Title: Mediation Analysis with Missing Data and Non-Normal Data
person("Shuigen", "Ming", role = "aut", email = "msg0@mail.ustc.edu.cn"),
person("Hong", "Zhang", role = "aut", email = "zhangh@ustc.edu.cn"),
person("Zhiyong", "Zhang", role = c("aut", "cre"), email = "johnnyzhz@gmail.com"),
person("Lijuan", "Wang", role = "aut", email = "lwang4@nd.edu"))
Depends: R (>= 3.0), Amelia, MASS, snowfall, rsem
Description: Methods for mediation analysis with missing data and non-normal data are implemented. For missing data, four methods are available: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included to handle missing data. For handling non-normal data, bootstrap and two-stage robust methods can be used. Technical details of the methods can be found in Zhang and Wang (2013, <doi:10.1007/s11336-012-9301-5>), Zhang (2014, <doi:10.3758/s13428-013-0424-0>), and Yuan and Zhang (2012, <doi:10.1007/s11336-012-9282-4>).

Depends: R (>= 3.5.0), Amelia, MASS, snowfall, rsem

Title: A Package for Bayesian Meta-Analysis and Meta-Regression
Description: Provides a collection of functions for conducting meta-analyses under Bayesian context in R. The package includes functions for computing various effect size or outcome measures (e.g. odds ratios, mean difference and incidence rate ratio) for different types of data based on MCMC simulations. Users are allowed to fit fixed- and random-effects models with different priors to the data. Meta-regression can be carried out if effects of additional covariates are observed. Furthermore, the package provides functions for creating posterior distribution plots and forest plot to display main model output. Traceplots and some other diagnostic plots are also available for assessing model fit and performance.

Title: Bayesian Meta-Analysis and Meta-Regression

Title: Bayesian Multivariate GARCH Models
Authors@R: c( person(given = "Philippe",
Description: Fit Bayesian multivariate GARCH models using 'Stan' for full Bayesian inference. Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) <doi:10.1198/073500102288618487> and Bollerslev (1990) <doi:10.2307/2109358>. The BEKK parameterization follows Engle and Kroner (1995) <doi:10.1017/S0266466600009063> while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) <doi:10.31234/osf.io/j57pk>. The fitted models contain 'rstan' objects and can be examined with 'rstan' functions.
Imports: rstan (>= 2.21.2), rstantools (>= 1.5.1), ggplot2, MASS,
Author: Philippe Rast [aut, cre] (<https://orcid.org/0000-0003-3630-6629>),