Title: Meta-analysis of significance values
Description: Meta-analysis of significance values. The package
provides routines for a number of methods for doing this, a
number of datasets to replicate published results, and a

Title: Meta-Analysis of Significance Values
Description: The canonical way to perform meta-analysis
involves using effect sizes. When they are not available
this package provides a number of methods for
number of data-sets to replicate published results, and a

Description: The canonical way to perform meta-analysis involves using effect sizes.
When they are not available this package provides a number of methods for
meta-analysis of significance values including the methods of Edgington, Fisher,
Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results;

Lancaster, Stouffer, Tippett, and Wilkinson; a number of data-sets to replicate published results;

Packaged: 2019-11-15 11:49:16 UTC; MichaelUser

Packaged: 2020-01-22 07:23:23 UTC; MichaelUser

Imports: lattice,Rdpack (>= 0.7),TFisher,mutoss,mathjaxr (>= 0.8-3)
Packaged: 2020-07-12 09:58:12 UTC; MichaelUser

Packaged: 2021-09-21 08:53:18 UTC; MichaelUser

Packaged: 2021-10-31 13:34:11 UTC; MichaelUser

Packaged: 2021-12-16 14:46:57 UTC; MichaelUser

Imports: lattice,Rdpack (>= 0.7),TFisher,mutoss,mathjaxr (>=
Packaged: 2022-02-08 13:50:17 UTC; MichaelUser

Title: Bayesian Meta-Analysis and Network Meta-Analysis
person("Ming-Hui", "Chen", email = "ming-hui.chen@uconn.edu",
person("Sungduk", "Kim", email = "kims2@mail.nih.gov",
person("Joseph", "Ibrahim", email = "ibrahim@bios.unc.edu",
person("Arvind", "Shah", email = "arvind_shah@merck.com", role = "ctb"),
person("Jianxin", "Lin", email = "jianxin_lin@merck.com", role = "ctb"))
Description: Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Yao, Kim, Chen, Ibrahim, Shah, and Jianxin Lin (2015) <doi:10.1080/01621459.2015.1006065> and network meta-regression models using heavy-tailed multivariate random effects with covariate-dependent variances. For maximal computational efficiency, the Markov chain Monte Carlo samplers for each model, written in C++, are fine-tuned. This software has been developed under the auspices of the National Institutes of Health and Merck & Co., Inc., Kenilworth, NJ, USA.
Imports: Rcpp, ggplot2, methods, gridExtra
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, BH