Title: Maximum Likelihood Multiple Imputation
Description: Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel (2018) <arXiv:1210.0870v9>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.
Imports: MASS, gsl, norm, cat, mix, Matrix, stats, utils

Description: Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2019) <arXiv:1210.0870v10>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.
Imports: MASS, gsl, norm, cat, mix, Matrix, stats, utils, nlme

Description: Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.

Title: Multilevel Model for Multivariate Responses with Missing Values
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"),person("Trustees","Columbia-University",email="secretary@columbia.edu",role="cph"))
Description: 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.
Maintainer: Irene SL Zeng <i.zeng@auckland.ac.nz>
Depends: R (>= 3.0.2), Rcpp (>= 0.12.12), methods, stats
Imports: rstan (>= 2.16.2), rstantools (>= 1.3.0), MASS, Matrix,