v0.5.5

• Removed a bug that broke learn() with datasets consisting of only one nominal variate.

v0.5.2

• Fix bug in handling metadata.

v0.5.0

• Added function qPr() for the computation of quantiles and their variability.

• Corrected some bugs.

• NB: learnt objects created with versions < 0.5.0 are incompatible with the functions of version 0.5.0. To convert to the new version, use the function util_learntvar2sd(file), where file is the path of the learnt object to be converted.

v0.4.0

• Eliminated dependence on packages 'foreach', 'doParallel', 'doRNG'. Now only the 'parallel' package, which ships with R by default, is used.
• Parallel computation of large numbers of probabilities is now faster on average; even five times faster.
• It's now easier to write minimal metadata files (though minimal files should only be used for testing).

v0.3.2

• Improved graphic output of flexiplot().

v0.3.1

• Added the function rPr(), which generates datapoints for any desired set of joint variates, according to the posterior probability calculated with the learn() function. See documentation.
• Added arguments xjitter and yjitter to flexiplot(), useful for scatterplots of discrete variates.
• Updated the mutualinfo() function, which should also be a little faster.
• Updated and corrected documentation.

v0.3.0

NB: this release makes all relevant functions incompatible with objects obtained with previous releases. Please submit an issue if you'd like to convert your previous results in a format compatible with the new release. A conversion utility will be made available soon if there are enough requests.

• The Pr() function has a new argument tails =, and now accepts arbitrary combinations with point-value arguments (Y = y) and left- or right-open interval arguments (Y <= y and Y >= y), the latter for ordinal and continuous variates only. Thus it covers and extends the use of the now-obsolete function tailPr(). See documentation, especially about the new argument tails.
• The Pr() function now outputs two new elements: values.MCerror and quantiles.MCerror, quantifying the accuracy of the Monte Carlo calculation of the values and quantiles elements. See documentation.
• New handling of ordinal and nominal variates, which should be faster and use slightly less memory.
• More precise calculation of probabilities for rounded and discrete variates.

v0.2.3

• Improved (hopefully) stopping rule of the Markov-chain Monte Carlo computation. Now partly based on the "bulk ESS" function from Vehtari & al.
• Mainly for debugging purposes, learn() now continuously updates the Monte Carlo trace plot during calculations.
• A couple more internal functions used for debugging and Monte Carlo monitoring.

v0.2.2

Major changes:

• A graphical user interface to basic functionalities of inferno is now available, thanks to @h587916 .
• New method for the hist() function, for objects of class probability. When applied to a probability object obtained withPr() or tailPr(), the function hist() produces histograms of the variability of the probabilities calculated with those functions. In other words, it displays the information contained in the $samples items.
• Draft of a vignette with a practical example of how to use inferno from scratch.

Minor changes:

• Changed default labels, legends, and titles of plots produced with plot() on a probability object. Hopefully the plots are now clearer.
• Fixed some bugs in plot functions.

v0.2.1

Updates to GitHub:
Added GitHub Actions workflow for automatic testing of the software.

Updates to code:

• New logical argument "verbose" (def. TRUE) in buildmetadata(). When TRUE, messages are given for each variate, explaining the internal heuristics and guessing to determine the various metadata values.

• Modified handling of rounded continuous variates, now more consistent according to discussion in issue #50.

• Elimination of type-"L" variates in Monte Carlo sampling. The type "D" handles both rounded continuous variates and ordinal variates having domain with more than 10 values. samplesFdistribution() and other functions have been updated accordingly.

• Rewritten plotFsamples(). Now it goes through every variate type in turn, and should be easier to understand.

• Modified the information contained in the internal "auxmetadata" object, and accordingly modified all functions that use this object.

Performed tests:

• Performed a battery of tests against many datasets available in base-R. This lead to the unveiling and fixing of several small bugs.

The tests were performed to check the working of buildmetadata(), buildauxmetadata(), samplesFdistribution(), plotFsamples().

• With the mentioned datasets, samplesFdistribution() has been checked against a clearer (but much slower), for-loop-based script -- written from scratch -- to calculate the various probabilities. This script also uses mathematical formulae that are theoretically identical but numerically different when it comes to finite-precision arithmetic. Some bugs have been fixed

• The latter test also shows that errors coming from finite-precision arithmetic are all below 10^-15.