Package: sssvcqr 0.0.4

Houjian Hou

sssvcqr: Sparse-Smooth Spatially Varying Coefficient Quantile Regression

Implements sparse-smooth spatially varying coefficient quantile regression (SS-SVCQR), combining quantile regression of Koenker and Bassett (1978) <doi:10.2307/1913643>, grouped variable selection of Yuan and Lin (2006) <doi:10.1111/j.1467-9868.2005.00532.x>, graph regularization, and the alternating direction method of multipliers of Boyd et al. (2011) <doi:10.1561/2200000016>. The package provides graph-regularized estimation, spatially blocked cross-validation, prediction, diagnostics, and simulation helpers for global-local spatial quantile regression.

Authors:Houjian Hou [aut, cre]

sssvcqr_0.0.4.tar.gz
sssvcqr_0.0.4.tar.gz(r-4.7-any)sssvcqr_0.0.4.tar.gz(r-4.6-any)
sssvcqr_0.0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
sssvcqr/json (API)
NEWS

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

Bug tracker:https://github.com/stork343/sssvcqr/issues

Datasets:

On CRAN:

Conda:

3.00 score 4 scripts 7 exports 12 dependencies

Last updated from:6bb7d0e7fb. Checks:4 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK120
source / vignettesOK193
linux-release-x86_64OK129
wasm-releaseOK122

Exports:build_graph_laplaciancv_ss_svcqrkkt_sssvcqrmake_spatial_foldsselection_recovery_tablesimulate_sssvcqr_datass_svcqr

Dependencies:clicpp11FNNglueigraphlatticelifecyclemagrittrMatrixpkgconfigrlangvctrs

Getting Started with sssvcqr

Houjian Hou

Rendered fromsssvcqr-introduction.Rmdusingknitr::rmarkdownon May 15 2026.

Last update: 2026-05-15
Started: 2026-05-15

Lucas County Housing Example

Houjian Hou

Rendered fromlucas-county-example.Rmdusingknitr::rmarkdownon May 15 2026.

Last update: 2026-05-15
Started: 2026-05-15