Authors@R: c(person("Irene SL","Zeng",email="i.zeng@auckland.ac.nz", role = c("aut", "cre")),person("Thomas","Lumley",email="t.lumley@auckland.ac.nz",role="ctb"))
Description: To conduct Bayesian inference regression for responses with
multilevel explanatory variables and missing values; It uses function from
'Stan', a software to implement posterior sampling using Hamiltonian MC and
its variation Non-U-Turn algorithms. It implements the posterior sampling of
regression coefficients from the multilevel regression models. The package has
two main functions to handle not-missing-at-random missing responses and left-
censored with not-missing-at random responses. The purpose is to provide a
similar format as the other R regression functions but using 'Stan' models.
Depends: R (>= 3.3.0), Rcpp (>= 0.12.12), methods, stats

Author: Fanny Bonnafous [aut], Olivier Guillaume[aut, cre], Brigitte Mangin [aut], Prune Pegot-Espagnet [aut], Vincent Segura [aut], Bjarni J. Vilhjalmsson [aut]
Description: Pipeline for Genome-Wide Association Study using Multi-Locus Mixed Model from Segura V, Vilhjálmsson BJ et al. (2012) <doi:10.1038/ng.2314>. The pipeline include detection of associated SNPs with MLMM, model selection by lowest eBIC, estimation of the effects of the SNPs in the selected model and graphical functions.
Imports: multcompView(>= 0.1-7), multcomp(>= 1.4-8), coxme(>= 2.2-5),
Packaged: 2018-03-28 13:42:26 UTC; olguillaume

Packaged: 2018-05-04 11:08:38 UTC; olguillaume

Author: Fanny Bonnafous [aut], Alexandra Duhnen [aut], Louise Gody [aut], Olivier Guillaume[aut, cre], Brigitte Mangin [aut], Prune Pegot-Espagnet [aut], Vincent Segura [aut], Bjarni J. Vilhjalmsson [aut]
Packaged: 2018-07-19 12:37:48 UTC; olguillaume

Author: Fanny Bonnafous [aut], Alexandra Duhnen [aut], Louise Gody [aut], Olivier Guillaume[aut, cre], Brigitte Mangin [aut], Prune Pegot-Espagnet [aut], Vincent Segura [aut], Bjarni J. Vilhjalmsson [aut], Clement Mabire [aut]
Description: Pipeline for Genome-Wide Association Study using Multi-Locus Mixed Model from Segura V, Vilhjálmsson BJ et al. (2012) <doi:10.1038/ng.2314>. The pipeline include detection of associated SNPs with MLMM, model selection by lowest eBIC and markers selection by p-value threshold, estimation of the effects of the SNPs in the selected model and graphical functions.