{ "_id": "6ad9ceb3db07788da70aa421", "Package": "RobMixReg", "Type": "Package", "Title": "Robust Mixture Regression", "Version": "1.1.3", "Date": "2026-06-01", "Authors@R": "c(person(\"Sha\", \"Cao\", email = \"shacao@iu.edu\", role = c(\"aut\", \"cph\",\"ths\")),\nperson(\"Wennan\", \"Chang\", email = \"changwn.nku@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Chi\", \"Zhang\", email = \"czhang87@iu.edu\", role = c(\"aut\", \"ctb\",\"ths\"))\n)", "Author": "Sha Cao [aut, cph, ths], Wennan Chang [aut, cre], Chi Zhang\n[aut, ctb, ths]", "Maintainer": "Wennan Chang ", "Description": "Finite mixture models are a popular technique for\nmodelling unobserved heterogeneity or to approximate general\ndistribution functions in a semi-parametric way. They are used\nin a lot of different areas such as astronomy, biology,\neconomics, marketing or medicine. This package is the\nimplementation of popular robust mixture regression methods\nbased on different algorithms including: fleximix, finite\nmixture models and latent class regression; CTLERob,\ncomponent-wise adaptive trimming likelihood estimation; mixbi,\nbi-square estimation; mixL, Laplacian distribution; mixt,\nt-distribution; TLE, trimmed likelihood estimation. The\nimplemented algorithms includes: CTLERob stands for\nComponent-wise adaptive Trimming Likelihood Estimation based\nmixture regression; mixbi stands for mixture regression based\non bi-square estimation; mixLstands for mixture regression\nbased on Laplacian distribution; TLE stands for Trimmed\nLikelihood Estimation based mixture regression. For more detail\nof the algorithms, please refer to below references. Reference:\nChun Yu, Weixin Yao, Kun Chen (2017) .\nNeyKov N, Filzmoser P, Dimova R et al. (2007)\n. Bai X, Yao W. Boyer JE (2012)\n. Wennan Chang, Xinyu Zhou,\nYong Zang, Chi Zhang, Sha Cao (2020)\n.", "License": "GPL", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "URL": "https://changwn.github.io/RobMixReg/", "BugReports": "https://github.com/changwn/RobMixReg/issues", "Repository": "https://changwn.r-universe.dev", "Date/Publication": "2026-06-01 22:43:34 UTC", "RemoteUrl": "https://github.com/changwn/robmixreg", "RemoteRef": "HEAD", "RemoteSha": "97c9b4954d135eea16b96f20dc08c77fae5334bb", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-22 08:47:38 UTC", "User": "root" }, "_user": "changwn", "_type": "src", "_file": "RobMixReg_1.1.3.tar.gz", "_fileid": "https://r2.ropensci.org/15870109d0f75a44724bca50f8c1628b300552a8ebcf4e3f058285c96a601dac", "_filesize": 2468024, "_sha256": "15870109d0f75a44724bca50f8c1628b300552a8ebcf4e3f058285c96a601dac", "_expires": "2027-01-30T08:52:01.000Z", "_created": "2026-10-22T08:47:38.000Z", "_published": "2026-10-22T08:52:03.348Z", "_upstream": "https://github.com/changwn/robmixreg", "_status": "success", "_maintainer": { "name": "Wennan Chang", "email": "changwn.nku@gmail.com", "login": "changwn", "description": "Ph.D. working on computation biology and mathmatical modeling for biomedical data analysis.", "uuid": 18462843 }, "_jobs": [ { "job": 119729425335, "time": 122, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "WARNING", "artifact": "12413436492" }, { "job": 119729425239, "time": 125, "config": "linux-release-x86_64", "r": "4.6.1", "check": "WARNING", "artifact": "12412944224" }, { "job": 119729425233, "time": 224, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "WARNING", "artifact": "12413825825" }, { "job": 119729425309, "time": 225, "config": "macos-release-arm64", "r": "4.6.1", "check": "WARNING", "artifact": "12413910576" }, { "job": 119728088141, "time": 183, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12412837973" }, { "job": 119729425187, "time": 112, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12413591501" }, { "job": 119729425214, "time": 104, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "WARNING", "artifact": "12413830348" }, { "job": 119729425435, "time": 131, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "WARNING", "artifact": "12412853318" }, { "job": 119729425430, "time": 100, "config": "windows-release-x86_64", "r": "4.6.1", "check": "WARNING", "artifact": "12413371599" } ], "_buildurl": "https://github.com/r-universe/changwn/actions/runs/39750024711", "_commit": { "id": "97c9b4954d135eea16b96f20dc08c77fae5334bb", "author": "WC ", "committer": "WC ", "message": "Remove Rcheck folder from repo and ignore it\n", "time": 1780353814 }, "_distro": "resolute", "_host": "GitHub-Actions", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "flexmix", "role": "Imports" }, { "package": "robustbase", "role": "Imports" }, { "package": "gtools", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "robust", "role": "Imports" }, { "package": "lars", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "scales", "role": "Imports" }, { "package": "gplots", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "RColorBrewer", "role": "Imports" }, { "package": "stats", "role": "Imports" } ], "_owner": "changwn", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2026-23", "n": 5 } ], "_tags": [], "_stars": 0, "_contributors": [ { "user": "changwn", "count": 124, "uuid": 18462843 } ], "_userbio": { "uuid": 18462843, "type": "user", "name": "Wennan Chang", "followers": 63, "description": "Ph.D. working on computation biology and mathmatical modeling for biomedical data analysis." }, "_downloads": { "count": 368, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/RobMixReg" }, "_devurl": "https://github.com/changwn/robmixreg", "_pkgdown": "https://changwn.github.io/RobMixReg/", "_searchresults": 13, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "extra/RobMixReg.html" ], "_homeurl": "https://github.com/changwn/robmixreg", "_realowner": "changwn", "_cranurl": true, "_releases": [ { "version": "0.1.0", "date": "2020-03-25" }, { "version": "0.2.0", "date": "2020-05-11" }, { "version": "0.2.1", "date": "2020-05-26" }, { "version": "1.0.0", "date": "2020-06-08" }, { "version": "1.1.0", "date": "2020-08-05" }, { "version": "1.1.3", "date": "2026-06-02" } ], "_exports": [ "biscalew", "bisquare", "blockMap", "calcobj_i", "compPlot", "Compute_Rbase_SVD", "CSMR", "CSMR_one", "CSMR_predict", "CSMR_train", "CTLERob", "denLp", "DeOut", "dmnorm", "estimClustPar_i", "findClustAssig_i", "flexmix_2", "getini", "InitClusters_i", "lars.lsa", "logLik_mixtureReg", "lsa", "mixlinrb_bi", "mixlinrb_bione", "mixLp", "mixLp_one", "mixtureReg", "MLM", "MLM_bic", "MLM_cv", "orderedLines", "plot_CTLE", "plot_mixtureReg", "plot_mixtureRegList", "Rec_Lm", "restr.diffax_i1", "restr.diffax_i2", "restr2_eigenv", "rmr", "simu_data_sparse", "simu_func", "simu_low", "ssclmat", "TLE", "TreatSingularity", "trim.cwm" ], "_datasets": [ { "name": "CCLE_data", "title": "RobMixReg package built-in CCLE data.", "object": "CCLE_data", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "colon_data", "title": "RobMixReg package built-in Colon cancer data.", "object": "colon_data", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "gaussData", "title": "RobMixReg package built-in gaussian example data.", "object": "gaussData", "class": [ "data.frame" ], "fields": [ "x", "y", "c" ], "rows": 100, "table": true, "tojson": true }, { "name": "simuData", "title": "RobMixReg package built-in simulated example data.", "object": "simuData", "class": [ "data.frame" ], "fields": [ "X1", "X2", "y", "c", "outlier" ], "rows": 500, "table": true, "tojson": true } ], "_help": [ { "page": "biscalew", "title": "biscalew :Robust M-estimates for scale.", "topics": [ "biscalew" ] }, { "page": "bisquare", "title": "bisquare : Robust estimates for mean.", "topics": [ "bisquare" ] }, { "page": "blockMap", "title": "Plot the coefficient matrix.", "topics": [ "blockMap" ] }, { "page": "calcobj_i", "title": "calcobj_i: it calculates the objective function", "topics": [ "calcobj_i" ] }, { "page": "CCLE_data", "title": "RobMixReg package built-in CCLE data.", "topics": [ "CCLE_data" ] }, { "page": "colon_data", "title": "RobMixReg package built-in Colon cancer data.", "topics": [ "colon_data" ] }, { "page": "compPlot", "title": "The plot wrapper function.", "topics": [ "compPlot" ] }, { "page": "Compute_Rbase_SVD", "title": "Compute the row space using SVD.", "topics": [ "Compute_Rbase_SVD" ] }, { "page": "CSMR", "title": "The main function of the RBSL algorithm.", "topics": [ "CSMR" ] }, { "page": "CSMR_one", "title": "Perform the RBSL algorithm one times.", "topics": [ "CSMR_one" ] }, { "page": "CSMR_predict", "title": "The predict function of the CSMR algorithm.", "topics": [ "CSMR_predict" ] }, { "page": "CSMR_train", "title": "The train function of the CSMR algorithm.", "topics": [ "CSMR_train" ] }, { "page": "CTLERob-methods", "title": "CTLERob: Robust mixture regression based on component-wise adaptive trimming likelihood estimation.", "topics": [ "CTLERob", "CTLERob,formula,ANY,ANY,numeric,ANY-method", "CTLERob,formula,ANY,ANY,numeric-method" ] }, { "page": "denLp", "title": "denLp : Density function for Laplace distribution.", "topics": [ "denLp" ] }, { "page": "DeOut", "title": "DeOut : Detect outlier observations.", "topics": [ "DeOut" ] }, { "page": "dmnorm", "title": "dmnorm: multivariate normal density function", "topics": [ "dmnorm" ] }, { "page": "estimClustPar_i", "title": "estimClustPar_i: see original CWM method", "topics": [ "estimClustPar_i" ] }, { "page": "findClustAssig_i", "title": "findClustAssig_i: it obtains the assigment corresponding to each point to the K populations and the set of trimmed points", "topics": [ "findClustAssig_i" ] }, { "page": "flexmix_2", "title": "flexmix_2: Multiple runs of MLE based mixture regression to stabilize the output.", "topics": [ "flexmix_2" ] }, { "page": "gaussData", "title": "RobMixReg package built-in gaussian example data.", "topics": [ "gaussData" ] }, { "page": "getini", "title": "getini: see original CWM method", "topics": [ "getini" ] }, { "page": "InitClusters_i", "title": "InitClusters_i: see original CWM method", "topics": [ "InitClusters_i" ] }, { "page": "lars.lsa", "title": "lars variant for LSA.", "topics": [ "lars.lsa" ] }, { "page": "logLik_mixtureReg", "title": "Obtain Log-likelihood from a mixtureReg Object", "topics": [ "logLik_mixtureReg" ] }, { "page": "lsa", "title": "Least square approximation. This version Oct 19, 2006.", "topics": [ "lsa" ] }, { "page": "mixlinrb_bi-methods", "title": "mixlinrb_bi: mixlinrb_bione estimates the mixture regression parameters robustly using bisquare function based on multiply initial value.", "topics": [ "mixlinrb_bi", "mixlinrb_bi,formula,ANY,numeric,numeric-method" ] }, { "page": "mixlinrb_bione", "title": "mixlinrb_bione : mixlinrb_bione estimates the mixture regression parameters robustly using bisquare function based on one initial value.", "topics": [ "mixlinrb_bione" ] }, { "page": "mixLp-methods", "title": "mixLp : mixLp_one estimates the mixture regression parameters robustly using Laplace distribution based on multiply initial value..", "topics": [ "CTLE,formula,ANY,numeric,numeric-method", "mixLp", "mixLp,formula,ANY,numeric,numeric-method" ] }, { "page": "mixLp_one", "title": "mixLp_one : mixLp_one estimates the mixture regression parameters robustly using Laplace distribution based on one initial value.", "topics": [ "mixLp_one" ] }, { "page": "mixtureReg", "title": "Function to Fit Mixture of Regressions", "topics": [ "mixtureReg" ] }, { "page": "MLM", "title": "The main function of mining the latent relationship among variables.", "topics": [ "MLM" ] }, { "page": "MLM_bic", "title": "Model selection function for low dimension data.", "topics": [ "MLM_bic" ] }, { "page": "MLM_cv", "title": "Cross validation (fold-5) function for high dimension data.", "topics": [ "MLM_cv" ] }, { "page": "orderedLines", "title": "Sort by X Coordinates and Add Line to a Plot", "topics": [ "orderedLines" ] }, { "page": "plot_CTLE-methods", "title": "plot_CTLE: Plot the mixture/single regression line(s) in a simply function.", "topics": [ "CTLERob,formula,ANY,numeric,ANY", "plot_CTLE", "plot_CTLE,formula,ANY,numeric-method" ] }, { "page": "plot_mixtureReg", "title": "Plot Fit and Mixing Probability of a mixtureReg Object", "topics": [ "plot_mixtureReg" ] }, { "page": "plot_mixtureRegList", "title": "Plot a List of mixtureReg Objects", "topics": [ "plot_mixtureRegList" ] }, { "page": "Rec_Lm", "title": "Adaptive lasso.", "topics": [ "Rec_Lm" ] }, { "page": "restr.diffax_i1", "title": "restr.diffax_i1: see original CWM method", "topics": [ "restr.diffax_i1" ] }, { "page": "restr.diffax_i2", "title": "restr.diffax_i2: see original CWM method", "topics": [ "restr.diffax_i2" ] }, { "page": "restr2_eigenv", "title": "restr2_eigenv: see original CWM method", "topics": [ "restr2_eigenv" ] }, { "page": "rmr", "title": "The main function of Robust Mixture Regression using five methods.", "topics": [ "rmr" ] }, { "page": "RobMixReg-class", "title": "Class RobMixReg.", "topics": [ "RobMixReg-class" ] }, { "page": "simu_data_sparse", "title": "Simulate high dimension data for RBSL algorithm validation.", "topics": [ "simu_data_sparse" ] }, { "page": "simu_func", "title": "The simulation function for low/high dimensional space.", "topics": [ "simu_func" ] }, { "page": "simu_low", "title": "The simulation function for low dimensional space.", "topics": [ "simu_low" ] }, { "page": "simuData", "title": "RobMixReg package built-in simulated example data.", "topics": [ "simuData" ] }, { "page": "ssclmat", "title": "ssclmat: see original CWM method", "topics": [ "ssclmat" ] }, { "page": "TLE-methods", "title": "TLE: robust mixture regression based on trimmed likelihood estimation.", "topics": [ "CTLE,formula,ANY,numeric,numeric,numeric-method", "TLE", "TLE,formula,ANY,numeric,numeric,numeric-method" ] }, { "page": "TreatSingularity", "title": "TreatSingularity: see original CWM method", "topics": [ "TreatSingularity" ] }, { "page": "trim.cwm", "title": "trim.cwm: main function", "topics": [ "trim.cwm" ] } ], "_readme": "https://github.com/changwn/robmixreg/raw/HEAD/README.md", "_rundeps": [ "bitops", "caTools", "cli", "DEoptimR", "dplyr", "farver", "fit.models", "flexmix", "generics", "glue", "gplots", "gtools", "KernSmooth", "labeling", "lars", "lattice", "lifecycle", "magrittr", "MASS", "modeltools", 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