Package: mpath 0.4-2.26
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 (2024) <doi:10.1111/anzs.12409>.
Authors:
mpath_0.4-2.26.tar.gz
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mpath.pdf |mpath.html✨
mpath/json (API)
NEWS
# Install 'mpath' in R: |
install.packages('mpath', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/zhuwang46/mpath/issues
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Last updated 6 months agofrom:6ef263b886. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 25 2024 |
R-4.5-linux-x86_64 | OK | Nov 25 2024 |
Exports:be.zeroinflbreadRegcfun2numcheck_scompute_gcompute_wtconv2glmregconv2zipathcv.foldscv.glmregcv.glmreg_fitcv.glmregNBcv.irglmregcv.irglmreg_fitcv.irsvmcv.irsvm_fitcv.nclregcv.nclreg_fitcv.zipathestfunReggfuncglmregglmregNBhessianRegirglmirglmregirglmreg_fitirsvmirsvm_fitllfunloss2loss2_irsvmloss3meatRegnclncl_fitnclregnclreg_fitpredictzeroinfl1pval.zipathrzisandwichRegsestantuning.zipathupdate_wty2numy2num4glmzipathzipath_fit
Dependencies:bstcodetoolsdoParallelforeachgbmglmnetiteratorslatticeMASSMatrixnumDerivpsclRcppRcppEigenrpartshapesurvivalWeightSVM
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Rendered fromstatic_brcancer.pdf.asis
usingR.rsp::asis
on Nov 25 2024.Last update: 2017-10-23
Started: 2017-10-23
Classification of Cancer Patients with Penalized Robust Nonconvex Loss Functions (without Results)
Rendered frombrcancer.Rnw
usingknitr::knitr
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Started: 2018-07-21
KKT Conditions for Zero-Inflated Regression
Rendered fromkkt.Rnw
usingutils::Sweave
on Nov 25 2024.Last update: 2023-01-06
Started: 2019-04-15
Robust Generalized Linear Models
Rendered fromstatic_irglmExample.pdf.asis
usingR.rsp::asis
on Nov 25 2024.Last update: 2022-02-21
Started: 2022-02-21
Robust Support Vector Machines
Rendered fromstatic_irsvmExample.pdf.asis
usingR.rsp::asis
on Nov 25 2024.Last update: 2022-02-21
Started: 2022-02-21
Variable Selection for Zero-inflated and Overdispersed Data with Application to Health Care Demand in Germany
Rendered fromstatic_german.pdf.asis
usingR.rsp::asis
on Nov 25 2024.Last update: 2020-11-10
Started: 2015-06-10