{ "_id": "6a153853acfb0bcc41d4fe07", "Package": "mpath", "Title": "Regularized Linear Models", "Version": "0.4-2.22", "Date": "2022-02-14", "Author": "Zhu Wang, with contributions from Achim Zeileis, Simon Jackman,\nBrian Ripley, and Patrick Breheny", "Maintainer": "Zhu Wang ", "Description": "Algorithms compute robust estimators for loss functions in\nthe concave convex (CC) family by the iteratively reweighted\nconvex optimization (IRCO), an extension of the iteratively\nreweighted least squares (IRLS). The IRCO reduces the weight of\nthe observation that leads to a large loss; it also provides\nweights to help identify outliers. Applications include robust\n(penalized) generalized linear models and robust support vector\nmachines. The package also contains penalized Poisson, negative\nbinomial, zero-inflated Poisson, zero-inflated negative\nbinomial regression models and robust models with non-convex\nloss functions. Wang et al. (2014) , Wang\net al. (2015) , Wang et al. (2016)\n, Wang (2021)\n, Wang (2020)\n.", "VignetteBuilder": "R.rsp, knitr", "License": "GPL-2", "URL": "https://github.com/zhuwang46/mpath", "BugReports": "https://github.com/zhuwang46/mpath", "Packaged": { "Date": "2026-05-26 06:00:27 UTC", "User": "root" }, "NeedsCompilation": "yes", "RoxygenNote": "7.1.1", "Repository": "https://zhuwang46.r-universe.dev", "Date/Publication": "2022-02-16 15:51:35 UTC", "RemoteUrl": "https://github.com/zhuwang46/mpath", "RemoteRef": "HEAD", "RemoteSha": "8d251b15873c67ca07034c52d8a9f78a85c94717", "MD5sum": "06647d346058542e224ae1a4915974ff", "_user": "zhuwang46", "_type": "src", "_file": "mpath_0.4-2.22.tar.gz", "_fileid": "0d45ff45025259791d07f8e63ffb04167b016f8db8872e7ab544782a59b792ed", "_filesize": 2911142, "_sha256": "0d45ff45025259791d07f8e63ffb04167b016f8db8872e7ab544782a59b792ed", "_created": "2026-05-26T06:00:27.000Z", "_published": "2026-05-26T06:06:11.686Z", "_distro": "noble", "_jobs": [ { "job": 77816412804, "time": 155, "config": 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"_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "methods", "role": "Depends" }, { "package": "glmnet", "role": "Depends" }, { "package": "pamr", "role": "Depends" }, { "package": "MASS", "role": "Imports" }, { "package": "pscl", "role": "Imports" }, { "package": "numDeriv", "role": "Imports" }, { "package": "foreach", "role": "Imports" }, { "package": "doParallel", "role": "Imports" }, { "package": "bst", "role": "Imports" }, { "package": "WeightSVM", "role": "Imports" }, { "package": "zic", "role": "Suggests" }, { "package": "R.rsp", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "gdata", "role": "Suggests" }, { "package": "e1071", "role": "Suggests" }, { "package": "SparseM", "role": "Suggests" }, { "package": "slam", "role": "Suggests" } ], "_owner": "zhuwang46", "_selfowned": true, "_usedby": 5, "_updates": [], "_tags": [], "_stars": 1, 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case weighted support vector machines with robust loss functions", "topics": [ "ccsvm", "ccsvm.default", "ccsvm.formula", "ccsvm.matrix", "coef.ccsvm" ] }, { "page": "ccsvm_fit", "title": "Fit iteratively re-weighted support vector machines for robust loss functions", "topics": [ "ccsvm_fit" ] }, { "page": "compute_g", "title": "Compute concave function values", "topics": [ "compute_g" ] }, { "page": "compute_wt", "title": "Weight value from concave function", "topics": [ "compute_wt" ] }, { "page": "conv2glmreg", "title": "convert glm object to class glmreg", "topics": [ "conv2glmreg" ] }, { "page": "conv2zipath", "title": "convert zeroinfl object to class zipath", "topics": [ "conv2zipath" ] }, { "page": "cv.ccglmreg", "title": "Cross-validation for ccglmreg", "topics": [ "coef.cv.ccglmreg", "cv.ccglmreg", "cv.ccglmreg.default", "cv.ccglmreg.formula", "cv.ccglmreg.matrix", "plot.cv.ccglmreg" ] }, { "page": "cv.ccglmreg_fit", "title": "Internal function of cross-validation for ccglmreg", "topics": [ "cv.ccglmreg_fit" ] }, { "page": "cv.ccsvm", "title": "Cross-validation for ccsvm", "topics": [ "cv.ccsvm", "cv.ccsvm.default", "cv.ccsvm.formula", "cv.ccsvm.matrix" ] }, { "page": "cv.ccsvm_fit", "title": "Internal function of cross-validation for ccsvm", "topics": [ "cv.ccsvm_fit" ] }, { "page": "cv.glmreg", "title": "Cross-validation for glmreg", "topics": [ "coef.cv.glmreg", "cv.glmreg", "cv.glmreg.default", "cv.glmreg.formula", "cv.glmreg.matrix", "plot.cv.glmreg", "predict.cv.glmreg" ] }, { "page": "cv.glmreg_fit", "title": "Internal function of cross-validation for glmreg", "topics": [ "cv.glmreg_fit" ] }, { "page": "cv.glmregNB", "title": "Cross-validation for glmregNB", "topics": [ "cv.glmregNB" ] }, { "page": "cv.nclreg", "title": "Cross-validation for nclreg", "topics": [ "coef.cv.nclreg", "cv.nclreg", "cv.nclreg.default", "cv.nclreg.formula", "cv.nclreg.matrix", "plot.cv.nclreg" ] }, { "page": "cv.nclreg_fit", "title": "Internal function of cross-validation for nclreg", "topics": [ "cv.nclreg_fit" ] }, { "page": "cv.zipath", "title": "Cross-validation for zipath", "topics": [ "coef.cv.zipath", "cv.zipath", "cv.zipath.default", "cv.zipath.formula", "cv.zipath.matrix", "predict.cv.zipath" ] }, { "page": "cv.zipath_fit", "title": "Cross-validation for zipath", "topics": [ "cv.zipath_fit" ] }, { "page": "docvisits", "title": "Doctor visits", "topics": [ "docvisits" ] }, { "page": "estfunReg", "title": "Extract Empirical First Derivative of Log-likelihood Function", "topics": [ "estfunReg", "estfunReg.zipath" ] }, { "page": "gfunc", "title": "Convert response value to raw prediction in GLM", "topics": [ "gfunc" ] }, { "page": "glmreg", "title": "fit a GLM with lasso (or elastic net), snet or mnet regularization", "topics": [ "deviance.glmreg", "glmreg", "glmreg.default", "glmreg.formula", "glmreg.matrix", "logLik.glmreg" ] }, { "page": "glmreg_fit", "title": "Internal function to fit a GLM with lasso (or elastic net), snet and mnet regularization", "topics": [ "glmreg_fit" ] }, { "page": "glmregNB", "title": "fit a negative binomial model with lasso (or elastic net), snet and mnet regularization", "topics": [ "glmregNB", "glmregNegbin" ] }, { "page": "hessianReg", "title": "Hessian Matrix of Regularized Estimators", "topics": [ "hessianReg" ] }, { "page": "loss2", "title": "Composite Loss Value", "topics": [ "loss2" ] }, { "page": "loss2_ccsvm", "title": "Composite Loss Value for epsilon-insensitive Type", "topics": [ "loss2_ccsvm" ] }, { "page": "loss3", "title": "Composite Loss Value for GLM", "topics": [ "loss3" ] }, { "page": "meatReg", "title": "Meat Matrix Estimator", "topics": [ "meatReg" ] }, { "page": "methods", "title": "Methods for mpath Objects", "topics": [ "AIC.glmreg", "AIC.zipath", "BIC.glmreg", "BIC.zipath" ] }, { "page": "ncl", "title": "fit a nonconvex loss based robust linear model", "topics": [ "ncl", "ncl.default", "ncl.formula", "ncl.matrix" ] }, { "page": "ncl_fit", "title": "Internal function to fit a nonconvex loss based robust linear model", "topics": [ "ncl_fit" ] }, { "page": "nclreg", "title": "Optimize a nonconvex loss with regularization", "topics": [ "nclreg", "nclreg.default", "nclreg.formula", "nclreg.matrix" ] }, { "page": "nclreg_fit", "title": "Internal function to fitting a nonconvex loss based robust linear model with regularization", "topics": [ "nclreg_fit" ] }, { "page": "plot.glmreg", "title": "plot coefficients from a \"glmreg\" object", "topics": [ "plot.glmreg" ] }, { "page": "predict.glmreg", "title": "Model predictions based on a fitted \"glmreg\" object.", "topics": [ "coef.glmreg", "predict.glmreg" ] }, { "page": "predict.zipath", "title": "Methods for zipath Objects", "topics": [ "coef.zipath", "fitted.zipath", "logLik.zipath", "model.matrix.zipath", "predict.zipath", "predprob.zipath", "print.summary.zipath", "residuals.zipath", "summary.zipath", "terms.zipath" ] }, { "page": "p_zipath", "title": "compute p-values from penalized 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"topics": [ "zipath", "zipath.default", "zipath.formula", "zipath.matrix" ] }, { "page": "zipath_fit", "title": "Internal function to fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularization", "topics": [ "zipath_fit" ] } ], "_rundeps": [ "bst", "cluster", "codetools", "doParallel", "foreach", "gbm", "glmnet", "iterators", "lattice", "MASS", "Matrix", "numDeriv", "pamr", "pscl", "Rcpp", "RcppEigen", "rpart", "shape", "survival", "WeightSVM" ], "_sysdeps": [ { "shlib": "libblas", "package": "libopenblas0-pthread", "source": "openblas", "version": "0.3.26+ds-1ubuntu0.1", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libgfortran", "package": "libgfortran5", "source": "gcc", "version": "14.2.0-4ubuntu2~24.04.1", "name": "fortran", "homepage": "http://gcc.gnu.org/", "description": "Runtime library for GNU Fortran applications" } ], 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