Package: robregcc 1.1

Aditya Mishra

robregcc: Robust Regression with Compositional Covariates

We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arxiv:1909.04990>.

Authors:Aditya Mishra [aut, cre], Christian Muller [ctb]

robregcc_1.1.tar.gz
robregcc_1.1.tar.gz(r-4.7-arm64)robregcc_1.1.tar.gz(r-4.7-x86_64)robregcc_1.1.tar.gz(r-4.6-arm64)robregcc_1.1.tar.gz(r-4.6-x86_64)
robregcc_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
robregcc/json (API)

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

Bug tracker:https://github.com/amishra-stats/robregcc/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • simulate_robregcc - Simulated date for testing functions in the robregcc package (sparse setting).

On CRAN:

Conda:

openblascpp

1.77 score 59 scripts 200 downloads 10 exports 4 dependencies

Last updated from:3ba1185aba. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK157
linux-devel-x86_64OK126
source / vignettesOK158
linux-release-arm64OK165
linux-release-x86_64OK487
wasm-releaseOK104

Exports:classoclasso_pathcoef_cccpsc_spplot_cvplot_pathplot_residrobregcc_optionrobregcc_simrobregcc_sp

Dependencies:magrittrMASSRcppRcppArmadillo