Multilevel Model for Multivariate Responses with Missing Values
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"),person("Trustees","Columbia-University",email="secretary@columbia.edu",role="cph"))
To conduct Bayesian inference regression for responses with multilevel explanatory variables and missing values(Zeng ISL (2017) <doi:10.1101/153049>). Functions utilizing 'Stan', a software to implement posterior sampling using Hamiltonian MC and its variation Non-U-Turn algorithms are generated and provided to implement 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.
rstan (>= 2.16.2), rstantools (>= 1.3.0), MASS, Matrix,
stats4, ggplot2
Irene SL Zeng [aut, cre],
Thomas Lumley [ctb],
Trustees Columbia-University [cph]

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"))
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.
Irene SL Zeng [aut, cre],
Thomas Lumley [ctb]

Fanny Bonnafous [aut], Olivier Guillaume[aut, cre], Brigitte Mangin [aut], Prune Pegot-Espagnet [aut], Vincent Segura [aut], Bjarni J. Vilhjalmsson [aut]
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.