Synthesizing Causal Evidence in a Distributed Research Network

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Documentation for package ‘EvidenceSynthesis’ version 1.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

approximateHierarchicalNormalPosterior	Approximate Bayesian posterior for hierarchical Normal model
approximateLikelihood	Approximate a likelihood function
approximateSimplePosterior	Approximate simple Bayesian posterior
biasCorrectionInference	Bias Correction with Inference
buildLabelReferences	Build a list of references that map likelihood names to integer labels for later use
computeBayesianMetaAnalysis	Compute a Bayesian random-effects meta-analysis
computeConfidenceInterval	Compute the point estimate and confidence interval given a likelihood function approximation
computeFixedEffectMetaAnalysis	Compute a fixed-effect meta-analysis
computeHierarchicalMetaAnalysis	Compute a Bayesian random-effects hierarchical meta-analysis
constructDataModel	Construct 'DataModel' objects from approximate likelihood or profile likelihood data
createApproximations	Create likelihood approximations from individual-trajectory data
createSccsSimulationSettings	Create SCCS simulation settings
createSimulationSettings	Create simulation settings
customFunction	A custom function to approximate a log likelihood function
detectApproximationType	Detect the type of likelihood approximation based on the data format
extractSourceSpecificEffects	Compute source-specific biases and bias-corrected estimates from hierarchical meta analysis results
fitBiasDistribution	Fit Bias Distribution
generateBayesianHMAsettings	Generate settings for the Bayesian random-effects hierarchical meta-analysis model
hermiteInterpolation	Cubic Hermite interpolation using both values and gradients to approximate a log likelihood function
hmaLikelihoodList	Example profile likelihoods for hierarchical meta analysis with bias correction
likelihoodProfileLists	A bigger example of profile likelihoods for hierarchical meta analysis with bias correction
loadCyclopsLibraryForJava	Load the Cyclops dynamic C++ library for use in Java
ncLikelihoods	Example profile likelihoods for negative control outcomes
ooiLikelihoods	Example profile likelihoods for a synthetic outcome of interest
plotBiasCorrectionInference	Plot bias correction inference
plotBiasDistribution	Plot bias distributions
plotCovariateBalances	Plot covariate balances
plotEmpiricalNulls	Plot empirical null distributions
plotLikelihoodFit	Plot the likelihood approximation
plotMcmcTrace	Plot MCMC trace
plotMetaAnalysisForest	Create a forest plot
plotPerDbMcmcTrace	Plot MCMC trace for individual databases
plotPerDbPosterior	Plot posterior density per database
plotPosterior	Plot posterior density
plotPreparedPs	Plot the propensity score distribution
preparePsPlot	Prepare to plot the propensity score distribution
prepareSccsIntervalData	Prepare SCCS interval data for pooled analysis
sequentialFitBiasDistribution	Fit Bias Distribution Sequentially or in Groups
simulateMetaAnalysisWithNegativeControls	Simulate survival data across a federated data network, with negative control outcomes as well.
simulatePopulations	Simulate survival data for multiple databases
skewNormal	The skew normal function to approximate a log likelihood function
summarizeChain	Utility function to summarize MCMC samples (posterior mean, median, HDI, std, etc.)
supportsJava8	Determine if Java virtual machine supports Java