Detection of spatial structure from genetic data.

Simulation and MCMC inference in landscape genetics
Detection of spatial structure from geo-referenced genetic data.

Arnaud Estoup, Gilles Guillot, Filipe Santos

Simulation and MCMC inference for subdivided populations
Detection of structure from multilocus genetic data.

Simulation and MCMC inference of structure from genetic data

Stochastic simulation and MCMC inference of structure from
genetic (optionnaly georeferenced) data

Stochastic simulation and MCMC inference of structure from
genetic data.
Gilles Guillot <gigu@imm.dtu.dk> Filipe Santos
<filipe.santos@mail.icav.up.pt>

Gilles Guillot <gigu@imm.dtu.dk> , Filipe Santos
<filipe.santos@mail.icav.up.pt>

Gilles Guillot, Filipe Santos, Arnaud Estoup

Stochastic simulation and MCMC inference of structure from genetic data.

Detection of Structure from Multilocus Genetic Data

Blinder-Oaxaca Decomposition for Generalized Linear Model
Aurelien Nicosia and Simon Baillargeon-Ladouceur
Perform the Blinder-Oaxaca decomposition for generalized linear
model with bootstrapped standard errors. The twofold and threefold
decomposition are given, even the generalized linear model output in each group.

person("Anton", "Bossenbroek", email = "anton.bossenbroek@me.com", role = c("aut", "cre"))
A general tree data structure implementation in R.
testthat, covr, knitr, DiagrammeR, iterators, foreach, tools,
rmarkdown, microbenchmark, doParallel

Test for Conditional Independence Based on the Generalized
Covariance Measure (GCM)
A statistical hypothesis test for conditional independence. It performs nonlinear regressions on the conditioning variable and then tests for a vanishing covariance between the resulting residuals. It can be applied to both univariate random variables and multivariate random vectors. Details of the method can be found in Rajen D. Shah and Jonas Peters (2018) <arXiv:1804.07203>.
CVST, graphics, kernlab, mgcv, stats, xgboost

A statistical hypothesis test for conditional independence. It performs nonlinear regressions on the conditioning variable and then tests for a vanishing covariance between the resulting residuals. It can be applied to both univariate random variables and multivariate random vectors. Details of the method can be found in Rajen D. Shah and Jonas Peters: The Hardness of Conditional Independence Testing and the Generalised Covariance Measure, Annals of Statistics 48(3), 1514--1538, 2020.