Computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed. See Taylor Arnold and Ryan Tibshirani (2016) <doi:10.1080/10618600.2015.1008638>.

Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations. See: "GENLIB: an R package for the analysis of genealogical data" Gauvin et al. (2015) <doi:10.1186/s12859-015-0581-5>.

Provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) <https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>. It also has an interactive 'RStudio' plot for better guessing dynamically of initial values for ease of included optimization and simulating.

Provides routines to estimate the Mixture Transition Distribution Model based on Raftery (1985) <http://www.jstor.org/stable/2345788> and Nicolau (2014) <doi:10.1111/sjos.12087> specifications, for multivariate data. Additionally, provides a  function for the estimation of a new model for multivariate non-homogeneous Markov chains. This new specification, Generalized Multivariate Markov Chains (GMMC) was proposed by Carolina Vasconcelos and Bruno Damasio and considers (continuous or discrete) covariates exogenous to the Markov chain.

Trace plots and convergence diagnostics for Markov Chain Monte Carlo (MCMC) algorithms on highly multivariate or unordered spaces. Methods outlined in a forthcoming paper.

Generalized meta-analysis is a technique for estimating parameters associated with a multiple regression model through meta-analysis of studies which may have information only on partial sets of the regressors. It estimates the effects of each variable while fully adjusting for all other variables that are measured in at least one of the studies. Using algebraic relationships between regression parameters in different dimensions, a set of moment equations is specified for estimating the parameters of a maximal model through information available on sets of parameter estimates from a series of reduced models available from the different studies. The specification of the equations requires a reference dataset to estimate the joint distribution of the covariates. These equations are solved using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. The proposed framework is implemented using iterated reweighted least squares algorithm for fitting generalized linear regression models. For more details about the method, please see pre-print version of the manuscript on generalized meta-analysis by Prosenjit Kundu, Runlong Tang and Nilanjan Chatterjee (2018) <doi:10.1093/biomet/asz030>.The current version (0.2.0) is updated to address some of the stability issues in the previous version (0.1).