Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling.  Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.

An update to the Joint Location-Scale (JLS) testing framework that identifies associated SNPs, gene-sets and pathways with main and/or interaction effects on quantitative traits (Soave et al., 2015; <doi:10.1016/j.ajhg.2015.05.015>). The JLS method simultaneously tests the null hypothesis of equal mean and equal variance across genotypes, by aggregating association evidence from the individual location/mean-only and scale/variance-only tests using Fisher's method. The generalized joint location-scale (gJLS) framework has been developed to deal specifically with sample correlation and group uncertainty (Soave and Sun, 2017; <doi:10.1111/biom.12651>). The current release: gJLS2, include additional functionalities that enable analyses of X-chromosome genotype data through novel methods for location (Chen et al., 2021; <doi:10.1002/gepi.22422>) and scale (Deng et al., 2019; <doi:10.1002/gepi.22247>).

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association.

Data sets used in the book Marra and Radice (2025, ISBN:9781032973111) "Copula Additive Distributional Regression Using R", for illustrating the fitting of various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association.

Functions for the g-and-k and generalised g-and-h distributions.

Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.

A simple way to interact with and extract data from
  the official 'Google Knowledge Graph' API <https://developers.google.com/knowledge-graph/>.