Likelihood inference in meta-analysis and meta-regression
models.
Annamaria Guolo <annamaria.guolo@univr.it> and Cristiano
Varin <sammy@unive.it>
first- and higher-order likelihood inference in
meta-analysis and meta-regression models.

Likelihood Inference in Meta-Analysis and Meta-Regression Models
First- and higher-order likelihood inference in
meta-analysis and meta-regression models.

Combines either p-values or modified effect sizes from different studies to find differentially expressed genes

Combines either p-values or modified effect sizes from
different studies to find differentially expressed genes

GEOquery, org.Hs.eg.db, VennDiagram, annaffy, hgu133plus2.db,
hgu133a.db, hgu95av2.db
Combines either p-values or modified effect sizes from different
studies to find differentially expressed genes

Combination of either p-values or modified effect sizes from different
studies to find differentially expressed genes.

Bayesian Mixture Analysis for Metagenomic Community Profiling
data.table (>= 1.9.2), Matrix, gtools, Rmpi, ggplot2
metaMix resolves complex metagenomic mixtures by analysing
deep sequencing data, using a Mixture Model based approach.
The use of parallel Monte Carlo Markov chains for the exploration
of the species space enables the identification of the set
of species more likely to contribute to the mixture.

Resolves complex metagenomic mixtures by analysing
deep sequencing data, using a mixture model based approach.
The use of parallel Monte Carlo Markov chains for the exploration
of the species space enables the identification of the set
of species more likely to contribute to the mixture.

Isabelle Weir [aut, cre], Lu Tian [aut], Ludovic Trinquart [aut]
R implementation of a multivariate meta-analysis of randomized controlled trials (RCT) with the difference in restricted mean survival times (RMSTD). Use this package with individual patient level data from an RCT for a time-to-event outcome to determine combined effect estimates according to 4 methods: 1)  a univariate meta-analysis using observed treatment effects, 2) a univariate meta-analysis using effects predicted by fitted Royston-Parmar flexible parametric models, 3) multivariate meta-analysis with analytically derived covariance, 4) multivariate meta-analysis with bootstrap derived covariance. This package computes all combined effects and provides an RMSTD curve with combined effect estimates and their confidence intervals.
mvmeta, meta, survival, survRM2, graphics