The default prior hyperparameters for PBclassifier() and miso() have
changed from a0 = 0.25, b0 = 0.25 to a0 = 0.5, b0 = 0.5 (Jeffreys
prior). Results from previous versions can be reproduced by explicitly
passing a0 = 0.25, b0 = 0.25.
mcmiso() is now an exported function. Previously it was internal. Its
interface has also changed: the future parallel plan must now be set by
the caller via future::plan() before calling mcmiso(), rather than
being set internally. This follows best practices for the future package.
The return value of miso() now has class "miso", enabling a dedicated
print method. Code that tested class(fit) == "list" will need updating.
misoN(): multivariable isotonic regression for continuous outcomes
using a Normal-Inverse-Chi-Squared conjugate model.
mcmisoN(): parallel computing wrapper for misoN() (requires the
future package and a parallel plan set by the caller).
mcPBclassifier(): parallel computing wrapper for PBclassifier().
boundary(): extracts the decision boundary (minimal positive set) from
a fitted "pbc" object.
New print methods: print.pbc(), print.miso(), print.misoN(),
print.boundary().
Comprehensive input validation has been added to all exported functions.
getScenesV3() has been rewritten from a brute-force expand.grid(2^K)
enumeration to a recursive backtracking algorithm. This substantially
reduces memory usage and computation time for large numbers of unique
feature combinations.
Vectorized computation replaces inner loops in SweepCombTogBinom(),
SweepMcCombBinom(), SweepCombTogNorm(), and SweepMcCombNorm().
dplyr has been removed as a dependency (no longer used internally).
future has been moved from Imports to Suggests. It is only required
for the parallel computing wrappers (mcmiso, mcPBclassifier,
mcmisoN). These functions check for the package at run time and give an
informative error if it is not installed.