McMiso - Multicore Multivariable Isotonic Regression
Provides functions for isotonic regression and
classification when there are multiple independent variables.
The functions solve the optimization problem using a projective
Bayes approach with recursive sequential update algorithms, and
are useful for situations with a relatively large number of
covariates. Supports binary outcomes via a Beta-Binomial
conjugate model ('miso', 'PBclassifier') and continuous
outcomes via a Normal-Inverse-Chi-Squared conjugate model
('misoN'). Parallel computing wrappers ('mcmiso',
'mcPBclassifier', 'mcmisoN') are provided that run the down-up
and up-down algorithms simultaneously and return whichever
finishes first. The estimation method follows the projective
Bayes solution described in Cheung and Diaz (2023)
<doi:10.1093/jrsssb/qkad014>.
