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✨
DESCRIPTION
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 278 downloads 7 exports 10 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64OK149
source / vignettesOK138
linux-release-x86_64OK177
macos-release-arm64OK120
macos-oldrel-arm64OK110
windows-develOK84
windows-releaseOK94
windows-oldrelOK88
wasm-releaseOK126

Exports:anglehier.phd.nthier.sphdphdprojnormsemi.phdsimulate_data

Dependencies:latticelbfgslocfitMASSMatrixnloptrnumDerivoptimxpracmaRcpp