{ "_id": "6a50bd3b89c83ec243a3027f", "Package": "csmpv", "Type": "Package", "Title": "Biomarker Confirmation, Selection, Modelling, Prediction, and\nValidation", "Version": "1.0.5", "Authors@R": "person(given = \"Aixiang\",\nfamily = \"Jiang\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"aijiang@bccrc.ca\",\ncomment = c(ORCID = \"0000-0002-6153-7595\"))", "Maintainer": "Aixiang Jiang ", "VignetteBuilder": "knitr", "Description": "There are diverse purposes such as biomarker confirmation,\nnovel biomarker discovery, constructing predictive models,\nmodel-based prediction, and validation. It handles binary,\ncontinuous, and time-to-event outcomes at the sample or patient\nlevel. - Biomarker confirmation utilizes established functions\nlike glm() from 'stats', coxph() from 'survival', surv_fit(),\nand ggsurvplot() from 'survminer'. - Biomarker discovery and\nvariable selection are facilitated by three LASSO-related\nfunctions LASSO2(), LASSO_plus(), and LASSO2plus(), leveraging\nthe 'glmnet' R package with additional steps. - Eight versatile\nmodeling functions are offered, each designed for predictive\nmodels across various outcomes and data types. 1) LASSO2(),\nLASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable\nselection using LASSO methods and construct predictive models\nbased on selected variables. 2) XGBtraining() employs 'XGBoost'\nfor model building and is the only function not involving\nvariable selection. 3) Functions like LASSO2_XGBtraining(),\nLASSOplus_XGBtraining(), and LASSO2plus_XGBtraining() combine\nLASSO-related variable selection with 'XGBoost' for model\nconstruction. - All models support prediction and validation,\nrequiring a testing dataset comparable to the training dataset.\nAdditionally, the package introduces XGpred() for risk\nprediction based on survival data, with the XGpred_predict()\nfunction available for predicting risk groups in new datasets.\nThe methodology is based on our new algorithms and various\nreferences: - Hastie et al. (1992, ISBN 0 534 16765-9), -\nTherneau et al. (2000, ISBN 0-387-98784-3), - Kassambara et al.\n(2021) , -\nFriedman et al. (2010) , - Simon et\nal. (2011) , - Harrell (2023)\n, - Harrell (2023)\n, - Chen and Guestrin\n(2016) , - Aoki et al. (2023)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.2", "NeedsCompilation": "no", "Config/pak/sysreqs": "cmake make libicu-dev libjpeg-dev libpng-dev\nlibuv1-dev libxml2-dev libssl-dev", "Repository": "https://ajiangsfu.r-universe.dev", "Date/Publication": "2025-12-12 02:45:40 UTC", "RemoteUrl": "https://github.com/ajiangsfu/csmpv", "RemoteRef": "HEAD", "RemoteSha": "b2412c98713c2de96bcfbb3e525b4eefb03d6c35", "Packaged": { "Date": "2026-07-10 09:32:14 UTC", "User": "root" }, "Author": "Aixiang Jiang [aut, cre, cph] (ORCID:\n)", "_user": "ajiangsfu", "_type": "src", "_file": "csmpv_1.0.5.tar.gz", "_fileid": "https://r2.ropensci.org/621c0b1a1a1421991211feb2fb8d297fa0b5e22eb897ec07b7dffe540a3035f8", "_filesize": 5419472, "_sha256": "621c0b1a1a1421991211feb2fb8d297fa0b5e22eb897ec07b7dffe540a3035f8", "_expires": "2026-10-18T09:36:57.000Z", "_created": "2026-07-10T09:32:14.000Z", "_published": "2026-07-10T09:36:59.350Z", "_jobs": [ { 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"https://github.com/ajiangsfu/csmpv/raw/HEAD/README.md", "_rundeps": [ "abind", "backports", "base64enc", "boot", "broom", "bslib", "cachem", "car", "carData", "checkmate", "cli", "cluster", "codetools", "colorspace", "commonmark", "corrplot", "cowplot", "cpp11", "curl", "data.table", "Deriv", "digest", "doBy", "dplyr", "evaluate", "exactRankTests", "farver", "fastmap", "fontawesome", "foreach", "forecast", "foreign", "forestmodel", "Formula", "fracdiff", "fs", "generics", "ggplot2", "ggpubr", "ggrepel", "ggsci", "ggsignif", "ggtext", "glmnet", "glue", "gridExtra", "gridtext", "gtable", "highr", "Hmisc", "htmlTable", "htmltools", "htmlwidgets", "isoband", "iterators", "jpeg", "jquerylib", "jsonlite", "knitr", "labeling", "lattice", "lifecycle", "litedown", "lme4", "lmtest", "magrittr", "markdown", "MASS", "Matrix", "MatrixModels", "maxstat", "memoise", "mgcv", "mime", "minqa", "modelr", "multcomp", "mvtnorm", "nlme", "nloptr", "nnet", "numDeriv", "pbkrtest", "pillar", "pkgconfig", 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Example data", "2. Biomarker confirmation/validation", "2.1 Binary outcome", "2.2 Continous outcome", "2.3 Time-to-event outcome", "3. Biomarker discovery with variable selection", "3.1 Variable selection with LASSO2", "3.1.1 Binary outcome", "3.1.2 Continuous outcome", "3.1.3 Time-to-event outcome", "3.2 Variable selection with LASSO2plus", "Binary outcome", "Continuous outcome", "Time-to-event outcome", "3.3. Variable selection with LASSO_plus", "4. Predictive model development", "4.1 LASSO2", "4.2 LASSO2 + regular regression", "4.3 LASSO_plus", "4.4 LASSO2plus", "4.5 XGBoost", "4.6 LASSO2 + XGBoost", "4.7 LASSO_plus + XGBoost", "4.8 LASSO2plus + XGBoost", "5. Model prediction", "5.1 LASSO2 prediction", "Binary ouctome", "Continuous ouctome", "Time-to-event ouctome", "5.2 LASSO2 + regular regression prediction", "5.3 LASSO_plus prediction", "5.4 LASSO2plus prediction", "5.5 XGBoost prediction", "5.6 LASSO2 + XGBoost prediction", "5.7 LASSO_plus + XGBoost prediction", "1) Binary ouctome", "2) Continuous ouctome", "3) Time-to-event ouctome", "5.8 LASSO2plus + XGBoost prediction", "6. (External) Model Validation", "6.1 LASSO2 validation", "6.2 LASSO2 + regular regression validation", "6.3 LASSO_plus validation", "6.4 LASSO2plus validation", "6.5 XGBoost validation", "6.6 LASSO2 + XGBoost validation", "6.7 LASSO_plus + XGBoost validation", "6.8 LASSO2plus + XGBoost validation", "7. All-in-one!", "8. 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