Package: JICO 0.0.0.9000
JICO: Joint and Individual Regression
An R package that implements the JICO algorithm [Wang, P., Wang, H., Li, Q., Shen, D., & Liu, Y. (2022). <arxiv:2209.12388>]. It aims at solving the multi-group regression problem. The algorithm decomposes the responses from multiple groups into shared and group-specific components, which are driven by low-rank approximations of joint and individual structures from the covariates respectively.
Authors:
JICO_0.0.0.9000.tar.gz
JICO_0.0.0.9000.zip(r-4.5)JICO_0.0.0.9000.zip(r-4.4)JICO_0.0.0.9000.zip(r-4.3)
JICO_0.0.0.9000.tgz(r-4.4-any)JICO_0.0.0.9000.tgz(r-4.3-any)
JICO_0.0.0.9000.tar.gz(r-4.5-noble)JICO_0.0.0.9000.tar.gz(r-4.4-noble)
JICO_0.0.0.9000.tgz(r-4.4-emscripten)JICO_0.0.0.9000.tgz(r-4.3-emscripten)
JICO.pdf |JICO.html✨
JICO/json (API)
NEWS
| # Install 'JICO' in R: |
| install.packages('JICO', repos = c('https://peiyaow.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/peiyaow/jico/issues
Last updated 2 years agofrom:a7bfa0c4db. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 01 2024 |
| R-4.5-win | OK | Nov 01 2024 |
| R-4.5-linux | OK | Nov 01 2024 |
| R-4.4-win | OK | Nov 01 2024 |
| R-4.4-mac | OK | Nov 01 2024 |
| R-4.3-win | OK | Nov 01 2024 |
| R-4.3-mac | OK | Nov 01 2024 |
Exports:continuumcontinuum.multigroup.itercv.continnum.iterparameter.set.G_2parameter.set.G_3parameter.set.rankA_eq
Dependencies:data.tablejsonlitelatticeMASSMatrixnleqslvrlistXMLyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Compute the coefficients from the continuum regression (CR) algorithm | C2beta |
| The continuum regression (CR) algorithm | continuum |
| The Joint and Individual Component Regression (JICO) algorithm | continuum.multigroup.iter |
| Utility function to create folds for stratified samples | createFolds |
| Fit JICO with cross-validation to tune hyperparameters | cv.continnum.iter |
| Generate diagonal matrix | DIAG |
| Helper function to compute the SVD results | initialize.UDVZ |
| Generate parameter sets (G=2) | parameter.set.G_2 |
| Generate parameter sets (G=3) | parameter.set.G_3 |
| Generate parameter sets (equal individual ranks) | parameter.set.rankA_eq |
| Helper function to compute the inverse of input X matrix | SOLVE |
