Package: hierSDR 0.1

hierSDR: Hierarchical Sufficient Dimension Reduction

Provides semiparametric sufficient dimension reduction for central mean subspaces for heterogeneous data defined by combinations of binary factors (such as chronic conditions). Subspaces are estimated to be hierarchically nested to respect the structure of subpopulations with overlapping characteristics. This package is an implementation of the proposed methodology of Huling and Yu (2021) <doi:10.1111/biom.13546>.

Authors:Jared Huling [aut, cre]

hierSDR_0.1.tar.gz
hierSDR_0.1.zip(r-4.5)hierSDR_0.1.zip(r-4.4)hierSDR_0.1.zip(r-4.3)
hierSDR_0.1.tgz(r-4.5-any)hierSDR_0.1.tgz(r-4.4-any)hierSDR_0.1.tgz(r-4.3-any)
hierSDR_0.1.tar.gz(r-4.5-noble)hierSDR_0.1.tar.gz(r-4.4-noble)
hierSDR_0.1.tgz(r-4.4-emscripten)hierSDR_0.1.tgz(r-4.3-emscripten)
hierSDR.pdf |hierSDR.html✨
hierSDR/json (API)

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

Bug tracker:https://github.com/jaredhuling/hiersdr/issues

On CRAN:

Conda:

3.00 score 2 stars 9 scripts 191 downloads 7 exports 10 dependencies

Last updated 4 years agofrom:f5fe8e2720. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 08 2025
R-4.5-winOKMar 08 2025
R-4.5-macOKMar 08 2025
R-4.5-linuxOKMar 08 2025
R-4.4-winOKMar 08 2025
R-4.4-macOKMar 08 2025
R-4.4-linuxOKMar 08 2025
R-4.3-winOKMar 08 2025
R-4.3-macOKMar 08 2025

Exports:anglehier.phd.nthier.sphdphdprojnormsemi.phdsimulate_data

Dependencies:latticelbfgslocfitMASSMatrixnloptrnumDerivoptimxpracmaRcpp