Package: MRFA 0.6
MRFA: Fitting and Predicting Large-Scale Nonlinear Regression Problems using Multi-Resolution Functional ANOVA (MRFA) Approach
Performs the MRFA approach proposed by Sung et al. (2020) <doi:10.1080/01621459.2019.1595630> to fit and predict nonlinear regression problems, particularly for large-scale and high-dimensional problems. The application includes deterministic or stochastic computer experiments, spatial datasets, and so on.
Authors:
MRFA_0.6.tar.gz
MRFA_0.6.zip(r-4.5)MRFA_0.6.zip(r-4.4)MRFA_0.6.zip(r-4.3)
MRFA_0.6.tgz(r-4.4-any)MRFA_0.6.tgz(r-4.3-any)
MRFA_0.6.tar.gz(r-4.5-noble)MRFA_0.6.tar.gz(r-4.4-noble)
MRFA_0.6.tgz(r-4.4-emscripten)MRFA_0.6.tgz(r-4.3-emscripten)
MRFA.pdf |MRFA.html✨
MRFA/json (API)
| # Install 'MRFA' in R: |
| install.packages('MRFA', repos = c('https://chihli.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 1 years agofrom:1d414715d5. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 13 2024 |
| R-4.5-win | OK | Nov 13 2024 |
| R-4.5-linux | OK | Nov 13 2024 |
| R-4.4-win | OK | Nov 13 2024 |
| R-4.4-mac | OK | Nov 13 2024 |
| R-4.3-win | OK | Nov 13 2024 |
| R-4.3-mac | OK | Nov 13 2024 |
Exports:aic.MRFAbic.MRFAconfidence.MRFAcv.MRFAMRFA_fitpredict.MRFA
Dependencies:codetoolsdotCall64fieldsforeachglmnetgrplassoiteratorslatticemapsMatrixplyrrandtoolboxRcppRcppEigenrngWELLshapespamsurvivalviridisLite
