# Synthesizing Causal Evidence in a Distributed Research Network

## Documentation for package ‘EvidenceSynthesis’ version 1.1.0

- 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