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"create_jags_init. Helper function to create jags init list dependant on length on chains provided", "topics": [ "create_jags_init" ] }, { "page": "create_template", "title": "Creates a minimal project template for selected model type", "topics": [ "create_template" ] }, { "page": "extract_BUGS_file", "title": "Helper function to extract BUGS files for given input parameters", "topics": [ "extract_BUGS_file" ] }, { "page": "gemtcPlus", "title": "gemtcPlus: A package for performing NMA in R", "topics": [ "gemtcPlus-package", "gemtcPlus" ] }, { "page": "get_fp_1o", "title": "First order fractional polynomial", "topics": [ "get_fp_1o" ] }, { "page": "get_fp_1o_GoF", "title": "Calculate the study and arm level survivor functions estimates from a 1st order fractional polynomial NMA. These estimates provide the basis for a goodness-of-fit graph when plotted along with the input data.", "topics": [ "get_fp_1o_GoF" ] }, { "page": "get_fp_1o_HR", "title": "Calculate the time-dependent hazard ratios obtained from fitting a first order fractional polynomial model.", "topics": [ "get_fp_1o_HR" ] }, { "page": "get_fp_1o_S", "title": "Calculate the survivor functions estimated in a 1st order fractional polynomial NMA model. The absolute S(t) estimates combining the estimated baseline survival from a reference trial (in the NMA) with the fractional polynomial (log)hazard ratio estimates to construct the S(t) functions for each treatment.", "topics": [ "get_fp_1o_S" ] }, { "page": "get_fp_2o", "title": "Second order fractional polynomial", "topics": [ "get_fp_2o" ] }, { "page": "get_fp_2o_GoF", "title": "Calculate the study and arm level survivor functions estimates from a 2nd order fractional polynomial NMA. These estimates provide the basis for a goodness-of-fit graph when plotted along with the input data.", "topics": [ "get_fp_2o_GoF" ] }, { "page": "get_fp_2o_HR", "title": "Calculate the time-dependent hazard ratios obtained from fitting a second order fractional polynomial model.", "topics": [ "get_fp_2o_HR" ] }, { "page": "get_fp_2o_S", "title": "Calculate the survivor functions estimated in a 2nd order fractional polynomial NMA model. The absolute S(t) estimates combining the estimated baseline survival from a reference trial (in the NMA) with the fractional polynomial (log)hazard ratio estimates to construct the S(t) functions for each treatment.", "topics": [ "get_fp_2o_S" ] }, { "page": "get_fp_comparison", "title": "Extract model information and fit statistics from a list of fractional polynomial NMAs.", "topics": [ "get_fp_comparison" ] }, { "page": "get_fp_contrasts", "title": "Extract the treatment contrasts vs the reference in the network", "topics": [ "get_fp_contrasts" ] }, { "page": "get_fp_corrs", "title": "Calculate correlations between the contrast estimates for multi-dimensional effect estimates for all treatments in a FP NMA.", "topics": [ "get_fp_corrs" ] }, { "page": "get_fp_elements", "title": "Extract model information and fit statistics from NMA fit in jags of a fractional polynomial model.", "topics": [ "get_fp_elements" ] }, { "page": "get_fp_GoF", "title": "Calculate the study and arm level survivor functions estimates from a fractional polynomial NMA. These estimates provide the basis for a goodness-of-fit graph when plotted along with the input data.", "topics": [ "get_fp_GoF" ] }, { "page": "get_fp_HR", "title": "Calculate the time-dependent hazard ratios obtained from fitting a fractional polynomial model (first or second order).", "topics": [ "get_fp_HR" ] }, { "page": "get_fp_S", "title": "Calculate the survivor functions estimated in a fractional polynomial NMA model. The absolute S(t) estimates combining the estimated baseline survival from a reference trial (in the NMA) with the fractional polynomial (log)hazard ratio estimates to construct the S(t) functions for each treatment.", "topics": [ "get_fp_S" ] }, { "page": "get_jags_info", "title": "Utility function to return jags data and model for reporting (e.g. in appendix)", "topics": [ "get_jags_info" ] }, { "page": "get_mtc_allVsNew", "title": "Utility function to extract effect estimates \"other treatments vs new\" from gemtc fit.", "topics": [ "get_mtc_allVsNew" ] }, { "page": "get_mtc_newVsAll", "title": "Utility function to extract effect estimates \"new vs other treatments\" from gemtc fit.", "topics": [ "get_mtc_newVsAll" ] }, { "page": "get_mtc_probBetter", "title": "Utility function to extract probabilities of new treatment being better from gemtc fit (e.g. P(HR<1) for HRs new vs other).", "topics": [ "get_mtc_probBetter" ] }, { "page": "get_mtc_sum", "title": "Utility function to extract summary stats from mtc.result object.", "topics": [ "get_mtc_sum" ] }, { "page": "get_nw_fromto", "title": "Extract edges information (\"from-to matrix\") from network data frame.", "topics": [ "get_nw_fromto" ] }, { "page": "get_pw_segments", "title": "Utility function to get segments (as character strings) from vector with cutpoints", "topics": [ "get_pw_segments" ] }, { "page": "get_pwe_comparison", "title": "Extract model information and fit statistics from a list of piecewise-exponential NMA fits.", "topics": [ "get_pwe_comparison" ] }, { "page": "get_pwe_contrasts", "title": "Utility function to extract HR estimates from piece-wise exponential model fit in (format needed for ggplot)", "topics": [ "get_pwe_contrasts" ] }, { "page": "get_pwe_conv_diag", "title": "Utility function: convergence diagnostics for piece-wise constant models", "topics": [ "get_pwe_conv_diag" ] }, { "page": "get_pwe_elements", "title": "Extract model information and fit statistics from NMA fit in jags of a piecewise-exponential model.", "topics": [ "get_pwe_elements" ] }, { "page": "get_pwe_GoF", "title": "Calculate the survivor function estimates for each study and arm. Calculate also the observed survival curves from the binned KM data to compare observed and estimated survivor functions.", "topics": [ "get_pwe_GoF" ] }, { "page": "get_pwe_S", "title": "Calculate the survivor functions estimated in piecewise-constant NMA model. The absolute S(t) estimates combining the estimated baseline survival from a reference trial (in the NMA) with the piecewise-constant hazard ratio estimates to construct the S(t) functions for each treatment.", "topics": [ "get_pwe_S" ] }, { "page": "get_segments", "title": "Utility function to get segments (as character strings) from vector with cutpoints", "topics": [ "get_segments" ] }, { "page": "groupedTTE_fp_pre_proc", "title": "Utility function for pre-processing: prepare jags input data for FP model.", "topics": [ "groupedTTE_fp_pre_proc" ] }, { "page": "groupedTTE_pwe_pre_proc", "title": "Utility function for pre-processing: prepare jags input data for PWE model.", "topics": [ "groupedTTE_pwe_pre_proc" ] }, { "page": "list_BUGS", "title": "Lists all available model files inside the inst directory", "topics": [ "list_BUGS" ] }, { "page": "match_args_to_func", "title": "Helper function to extract named elements from a list to match the arguments of supplied function", "topics": [ "match_args_to_func" ] }, { "page": "mtc.prob.better.table", "title": "Utility function providing pairwise probability of being better (col vs row). (Adapted from gemtc::relative.effect.table()).", "topics": [ "mtc.prob.better.table" ] }, { "page": "nma_fit", "title": "Takes input data and a model plan and passes to the model engine specified. Current supported engines are the `gemtc` package (using mtc.model & mtc.run) or `rjags` (using jags and dic.samples functions)", "topics": [ "nma_fit" ] }, { "page": "nma_pre_proc", "title": "NMA data pre-processing", "topics": [ "nma_pre_proc" ] }, { "page": "plan_binary", "title": "Creates a model plan for binary data", "topics": [ "plan_binary" ] }, { "page": "plan_fp", "title": "Creates a fractional polynomial model plan", "topics": [ "plan_fp" ] }, { "page": "plan_hr", "title": "Creates a model plan for hazard ratio", "topics": [ "plan_hr" ] }, { "page": "plan_pwe", "title": "Creates a fractional polynomial model plan", "topics": [ "plan_pwe" ] }, { "page": "plot_fp_HR", "title": "Produce ggplot from HR values in data.frame (medians vs time for several trts, all in one plot)", "topics": [ "plot_fp_HR" ] }, { "page": "plot_mtc_forest", "title": "Utility function to do forest plot from data.frame with effect estimates.", "topics": [ "plot_mtc_forest" ] }, { "page": "process_binary", "title": "Transforms binary data", "topics": [ "process_binary" ] }, { "page": "process_gsd", "title": "Transforms grouped survival data", "topics": [ "process_gsd" ] }, { "page": "process_hr", "title": "Transforms hazard ratio data", "topics": [ "process_hr" ] }, { "page": "pwe_Hu", "title": "Calculate the cumulative hazard over [0, tmax] from piecewise constant model.", "topics": [ "pwe_Hu" ] }, { "page": "pwe_S", "title": "Calculate the survivor function S(t) from a piecewise exponential model.", "topics": [ "pwe_S" ] } ], "_readme": "https://github.com/Roche/gemtcPlus/raw/HEAD/README.md", "_rundeps": [ "abind", "base64enc", "bit", "bit64", "bitops", "boot", "bslib", "cachem", "caTools", "cli", "clipr", "coda", "CompQuadForm", "cpp11", "crayon", "digest", "dplyr", "evaluate", "farver", "fastmap", "fontawesome", "forcats", "fs", "gemtc", "generics", "GGally", "ggmcmc", "ggplot2", "ggstats", "glue", "gplots", "gtable", "gtools", "highr", "hms", "htmltools", "igraph", "isoband", "jquerylib", "jsonlite", "KernSmooth", "knitr", "labeling", "lattice", "lifecycle", "lme4", "magrittr", "MASS", "mathjaxr", "Matrix", "memoise", "meta", "metabook", "metadat", "metafor", "mime", "minqa", "network", "nlme", "nloptr", "numDeriv", "patchwork", "pbapply", "pillar", "pkgconfig", "plyr", "prettyunits", "progress", "purrr", "R2jags", "R2WinBUGS", "R6", "rappdirs", "rbibutils", "RColorBrewer", "Rcpp", "RcppEigen", "Rdpack", "readr", "reformulas", "reshape", "Rglpk", "rjags", "rlang", "rmarkdown", "S7", "sass", "scales", "slam", "statnet.common", "stringi", "stringr", "tibble", "tidyr", "tidyselect", "tinytex", "truncnorm", "tzdb", "utf8", "vctrs", "viridisLite", "vroom", "withr", "xfun", "xml2", "yaml" ], "_sysdeps": [ { "shlib": "libjags", "package": "jags", "headers": "jags", "source": "jags", "version": "4.3.2-2.2404.0", "name": "jags", "homepage": "https://mcmc-jags.sourceforge.io", "description": "Just Another Gibbs Sampler for Bayesian MCMC - binary\nJAGS is Just Another Gibbs Sampler. 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