Package index • prova

Learn from data

learn(): Monte Carlo computation of posterior probability distribution
learntExample: Example learnt object produced by learn()

Calculate probabilities & statistics

Pr(): Calculate posterior probabilities
qPr(): Calculate quantiles
vrtgrid(): Create a grid of values for a variate

Generate synthetic datapoints

rPr(): Generate datapoints

Plot & print probability distributions

flexiplot(): Plot numeric or character values
hist(<probability>): Plot the variability of an object of class "probability" as a histogram
plot(<probability>): Plot an object of class "probability"
plotquantiles(): Plot pairs of quantiles
print(<probability>): Print an object of class "probability"

Handle metadata and data files

metadataExample: Example metadata file
metadatatemplate(): Metadata and helper function for metadata
pwrite.csv() pread.csv(): Write and read CSV files in Prova

Quantify associations between variate groups

mutualinfo(): Calculate mutual information between groups of joint variates

Internal functions

For developers (beware!)

buildauxmetadata(): Build augmented metadata file
createQfunction(): Calculate and save transformation function for ordinal variates
fftNGS(): Find optimal FFT size
funAC(): Compute autocovariance
funESS3(): Compute ESS
funMCEQ(): Calculate credibility quantiles on estimated quantile
funMCSELD(): Calculate MC standard error using LaplacesDemon's batch means
learnbind(): Bind 3D arrays by first dimension
mcjoin(): Concatenate mcsample objects
mcsubset(): Eliminate samples from mcsamples object
plotFsamples(): Plot one-dimensional posterior probabilities
prova prova-package: prova: Nonparametric Probabilistic-Statistical Variate Analysis with Automated Markov-Chain Monte Carlo
prsubset(): Subset variates of an object of class "probability"
rowcumsum(): Cumulative sum along first dimension
rowinvcumsum(): Inverse cumulative sum along first dimension
testPr(): Test posterior probabilities
util_Pcheckpoints(): Calculate joint frequencies for MCMC-monitoring checkpoints
util_cleanup(): Cleanup a learn()-output directory
util_combineYX(): Calculate probabilities, quantiles, etc, for all Y and X combinations
util_denorm(): Utility function to avoid finite-precision accuracys
util_joinPtraces(): Join '____tempPtraces-' files
util_lprobsargsyx(): Prepare arguments for util_lprobsyx from data
util_lprobsbase(): Calculate collection of log-probabilities for different components and samples
util_lprobsmi(): Calculate pairs of log-probabilities for mutualinfo()
util_prepPcheckpoints(): Format datapoints used for MCMC monitoring
util_qYXcont(): Calculate quantiles for continuous Y by bisection
util_qYXdiscr(): Calculate quantiles for discrete Y by bisection
vtransform(): Transforms variates to different representations
workerfun(): Worker function called by learn()