Package: mpath 0.4-2.22

Zhu Wang

mpath: Regularized Linear Models

Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2021) <doi:10.1007/s11749-021-00770-2>, Wang (2020) <arxiv:2010.02848>.

Authors:Zhu Wang, with contributions from Achim Zeileis, Simon Jackman, Brian Ripley, and Patrick Breheny

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mpath/json (API)
NEWS

# Install 'mpath' in R:
install.packages('mpath', repos = c('https://zhuwang46.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/zhuwang46/mpath/issues

Uses libs:
  • openblas– Optimized BLAS
  • fortran– Runtime library for GNU Fortran applications

On CRAN:

50 exports 1 stars 2.26 score 20 dependencies 4 dependents 4 mentions 125 scripts 958 downloads

Last updated 3 years agofrom:8d251b1587. Checks:OK: 1 ERROR: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 08 2024
R-4.5-win-x86_64ERRORSep 08 2024
R-4.5-linux-x86_64ERRORSep 08 2024
R-4.4-win-x86_64ERRORSep 08 2024
R-4.4-mac-x86_64ERRORSep 08 2024
R-4.4-mac-aarch64ERRORSep 08 2024
R-4.3-win-x86_64ERRORSep 08 2024
R-4.3-mac-x86_64ERRORSep 08 2024
R-4.3-mac-aarch64ERRORSep 08 2024

Exports:be.zeroinflbreadRegccglmccglmregccglmreg_fitccsvmccsvm_fitcfun2numcheck_scompute_gcompute_wtconv2glmregconv2zipathcv.ccglmregcv.ccglmreg_fitcv.ccsvmcv.ccsvm_fitcv.foldscv.glmregcv.glmreg_fitcv.glmregNBcv.nclregcv.nclreg_fitcv.zipathestfunReggfuncglmregglmregNBhessianRegllfunloss2loss2_ccsvmloss3meatRegnclncl_fitnclregnclreg_fitpredictzeroinfl1pval.zipathrzisandwichRegsestantuning.zipathupdate_wty2numy2num4glmzipathzipath_fit

Dependencies:bstclustercodetoolsdoParallelforeachgbmglmnetiteratorslatticeMASSMatrixnumDerivpamrpsclRcppRcppEigenrpartshapesurvivalWeightSVM

Classification of Cancer Patients with Penalized Robust Nonconvex Loss Functions (with Results)

Rendered fromstatic_brcancer.pdf.asisusingR.rsp::asison Sep 08 2024.

Last update: 2019-01-24
Started: 2019-01-24

Classification of Cancer Patients with Penalized Robust Nonconvex Loss Functions (without Results)

Rendered frombrcancer.Rnwusingknitr::knitron Sep 08 2024.

Last update: 2021-04-05
Started: 2019-01-24

KKT Conditions for Zero-Inflated Regression

Rendered fromkkt.Rnwusingutils::Sweaveon Sep 08 2024.

Last update: 2019-11-20
Started: 2019-11-20

Robust Generalized Linear Models

Rendered fromstatic_ccglmExample.pdf.asisusingR.rsp::asison Sep 08 2024.

Last update: 2020-11-12
Started: 2020-11-12

Robust Support Vector Machines

Rendered fromstatic_ccsvmExample.pdf.asisusingR.rsp::asison Sep 08 2024.

Last update: 2020-11-12
Started: 2020-11-12

Variable Selection for Zero-inflated and Overdispersed Data with Application to Health Care Demand in Germany

Rendered fromstatic_german.pdf.asisusingR.rsp::asison Sep 08 2024.

Last update: 2020-11-12
Started: 2019-01-24

Readme and manuals

Help Manual

Help pageTopics
conduct backward stepwise variable elimination for zero inflated count regressionbe.zeroinfl
Bread for Sandwiches in Regularized EstimatorsbreadReg breadReg.zipath
Breast feeding decisionbreastfeed
fit a CC-estimator for robust generalized linear modelsccglm ccglm.formula
Fit a penalized CC-estimatorccglmreg ccglmreg.default ccglmreg.formula ccglmreg.matrix
Internal function for penalized CC-estimatorsccglmreg_fit
fit case weighted support vector machines with robust loss functionsccsvm ccsvm.default ccsvm.formula ccsvm.matrix coef.ccsvm
Fit iteratively re-weighted support vector machines for robust loss functionsccsvm_fit
Compute concave function valuescompute_g
Weight value from concave functioncompute_wt
convert glm object to class glmregconv2glmreg
convert zeroinfl object to class zipathconv2zipath
Cross-validation for ccglmregcoef.cv.ccglmreg cv.ccglmreg cv.ccglmreg.default cv.ccglmreg.formula cv.ccglmreg.matrix plot.cv.ccglmreg
Internal function of cross-validation for ccglmregcv.ccglmreg_fit
Cross-validation for ccsvmcv.ccsvm cv.ccsvm.default cv.ccsvm.formula cv.ccsvm.matrix
Internal function of cross-validation for ccsvmcv.ccsvm_fit
Cross-validation for glmregcoef.cv.glmreg cv.glmreg cv.glmreg.default cv.glmreg.formula cv.glmreg.matrix plot.cv.glmreg predict.cv.glmreg
Internal function of cross-validation for glmregcv.glmreg_fit
Cross-validation for glmregNBcv.glmregNB
Cross-validation for nclregcoef.cv.nclreg cv.nclreg cv.nclreg.default cv.nclreg.formula cv.nclreg.matrix plot.cv.nclreg
Internal function of cross-validation for nclregcv.nclreg_fit
Cross-validation for zipathcoef.cv.zipath cv.zipath cv.zipath.default cv.zipath.formula cv.zipath.matrix predict.cv.zipath
Cross-validation for zipathcv.zipath_fit
Doctor visitsdocvisits
Extract Empirical First Derivative of Log-likelihood FunctionestfunReg estfunReg.zipath
Convert response value to raw prediction in GLMgfunc
fit a GLM with lasso (or elastic net), snet or mnet regularizationdeviance.glmreg glmreg glmreg.default glmreg.formula glmreg.matrix logLik.glmreg
Internal function to fit a GLM with lasso (or elastic net), snet and mnet regularizationglmreg_fit
fit a negative binomial model with lasso (or elastic net), snet and mnet regularizationglmregNB glmregNegbin
Hessian Matrix of Regularized EstimatorshessianReg
Composite Loss Valueloss2
Composite Loss Value for epsilon-insensitive Typeloss2_ccsvm
Composite Loss Value for GLMloss3
Meat Matrix EstimatormeatReg
Methods for mpath ObjectsAIC.glmreg AIC.zipath BIC.glmreg BIC.zipath
fit a nonconvex loss based robust linear modelncl ncl.default ncl.formula ncl.matrix
Internal function to fit a nonconvex loss based robust linear modelncl_fit
Optimize a nonconvex loss with regularizationnclreg nclreg.default nclreg.formula nclreg.matrix
Internal function to fitting a nonconvex loss based robust linear model with regularizationnclreg_fit
plot coefficients from a "glmreg" objectplot.glmreg
Model predictions based on a fitted "glmreg" object.coef.glmreg predict.glmreg
Methods for zipath Objectscoef.zipath fitted.zipath logLik.zipath model.matrix.zipath predict.zipath predprob.zipath print.summary.zipath residuals.zipath summary.zipath terms.zipath
compute p-values from penalized zero-inflated model with multi-split datapval.zipath
random number generation of zero-inflated count responserzi
Making Sandwiches with Bread and Meat for Regularized EstimatorssandwichReg
Standard Error of Regularized Estimatorsse se.zipath
standardize variablesstan
Summary Method Function for Objects of Class 'glmregNB'print.summary.glmregNB summary.glmregNB
find optimal path for penalized zero-inflated modeltuning.zipath
Compute weight valueupdate_wt
Fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularizationzipath zipath.default zipath.formula zipath.matrix
Internal function to fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularizationzipath_fit