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:Chih-Li Sung

MRFA_0.6.tar.gz
MRFA_0.6.zip(r-4.7)MRFA_0.6.zip(r-4.6)MRFA_0.6.zip(r-4.5)
MRFA_0.6.tgz(r-4.6-any)MRFA_0.6.tgz(r-4.5-any)
MRFA_0.6.tar.gz(r-4.7-any)MRFA_0.6.tar.gz(r-4.6-any)
MRFA_0.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
MRFA/json (API)

# Install 'MRFA' in R:
install.packages('MRFA', repos = c('https://chihli.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.98 score 32 scripts 177 downloads 3 mentions 6 exports 20 dependencies

Last updated from:1d414715d5. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK125
source / vignettesOK160
linux-release-x86_64OK126
macos-release-arm64OK122
macos-oldrel-arm64OK85
windows-develOK95
windows-releaseOK96
windows-oldrelOK86
wasm-releaseOK103

Exports:aic.MRFAbic.MRFAconfidence.MRFAcv.MRFAMRFA_fitpredict.MRFA

Dependencies:codetoolsdotCall64fieldsforeachglmnetgrplassoiteratorslatticemapsMatrixplyrrandtoolboxRColorBrewerRcppRcppEigenrngWELLshapespamsurvivalviridisLite