{ "_id": "6a1022fdacfb0bcc41c8d412", "Package": "MFSIS", "Type": "Package", "Title": "Model-Free Sure Independent Screening Procedures", "Version": "0.3.1", "Date": "2026-05-12", "Authors@R": "c(\nperson(\"Xuewei\", \"Cheng\", role = c(\"aut\",\"cre\"), email = \"xwcheng@hunnu.edu.cn\"),\nperson(\"Hong\", \"Wang\", role = c(\"aut\"), email = \"wh@csu.edu.cn\"),\nperson(\"Liping\", \"Zhu\", role = c(\"aut\"), email = \"zhu.liping@ruc.edu.cn\"),\nperson(\"Wei\", \"Zhong\", role = c(\"aut\"), email = \"wzhong@xmu.edu.cn\"),\nperson(\"Hanpu\", \"Zhou\", role = c(\"aut\"), email = \"zhouhanpu@csu.edu.cn\")\n)", "Author": "Xuewei Cheng [aut, cre], Hong Wang [aut], Liping Zhu [aut], Wei\nZhong [aut], Hanpu Zhou [aut]", "Maintainer": "Xuewei Cheng ", "Description": "An implementation of popular screening methods that are\ncommonly employed in ultra-high and high dimensional data.\nThrough this publicly available package, we provide a unified\nframework to carry out model-free screening procedures\nincluding SIS (Fan and Lv (2008)\n), SIRS(Zhu et al.\n(2011)), DC-SIS (Li et al.\n(2012) ), MDC-SIS(Shao and\nZhang (2014) ), Bcor-SIS (Pan\net al. (2019) ), PC-Screen\n(Liu et al. (2020) ), WLS\n(Zhong et al.(2021) ),\nKfilter (Mai and Zou (2015) ), MVSIS\n(Cui et al. (2015) ), PSIS\n(Pan et al. (2016) ), CAS\n(Xie et al. (2020) ), CI-SIS\n(Cheng and Wang. (2023) ), CSIS\n(Cheng et al. (2024) ) and\nLog-rank SIS.", "License": "GPL (>= 2)", "SystemRequirements": "Python (>= 3.8.0)", "Encoding": "UTF-8", "Language": "en-US", "NeedsCompilation": "no", "RoxygenNote": "7.2.3", "Packaged": { "Date": "2026-05-13 23:08:58 UTC", "User": "root" }, "Config/pak/sysreqs": "python3", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-05-13 21:00:07 UTC", "RemoteUrl": "https://github.com/cran/MFSIS", "RemoteRef": "HEAD", "RemoteSha": "92d3329fcef470453f7279c76615d76d462f7490", "MD5sum": "3df7c1654659382ad0081407f8f2c8be", "_user": "cran", "_type": "src", "_file": "MFSIS_0.3.1.tar.gz", "_fileid": "9c89fd227a15e65c47863f40c86fd50b90a20b2a20d8934e37304bce89096fee", "_filesize": 302951, "_sha256": "9c89fd227a15e65c47863f40c86fd50b90a20b2a20d8934e37304bce89096fee", "_created": "2026-05-13T23:08:58.000Z", "_published": "2026-05-22T09:33:49.954Z", "_distro": "noble", "_jobs": [ { "job": 77353557896, "time": 175, "config": "linux-devel-x86_64", "r": "4.7.0", "check": 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"reticulate", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "crayon", "role": "Imports" }, { "package": "cli", "role": "Imports" }, { "package": "dr", "role": "Imports" }, { "package": "foreach", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "doParallel", "role": "Imports" }, { "package": "fs", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "utils", "role": "Suggests" }, { "package": "pkgdown", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2026-20", "n": 1 } ], "_tags": [ { "name": "0.3.1", "date": "2026-05-13" } ], "_stars": 2, "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 219, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/MFSIS" }, "_searchresults": 4, "_rbuild": "4.6.0", "_assets": [ 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"GendataCox", "title": "Generate simulation data (Survival data based on the Cox model)", "topics": [ "GendataCox" ] }, { "page": "GendataGP", "title": "Generate simulation data (Complete data with group predictors)", "topics": [ "GendataGP" ] }, { "page": "GendataIC", "title": "Generate Simulation Data for Interval-Censored Screening", "topics": [ "GendataIC" ] }, { "page": "GendataIM", "title": "Generate simulation data (Complete data for intersection variables)", "topics": [ "GendataIM" ] }, { "page": "GendataLDA", "title": "Generate simulation data (Categorial based on linear discriminant analysis model)", "topics": [ "GendataLDA" ] }, { "page": "GendataLGM", "title": "Generate simulation data (Binary category data based on logistic model)", "topics": [ "GendataLGM" ] }, { "page": "GendataLM", "title": "Generate simulation data (Complete data based on linear models)", "topics": [ "GendataLM" ] }, { "page": "GendataMRM", "title": "Generate simulation data (Multivariate response models)", "topics": [ "GendataMRM" ] }, { "page": "GendataPM", "title": "Generate simulation data (Discrete response data based on poisson model)", "topics": [ "GendataPM" ] }, { "page": "GendataTM", "title": "Generate simulation data (Complete data based on transformation model)", "topics": [ "GendataTM" ] }, { "page": "get_arccos", "title": "Arccos function", "topics": [ "get_arccos" ] }, { "page": "Kfilter", "title": "The Kolmogorov filter for variable screening", "topics": [ "Kfilter" ] }, { "page": "Kfilter_fused", "title": "The fused kolmogorov filter: a nonparametric model-free screening method", "topics": [ "Kfilter_fused" ] }, { "page": "Kfilter_single", "title": "The Kolmogorov filter for variable screening in high-dimensional binary classification", "topics": [ "Kfilter_single" ] }, { "page": "LogrankSIS", "title": "Generalized Log-rank Sure Independence Screening for Ultrahigh-Dimensional Interval-Censored Response", "topics": [ "LogrankSIS" ] }, { "page": "MDCSIS", 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