Package: semidist 0.1.0
Zhuoxi Li
semidist: Measure Dependence Between Categorical and Continuous Variables
Semi-distance and mean-variance (MV) index are proposed to measure the dependence between a categorical random variable and a continuous variable. Test of independence and feature screening for classification problems can be implemented via the two dependence measures. For the details of the methods, see Zhong et al. (2023) <doi:10.1080/01621459.2023.2284988>; Cui and Zhong (2019) <doi:10.1016/j.csda.2019.05.004>; Cui, Li and Zhong (2015) <doi:10.1080/01621459.2014.920256>.
Authors:
semidist_0.1.0.tar.gz
semidist_0.1.0.tar.gz(r-4.5-noble)semidist_0.1.0.tar.gz(r-4.4-noble)
semidist_0.1.0.tgz(r-4.4-emscripten)semidist_0.1.0.tgz(r-4.3-emscripten)
semidist.pdf |semidist.html✨
semidist/json (API)
NEWS
| # Install 'semidist' in R: |
| install.packages('semidist', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/wzhong41/semidist/issues
Last updated 1 years agofrom:9f2a02f817. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 16 2024 |
| R-4.5-linux-x86_64 | OK | Dec 16 2024 |
Exports:MINTsemiautoMINTsemipermmvmv_sismv_testsd_sissd_testsdcorsdcovswitch_cat_repr
Dependencies:bootclicodetoolsdigestenergyFNNfurrrfutureglobalsgluegsllifecyclelistenvmagrittrparallellypurrrRcppRcppArmadillorlangvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Mutual information independence test (categorical-continuous case) | MINTsemiauto MINTsemiperm |
| Mean Variance (MV) statistics | mv |
| Feature screening via MV Index | mv_sis |
| MV independence test | mv_test |
| Print Method for Independence Tests Between Categorical and Continuous Variables | print.indtest |
| Feature screening via semi-distance correlation | sd_sis |
| Semi-distance independence test | sd_test |
| Semi-distance covariance and correlation statistics | sdcor sdcov |
| Switch the representation of a categorical object | switch_cat_repr |
| Estimate the trace of the covariance matrix and its square | tr_estimate |
