Package: McMiso 0.2.0
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>.
Authors:
McMiso_0.2.0.tar.gz
McMiso_0.2.0.zip(r-4.7)McMiso_0.2.0.zip(r-4.6)McMiso_0.2.0.zip(r-4.5)
McMiso_0.2.0.tgz(r-4.6-any)McMiso_0.2.0.tgz(r-4.5-any)
McMiso_0.2.0.tar.gz(r-4.7-any)McMiso_0.2.0.tar.gz(r-4.6-any)
McMiso_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
McMiso/json (API)
NEWS
| # Install 'McMiso' in R: |
| install.packages('McMiso', repos = c('https://kencheung2.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:1d34da1fc5. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 94 | ||
| source / vignettes | OK | 184 | ||
| linux-release-x86_64 | OK | 95 | ||
| macos-release-arm64 | OK | 144 | ||
| macos-oldrel-arm64 | OK | 180 | ||
| windows-devel | OK | 83 | ||
| windows-release | OK | 80 | ||
| windows-oldrel | OK | 100 | ||
| wasm-release | OK | 82 |
Exports:boundarymcmisomcmisoNmcPBclassifiermisomisoNPBclassifier
Dependencies:
