Perform gene set enrichment analyses using the Gene set Ordinal 
    Association Test (GOAT) algorithm and  visualize your 
    results. Koopmans, F. (2024) <doi:10.1101/2023.12.10.570979>.

Supports the assessment of functional enrichment analyses obtained for several lists of genes and provides a workflow to analyze them between two species via weighted graphs. Methods are described in Sosa et al. (2023) <doi:10.1016/j.ygeno.2022.110528>.

A collection of functions useful in (vegetation) community analyses and ordinations. Includes automatic species selection for ordination diagrams, NMDS stress/scree plots, species response curves, merging of taxa as well as calculation and sorting of synoptic tables.

Divide and conquer approach for estimating low-rank and sparse coefficient matrix in the generalized co-sparse factor regression. Please refer the manuscript 'Mishra, Aditya, Dipak K. Dey, Yong Chen, and Kun Chen. Generalized co-sparse factor regression. Computational Statistics & Data Analysis 157 (2021): 107127' for more details.

A post-estimation method for categorical response models (CRM). 
    Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(), 
    vglm() and glm() are currently supported. Available tests include the 
    Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic 
    regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal 
    models. The proportional odds, adjacent-category, and constrained continuation-ratio 
    models are particularly supported at ordinal level. Tests for the proportional 
    odds assumptions in ordinal models are also possible with the Brant and the 
    Likelihood-Ratio tests. Moreover, several summary measures of predictive strength 
    (Pseudo R-squared), and some useful error metrics, including, the brier 
    score, misclassification rate and logloss are also available for the 
    binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018) 
    <http://www.statmod.org/workshops_archive_proceedings_2018.html>.