Title: Fixed effects Selection in Linear Mixed Models
Description: Fixed effects Selection in Linear Mixed Models

Title: Fixed Effects Selection in Linear Mixed Models
Description: Perform Fixed effects Selection in Linear Mixed Models using a multicycle ECM algorithm; see F. Rohart & al, 2014, <doi:10.1016/j.csda.2014.06.022>.

Description: The datasets from Modern Multivariate Statistical
Techniques by Alan Julian Izenman are contained in this
package.  The documentation descriptions show the page numbers
of references to the data set within the text.  See the text
for detailed descriptions of the datasets.  Also included in
this package is a function for exporting these datasets en

Title: Missing Multivariate Bayesian Variable Selection
Description: A variable selection tool for multivariate normal variables with missing-at-random values using Bayesian Hierarchical Model.
Visualization functions show the posterior distribution of gamma (inclusion variables) and beta (coefficients).
Users can also visualize the heatmap of the posterior mean of covariance matrix.
Kim, T. Nicolae, D. (2019) <https://github.com/tk382/MMVBVS/blob/master/workingpaper.pdf>.
Imports: Rcpp, reshape, reshape2, ggplot2, rlang

Title: Convert Dates to MMWR Day, Week, and Year
Description: The first day of any MMWR week is Sunday.
MMWR week numbering is sequential beginning with 1
and incrementing with each week to a maximum of 52
or 53. MMWR week #1 of an MMWR year is the first week
of the year that has at least four days in the calendar
year. This package provides functionality to convert
Dates to MMWR day, week, and year and the reverse.

Description: Analyzing data under multivariate mixed effects model using multivariate REML and multivariate Henderson3 methods.
Imports: stats, MASS, Matrix, jointDiag, lme4, matrixcalc, psych,
Packaged: 2019-01-25 18:16:00 UTC; pengluyao

Packaged: 2021-09-07 15:16:34 UTC; pengluyao

Title: Clustering Data with Non-Ignorable Missingness using
Description: Clustering of data under a non-ignorable missingness mechanism. Clustering is achieved by a semi-parametric mixture model and missingness is managed by using the pattern-mixture approach. More details of the approach are available in Du Roy de Chaumaray et al. (2020) <arXiv:2009.07662>.
Authors@R: c(person(given="Marie", family="Du Roy de Chaumaray", role = "aut"),
person(given="Matthieu", family="Marbac", role = c("aut", "cre", "cph"), email = "matthieu.marbac-lourdelle@ensai.fr"))
Author: Marie Du Roy de Chaumaray [aut],
Collate: algoCat.R algoCont.R algoMixed.R MNARclust.R RcppExports.R