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-4.7)hierSDR_0.1.zip(r-4.6)hierSDR_0.1.zip(r-4.5)
hierSDR_0.1.tgz(r-4.6-any)hierSDR_0.1.tgz(r-4.5-any)
hierSDR_0.1.tar.gz(r-4.7-any)hierSDR_0.1.tar.gz(r-4.6-any)
hierSDR_0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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 155 downloads 7 exports 10 dependencies

Last updated from:f5fe8e2720. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK152
source / vignettesOK126
linux-release-x86_64OK161
macos-release-arm64OK89
macos-oldrel-arm64OK77
windows-develOK79
windows-releaseOK90
windows-oldrelOK79
wasm-releaseOK110

Exports:anglehier.phd.nthier.sphdphdprojnormsemi.phdsimulate_data

Dependencies:latticelbfgslocfitMASSMatrixnloptrnumDerivoptimxpracmaRcpp