{ "_id": "6a1157f6acfb0bcc41ceb8f7", "Package": "bnma", "Type": "Package", "Title": "Bayesian Network Meta-Analysis using 'JAGS'", "Version": "1.6.1", "Date": "2025-07-27", "Authors@R": "c( \nperson(\"Michael\", \"Seo\", email = \"swj8874@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Christopher\", \"Schmid\", email = \"christopher_schmid@brown.edu\", role = \"aut\"))", "Description": "Network meta-analyses using Bayesian framework following\nDias et al. (2013) . Based on the\ndata input, creates prior, model file, and initial values\nneeded to run models in 'rjags'. Able to handle binomial,\nnormal and multinomial arm-level data. Can handle multi-arm\ntrials and includes methods to incorporate covariate and\nbaseline risk effects. 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network deviance plot", "topics": [ "contrast.network.deviance.plot" ] }, { "page": "contrast.network.leverage.plot", "title": "Make a leverage plot", "topics": [ "contrast.network.leverage.plot" ] }, { "page": "contrast.network.run", "title": "Run the model using the network object", "topics": [ "contrast.network.run" ] }, { "page": "draw.network.graph", "title": "Draws network graph using igraph package", "topics": [ "draw.network.graph" ] }, { "page": "network.autocorr.diag", "title": "Generate autocorrelation diagnostics using coda package", "topics": [ "network.autocorr.diag" ] }, { "page": "network.autocorr.plot", "title": "Generate autocorrelation plot using coda package", "topics": [ "network.autocorr.plot" ] }, { "page": "network.covariate.plot", "title": "Make a covariate plot", "topics": [ "network.covariate.plot" ] }, { "page": "network.cumrank.tx.plot", "title": "Create a treatment cumulative rank plot", "topics": [ "network.cumrank.tx.plot" ] }, { "page": "network.data", 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