) <doi:10.1016/j.foodqual.2022.104564>. Consumers can also be clustered on their product-related hedonic responses; see Castura, Meyners, Pohjanheimo, Varela & Næs (2023) <doi:10.1111/joss.12860>. Permutation tests based on the L1-norm methods are provided; for details, see Chaya, Castura & Greenacre (2025) <doi:10.1016/j.foodqual.2025.105639>.