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
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robregcc.pdf |robregcc.html✨
robregcc/json (API)

# Install 'robregcc' in R:
install.packages('robregcc', repos = c('https://amishra-stats.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).
  • simulate_robregcc_nsp - Simulated date for testing functions in the robregcc package (non-sparse setting).
  • simulate_robregcc_sp - Simulated date for testing functions in the robregcc package (sparse setting).

On CRAN:

Conda:

openblascpp

4.11 score 6 stars 43 scripts 153 downloads 11 exports 4 dependencies

Last updated 4 years agofrom:0ea849d8c9. Checks:1 OK, 11 ERROR. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 06 2025
R-4.5-win-x86_64ERRORMar 06 2025
R-4.5-mac-x86_64ERRORMar 06 2025
R-4.5-mac-aarch64ERRORMar 06 2025
R-4.5-linux-x86_64ERRORMar 06 2025
R-4.4-win-x86_64ERRORMar 06 2025
R-4.4-mac-x86_64ERRORMar 06 2025
R-4.4-mac-aarch64ERRORMar 06 2025
R-4.4-linux-x86_64ERRORMar 06 2025
R-4.3-win-x86_64ERRORMar 06 2025
R-4.3-mac-x86_64ERRORMar 06 2025
R-4.3-mac-aarch64ERRORMar 06 2025

Exports:classoclasso_pathcoef_cccpsc_spplot_cvplot_pathplot_residrobregcc_optionrobregcc_simrobregcc_sim2robregcc_sp

Dependencies:magrittrMASSRcppRcppArmadillo

Readme and manuals

Help Manual

Help pageTopics
Estimate parameters of linear regression model with compositional covariates using method suggested by Pixu shi.classo
Compute solution path of constrained lasso.classo_path
Extract coefficients estimate from the sparse version of the robregcc fitted object.coef_cc
Principal sensitivity component analysis with compositional covariates in non-sparse setting.cpsc_nsp
Principal sensitivity component analysis with compositional covariates in sparse setting.cpsc_sp
Subfunction for principal sensitive component analysis:getscsfun
Subfunction for principal sensitive component analysis (sparsity):getscsfun.sp
Plot cross-validation error plotplot_cv
Plot solution path at different value of lambdaplot_path
Plot residuals estimate from robregcc objectplot_resid
Extract residuals estimate from the sparse version of the robregcc fitted object.residuals residuals.robregcc
Robust model estimation approach for regression with compositional covariates.robregcc_nsp
Control parameter for model estimation:robregcc_option
Simulation datarobregcc_sim
Simulation data with mis-specified model parametersrobregcc_sim2
Robust model estimation approach for regression with compositional covariates.robregcc_sp
Simulated date for testing functions in the robregcc package (sparse setting).simulate_robregcc
Simulated date for testing functions in the robregcc package (non-sparse setting).simulate_robregcc_nsp
Simulated date for testing functions in the robregcc package (sparse setting).simulate_robregcc_sp