Package: csmpv
Type: Package
Title: Biomarker Confirmation, Selection, Modelling, Prediction, and
        Validation
Version: 1.0.5
Authors@R: person(given = "Aixiang",
                    family = "Jiang",
                    role = c("aut", "cre", "cph"),
                    email = "aijiang@bccrc.ca",
                    comment = c(ORCID = "0000-0002-6153-7595"))
Maintainer: Aixiang Jiang <aijiang@bccrc.ca>
Depends: R (>= 4.4.0), stats
Imports: survival, glmnet, Hmisc, rms, forestmodel, ggplot2, ggpubr,
        survminer, xgboost, scales, Matrix
Suggests: knitr, rmarkdown, devtools
VignetteBuilder: knitr
Description: There are diverse purposes such as biomarker confirmation,
        novel biomarker discovery, constructing predictive models,
        model-based prediction, and validation. It handles binary,
        continuous, and time-to-event outcomes at the sample or patient
        level. - Biomarker confirmation utilizes established functions
        like glm() from 'stats', coxph() from 'survival', surv_fit(),
        and ggsurvplot() from 'survminer'. - Biomarker discovery and
        variable selection are facilitated by three LASSO-related
        functions LASSO2(), LASSO_plus(), and LASSO2plus(), leveraging
        the 'glmnet' R package with additional steps. - Eight versatile
        modeling functions are offered, each designed for predictive
        models across various outcomes and data types. 1) LASSO2(),
        LASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable
        selection using LASSO methods and construct predictive models
        based on selected variables. 2) XGBtraining() employs 'XGBoost'
        for model building and is the only function not involving
        variable selection. 3) Functions like LASSO2_XGBtraining(),
        LASSOplus_XGBtraining(), and LASSO2plus_XGBtraining() combine
        LASSO-related variable selection with 'XGBoost' for model
        construction. - All models support prediction and validation,
        requiring a testing dataset comparable to the training dataset.
        Additionally, the package introduces XGpred() for risk
        prediction based on survival data, with the XGpred_predict()
        function available for predicting risk groups in new datasets.
        The methodology is based on our new algorithms and various
        references: - Hastie et al. (1992, ISBN 0 534 16765-9), -
        Therneau et al. (2000, ISBN 0-387-98784-3), - Kassambara et al.
        (2021) <https://CRAN.R-project.org/package=survminer>, -
        Friedman et al. (2010) <doi:10.18637/jss.v033.i01>, - Simon et
        al. (2011) <doi:10.18637/jss.v039.i05>, - Harrell (2023)
        <https://CRAN.R-project.org/package=rms>, - Harrell (2023)
        <https://CRAN.R-project.org/package=Hmisc>, - Chen and Guestrin
        (2016) <doi:10.48550/arXiv.1603.02754>, - Aoki et al. (2023)
        <doi:10.1200/JCO.23.01115>.
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
        libuv1-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: 2026-07-10 09:32:14 UTC; root
Author: Aixiang Jiang [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0002-6153-7595>)