Package: risdr
Title: Regularised and Information-Theoretic Sufficient Dimension
        Reduction
Version: 0.3.1
Authors@R: 
    person("Kabir", "Olorede", email = "kabirolorede@gmail.com", role = c("aut", "cre"))
Description: Implements covariance-stabilised sufficient dimension reduction
 for continuous responses with information-theoretic structural dimension
 selection. Supported methods include sliced inverse regression, sliced
 average variance estimation, directional regression, and principal Hessian
 directions. Sample, ridge, Oracle Approximating Shrinkage, Ledoit-Wolf, and
 Maximum Entropy Covariance (MEC) estimators are provided alongside
 prediction, resampling, simulation, and diagnostic utilities. The
 sufficient dimension reduction
 methods build on Li (1991) <doi:10.1080/01621459.1991.10475035>, Li (1992)
 <doi:10.1080/01621459.1992.10476258>, and Li and Wang (2007)
 <doi:10.1198/016214507000000536>. Covariance shrinkage follows Olorede and
 Yahya (2019) <doi:10.48550/arXiv.1909.13017>, Ledoit and
 Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4> and Chen et al. (2010)
 <doi:10.1109/TSP.2010.2053029>.
License: GPL (>= 3)
Encoding: UTF-8
Language: en-GB
Depends: R (>= 4.1.0)
RoxygenNote: 7.3.3
Imports: stats, graphics, utils, MASS, Matrix
Suggests: dplyr, knitr, readr, rmarkdown, testthat (>= 3.0.0), VIM
Config/testthat/edition: 3
Config/Needs/coverage: covr, xml2
Config/Needs/website: r-lib/pkgdown
VignetteBuilder: knitr, rmarkdown
URL: https://github.com/ilovemaths/risdr,
        https://ilovemaths.github.io/risdr/
BugReports: https://github.com/ilovemaths/risdr/issues
NeedsCompilation: no
Packaged: 2026-07-19 20:33:30 UTC; DR OLOREDE
Author: Kabir Olorede [aut, cre]
Maintainer: Kabir Olorede <kabirolorede@gmail.com>
Repository: CRAN
Date/Publication: 2026-07-28 16:40:02 UTC
Built: R 4.7.0; ; 2026-07-28 23:51:11 UTC; windows
