{ "_id": "6a12ace9acfb0bcc41d183f1", "Package": "tcv", "Type": "Package", "Title": "Determining the Number of Factors in Poisson Factor Models via\nThinning Cross-Validation", "Version": "0.1.0", "Date": "2025-09-01", "Authors@R": "c(person(\"Zhijing\", \"Wang\", email = \"wangzhijing@sjtu.edu.cn\", role = c(\"aut\", \"cre\")), person(\"Heng\", \"Peng\", email = \"hpeng@hkbu.edu.hk\", role = \"aut\"), person(\"Peirong\", \"Xu\", email = \"prxu@sjtu.edu.cn\", role = \"aut\"))", "Description": "Implements methods for selecting the number of factors in\nPoisson factor models, with a primary focus on Thinning\nCross-Validation (TCV). The TCV method is based on the 'data\nthinning' technique, which probabilistically partitions each\ncount observation into training and test sets while preserving\nthe underlying factor structure. The Poisson factor model is\nthen fit on the training set, and model selection is performed\nby comparing predictive performance on the test set. This\ntoolkit is designed for researchers working with\nhigh-dimensional count data in fields such as genomics, text\nmining, and social sciences. The data thinning methodology is\ndetailed in Dharamshi et al. (2025)\n and Wang et al. (2025)\n.", "License": "GPL (>= 3)", "Encoding": "UTF-8", "SystemRequirements": "C++17", "RoxygenNote": "7.3.2", "URL": "https://github.com/Wangzhijingwzj/tcv", "BugReports": "https://github.com/Wangzhijingwzj/tcv/issues", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-24 07:44:05 UTC", "User": "root" }, "Author": "Zhijing Wang [aut, cre], Heng Peng [aut], Peirong Xu [aut]", "Maintainer": "Zhijing Wang ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-09-23 07:40:02 UTC", "RemoteUrl": "https://github.com/cran/tcv", "RemoteRef": "HEAD", "RemoteSha": "cc9e2cc3622dbc1a5b784220e936f8f0b69eb66b", "MD5sum": "c08261d429d1dc2b4690998a1f89b67e", "_user": "cran", "_type": "src", "_file": "tcv_0.1.0.tar.gz", "_fileid": "1f48d94af28b9400424fd3144e67982b8c62b869d91c8fe9aee1310ca1db8c5d", "_filesize": 97554, "_sha256": "1f48d94af28b9400424fd3144e67982b8c62b869d91c8fe9aee1310ca1db8c5d", "_created": "2026-05-24T07:44:05.000Z", "_published": 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