verbose = in several function, so
as to show information to console only if the user so desires. This
argument is FALSE for all functions except
learn().print() method for probability
objects.Pr() and related
functions.Renamed the package from inferno to Prova. It turns out, unsurprisingly, that the name “inferno” was already in use for several inference-related software packages of various kinds.
learn() with datasets
consisting of only one nominal variate.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.
flexiplot().rPr(), which generates datapoints
for any desired set of joint variates, according to the posterior
probability calculated with the learn() function. See
documentation.flexiplot(),
useful for scatterplots of discrete variates.mutualinfo() function, which should also be
a little faster.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.
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.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.learn() now continuously
updates the Monte Carlo trace plot during calculations.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:
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.
Major changes:
New initialization procedure for Monte Carlo sampling (this led to great improvements)
New stopping rule for the Monte Carlo sampling (partly based on the ideas in doi.org/10.1080/10618600.2015.1044092)
New handling of rounded and ordinal variates
New argument ‘auxdata’ in inferpopulation(): the
user can here give a much larger dataset (of which ‘data’ argument is
presumably a subset), which is used to calculate some general statistics
to improve the inference. The idea is that ‘auxdata’ cannot be used for
the Monte Carlo proper, owing to memory or time limitations, but at
least we can squeeze some other useful information out of it.
New argument ‘relerror’ in inferpopulation(): an
(approximate) upper bound to the desired numerical error. It’s the
numerical error relative to the width of the probability
distribution.
New argument ‘ncheckpoints’ in inferpopulation():
number of datapoints to be used to check Monte Carlo convergence. NULL
value (default) is equal to the number of variates + 1.
Minor changes:
Elimination of fields “centralvalue”, “lowvalue”, “highvalue” in metadata - they aren’t required anymore
Elimination of not-used diagnostics in log files
Elimination of dependence on LaplacesDemon
Corrections and improvements of plots and log information
Correction in how parallel cores were closed (was leading to errors)
Various bug fixes
The code is now available as an R package on GitHub.
This release includes many changes to the code.
mutualinfo() has been added. This function calculates
mutual information between groups of joint variates.