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)