Package: metapack 0.3

metapack: Bayesian Meta-Analysis and Network Meta-Analysis

Contains functions performing Bayesian inference for meta-analytic and network meta-analytic models through Markov chain Monte Carlo algorithm. Currently, the package implements Hui Yao, Sungduk Kim, Ming-Hui Chen, Joseph G. Ibrahim, Arvind K. Shah, and Jianxin Lin (2015) <doi:10.1080/01621459.2015.1006065> and Hao Li, Daeyoung Lim, Ming-Hui Chen, Joseph G. Ibrahim, Sungduk Kim, Arvind K. Shah, Jianxin Lin (2021) <doi:10.1002/sim.8983>. 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.

Authors:Daeyoung Lim [aut, cre], Ming-Hui Chen [ctb], Sungduk Kim [ctb], Joseph Ibrahim [ctb], Arvind Shah [ctb], Jianxin Lin [ctb]

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metapack.pdf |metapack.html✨
metapack/json (API)
NEWS

# Install 'metapack' in R:
install.packages('metapack', repos = c('https://daeyounglim.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/daeyounglim/metapack/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • TNM - Triglycerides Network Meta (TNM) data
  • cholesterol - 26 double-blind, randomized, active, or placebo-controlled clinical trials on patients with primary hypercholesterolemia sponsored by Merck & Co., Inc., Kenilworth, NJ, USA.

On CRAN:

4.65 score 3 stars 10 scripts 285 downloads 8 exports 34 dependencies

Last updated 10 months agofrom:79940afab2. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-win-x86_64OKNov 20 2024
R-4.5-linux-x86_64OKNov 20 2024
R-4.4-win-x86_64OKNov 20 2024
R-4.4-mac-x86_64OKNov 20 2024
R-4.4-mac-aarch64OKNov 20 2024
R-4.3-win-x86_64OKNov 20 2024
R-4.3-mac-x86_64OKNov 20 2024
R-4.3-mac-aarch64OKNov 20 2024

Exports:bayes_nmrbayes_parobsbmeta_analysebmeta_analyzehpdmodel_compnssucra

Dependencies:BHclicolorspacefansifarverFormulaggplot2gluegridExtragtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcppRcppArmadilloRcppProgressrlangscalestibbleutf8vctrsviridisLitewithr

Introduction to metapack

Daeyoung Lim

Rendered fromintro-to-metapack.Rmdusingknitr::rmarkdownon Nov 20 2024.

Last update: 2021-08-13
Started: 2020-12-09

Readme and manuals

Help Manual

Help pageTopics
Fit Bayesian Network Meta-Regression Modelsbayes_nmr
Fit Bayesian Inference for Meta-Regressionbayes_parobs
bmeta_analyze supersedes the previous two functions: bayes_parobs, bayes_nmrbmeta_analyse bmeta_analyze
26 double-blind, randomized, active, or placebo-controlled clinical trials on patients with primary hypercholesterolemia sponsored by Merck & Co., Inc., Kenilworth, NJ, USA.cholesterol
get the posterior mean of fixed-effect coefficientscoef.bsynthesis
get fitted valuesfitted.bayesnmr
get fitted valuesfitted.bayesparobs
get the highest posterior density (HPD) intervalhpd
get the highest posterior density (HPD) intervalhpd.bayesnmr
get the highest posterior density (HPD) interval or equal-tailed credible intervalhpd.bayesparobs
metapack: a package for Bayesian meta-analysis and network meta-analysismetapack
compute the model comparison measures: DIC, LPML, or Pearson's residualsmodel_comp
get compute the model comparison measuresmodel_comp.bayesnmr
compute the model comparison measuresmodel_comp.bayesparobs
helper function encoding trial sample sizes in formulasns
get goodness of fitplot.bayesnmr
get goodness of fitplot.bayesparobs
plot the surface under the cumulative ranking curve (SUCRA)plot.sucra
Print resultsprint.bayesnmr
Print resultsprint.bayesparobs
get surface under the cumulative ranking curve (SUCRA)sucra
get surface under the cumulative ranking curve (SUCRA)sucra.bayesnmr
`summary` method for class "`bayesnmr`"summary.bayesnmr
'summary' method for class "'bayesparobs'"summary.bayesparobs
Triglycerides Network Meta (TNM) dataTNM