mlmm: Multilevel Model for Multivariate Responses with Missing Values

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.

Version: 1.0
Depends: R (≥ 3.0.2), Rcpp (≥ 0.12.12), methods, stats
Imports: rstan (≥ 2.16.2), rstantools (≥ 1.3.0), MASS, Matrix, stats4, ggplot2
LinkingTo: StanHeaders (≥ 2.16.0.1), rstan (≥ 2.16.2), BH (≥ 1.65.0.1), Rcpp (≥ 0.12.12), RcppEigen (≥ 0.3.3.3.0)
Suggests: testthat
Published: 2017-11-02
Author: Irene SL Zeng [aut, cre], Thomas Lumley [ctb], Trustees Columbia-University [cph]
Maintainer: Irene SL Zeng <i.zeng at auckland.ac.nz>
License: GPL-2
URL: https://doi.org/10.1101/153049
NeedsCompilation: yes
CRAN checks: mlmm results

Downloads:

Reference manual: mlmm.pdf
Package source: mlmm_1.0.tar.gz
Windows binaries: r-devel: mlmm_1.0.zip, r-release: mlmm_1.0.zip, r-oldrel: mlmm_1.0.zip
OS X El Capitan binaries: r-release: not available
OS X Mavericks binaries: r-oldrel: not available

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