Package: pre 1.0.7
Marjolein Fokkema
pre: Prediction Rule Ensembles
Derives prediction rule ensembles (PREs). Largely follows the procedure for deriving PREs as described in Friedman & Popescu (2008; <doi:10.1214/07-AOAS148>), with adjustments and improvements. The main function pre() derives prediction rule ensembles consisting of rules and/or linear terms for continuous, binary, count, multinomial, and multivariate continuous responses. Function gpe() derives generalized prediction ensembles, consisting of rules, hinge and linear functions of the predictor variables.
Authors:
pre_1.0.7.tar.gz
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pre_1.0.7.tgz(r-4.4-any)pre_1.0.7.tgz(r-4.3-any)
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pre_1.0.7.tgz(r-4.4-emscripten)pre_1.0.7.tgz(r-4.3-emscripten)
pre.pdf |pre.html✨
pre/json (API)
NEWS
# Install 'pre' in R: |
install.packages('pre', repos = c('https://marjoleinf.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/marjoleinf/pre/issues
- carrillo - Data on personality characteristics and depressive symptom severity
Last updated 5 months agofrom:cc16758b3c. Checks:OK: 5 NOTE: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 18 2024 |
R-4.5-win | NOTE | Oct 18 2024 |
R-4.5-linux | NOTE | Oct 18 2024 |
R-4.4-win | OK | Oct 18 2024 |
R-4.4-mac | OK | Oct 18 2024 |
R-4.3-win | OK | Oct 18 2024 |
R-4.3-mac | OK | Oct 18 2024 |
Exports:bsnullinteractcaret_pre_modelcorplotcvpreeTermexplaingpegpe_cv.glmnetgpe_earthgpe_lineargpe_rules_pregpe_samplegpe_treesimportanceinteractlTermmaxdepth_samplermi_meanmi_prepairplotpreprune_prerare_level_samplerrTermsingleplot
Dependencies:clicodetoolsearthforeachFormulaglmnetglueinumiteratorslatticelibcoinlifecyclemagrittrMatrixMatrixModelsmvtnormpartykitplotmoplotrixRcppRcppEigenrlangrpartshapestringistringrsurvivalvctrs
Dealing with missing data in fitting prediction rule ensembles
Rendered fromMissingness.Rmd
usingknitr::rmarkdown
on Oct 18 2024.Last update: 2024-06-20
Started: 2022-02-24
Speeding up computations
Rendered fromspeed.Rmd
usingknitr::rmarkdown
on Oct 18 2024.Last update: 2024-06-20
Started: 2024-01-12
More adaptive or relaxed: Fitting sparser rule ensembles with relaxed and/or adaptive lasso
Rendered fromrelaxed.Rmd
usingknitr::rmarkdown
on Oct 18 2024.Last update: 2024-06-20
Started: 2022-03-15
Tuning the parameters of function pre
Rendered fromTuning.Rmd
usingknitr::rmarkdown
on Oct 18 2024.Last update: 2024-06-20
Started: 2022-02-24