Title: Simulation and MCMC inference of structure from genetic data
Packaged: Thu Sep 25 16:02:50 2008; gilles

Packaged: Mon Sep 29 14:59:39 2008; gilles

Packaged: Mon Nov  3 17:25:34 2008; gilles

Packaged: Tue Nov 25 10:26:25 2008; gilles

Title: Detection of structure from multilocus genetic data.
Description: Stochastic simulation and MCMC inference of structure from

Maintainer: Gilles Guillot <gigu@imm.dtu.dk> Filipe Santos

Maintainer: Gilles Guillot <gigu@imm.dtu.dk> , Filipe Santos

Author: Gilles Guillot, Filipe Santos, Arnaud Estoup

Description: Stochastic simulation and MCMC inference of structure from genetic data.

Title: Detection of Structure from Multilocus Genetic Data

Title: Blinder-Oaxaca Decomposition for Generalized Linear Model
Author: Aurelien Nicosia and Simon Baillargeon-Ladouceur
Description: 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.
Packaged: 2015-08-16 22:11:14 UTC; aureliennicosia

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

Title: Test for Conditional Independence Based on the Generalized
Description: 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>.
Imports: CVST, graphics, kernlab, mgcv, stats, xgboost

Description: 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.