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The default\nsetting flexibly models the transition probabilities using\nDirichlet mixtures and the duration times using gamma mixtures.\nIt also provides the flexibility of modeling the categorical\nsequences using Bayesian Markov mixed models alone, either\nignoring the duration times altogether or dividing duration\ntime into multiples of an additional category in the sequence\nby a user-specific unit. The package allows extensive inference\nof the state transition probabilities and the duration times as\nwell as relevant plots and graphs. It also includes a synthetic\ndata set to demonstrate the desired format of input data set\nand the utility of various functions. Methods for Bayesian\nMarkov renewal mixed models are as described in: Abhra Sarkar\net al., (2018) and Yutong\nWu et al., (2022) .", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.2.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-10 08:20:18 UTC", "User": "root" }, "Repository": "https://yutongwu96.r-universe.dev", "Date/Publication": "2024-04-23 02:34:52 UTC", "RemoteUrl": "https://github.com/cran/BMRMM", "RemoteRef": "HEAD", "RemoteSha": "5a90712df9fd67fbe6b04b32f5a02f7ac12dc88d", "MD5sum": "bb221daaac28f041d52b23978514dc5d", "_user": "yutongwu96", "_type": "src", "_file": "BMRMM_1.0.1.tar.gz", "_fileid": "7bd36ba92f462de1d93e78e745794cef9fd679e1f8f7b84e4b4ec410a2631d7e", "_filesize": 247801, "_sha256": "7bd36ba92f462de1d93e78e745794cef9fd679e1f8f7b84e4b4ec410a2631d7e", "_created": "2026-05-10T08:20:18.000Z", "_published": "2026-05-22T12:21:07.445Z", "_distro": "noble", "_jobs": [ { "job": 77378242769, "time": 134, "config": 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