{ "_id": "6a104924acfb0bcc41c9f631", "Package": "RFmstate", "Type": "Package", "Title": "Random Forest-Based Multistate Survival Analysis", "Version": "0.1.2", "Authors@R": "person(\"Yiqing\", \"Chen\", role = c(\"aut\", \"cre\"),\nemail = \"y.chen@tamu.edu\")", "Author": "Yiqing Chen [aut, cre]", "Maintainer": "Yiqing Chen ", "Description": "Fits cause-specific random survival forests for flexible\nmultistate survival analysis with covariate-adjusted transition\nprobabilities computed via product-integral. State transitions\nare modeled by random forests. Subject-specific transition\nprobability matrices are assembled from predicted cumulative\nhazards using the product-integral formula. Also provides a\nstandalone Aalen-Johansen nonparametric estimator as a\ncovariate-free baseline. Supports arbitrary state spaces with\nany number of states (three or more) and any set of allowed\ntransitions, applicable to clinical trials, disease\nprogression, reliability engineering, and other domains where\nsubjects move among discrete states over time. Provides\nper-transition feature importance, bias-variance diagnostics,\nand comprehensive visualizations. Handles right censoring and\ncompeting transitions. Methods are described in Ishwaran et al.\n(2008) for random survival forests,\nPutter et al. 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