Package index • prova

Learn from data

Kexample: Example Knowledge object produced by learn()
learn(): Monte Carlo computation of posterior probability distribution

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

Quantify associations between variates

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

Calculate expected utilities

exputility(): Calculate expected utilities and their uncertainties

Plot & print

hist(<prova_mi>): Plot the revisability of an object of class "prova_mi" (mutual information) as a histogram
hist(<prova_pr>): Plot the revisability of an object of class "prova_pr" (probability) as a histogram
plot(<prova_eu>): Plot an object of class "prova_eu" (expected utility) and its revisability
plot(<prova_pr>): Plot an object of class "prova_pr" (probability)
pplot(): Plot numeric or character values
print(<prova_K>): Print summary of a "prova_K" (knowledge) object
print(<prova_eu>): Print an object of class "prova_eu" (expected utility)
print(<prova_mi>): Print an object of class "prova_mi" (mutual information) (mutual information)
print(<prova_pr>): Print an object of class "prova_pr" (probability)

Handle metadata and data files

pwrite.csv() pread.csv(): Write and read CSV files in Prova
meta_penguins: Metadata file for "penguins" dataset
metadatatemplate(): Metadata and helper function to create a template metadata file or object.
metadataExample: Example metadata file

Internal functions

For developers (beware!)

.Pcheckpoints(): Calculate joint frequencies for MCMC-monitoring checkpoints
.buildauxmetadata(): Build augmented metadata file
.cleanup(): Cleanup a learn()-output directory
.combineYX(): Calculate probabilities, quantiles, etc, for all Y and X combinations
.createQfunction(): Calculate and save transformation function for ordinal variates
.denorm(): Utility function to improve accuracy
.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
.joinPtraces(): Join '____tempPtraces-' files
.learnbind(): Bind 3D arrays by first dimension
.lprobsargsyx(): Prepare arguments for util_lprobsyx from data
.lprobsbase(): Calculate collection of log-probabilities for different components and samples
.lprobsmi(): Calculate and combine log-probabilities to compute entropies
.mcjoin(): Concatenate mcsample objects
.mcsubset(): Eliminate samples from mcsamples object
.plotFsamples(): Plot one-dimensional posterior probabilities
.prepPcheckpoints(): Format datapoints used for MCMC monitoring
.prsubset(): Subset variates of an object of class "prova_pr" (probability)
.qYXcont(): Calculate quantiles for continuous Y by bisection
.qYXdiscr(): Calculate quantiles for discrete Y by bisection
.retrieveK(): Retrieve a "prova_K" (knowledge) object
.rowcumsum(): Cumulative sum along first dimension
.rowinvcumsum(): Inverse cumulative sum along first dimension
.signifC(): Format numbers respecting significant digits
.testPr(): Test posterior probabilities
.vtransform(): Transforms variates to different representations
.workerfun(): Worker function called by learn()
prova prova-package: prova: Nonparametric Probabilistic-Statistical Variate Analysis