Reference

All functions

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