{"id":23069538,"url":"https://github.com/tmsalab/slcm","last_synced_at":"2025-04-30T05:09:09.094Z","repository":{"id":187536051,"uuid":"667684624","full_name":"tmsalab/slcm","owner":"tmsalab","description":"Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data      described by Chen, Y., Culpepper, S. A., and Liang, F. 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A., and Liang, F. (2020) \u003cdoi:10.1007/s11336-019-09693-2\u003e.\n\nThis package contains a new implementation of the proposed SLCM based on\nthe paper. You may find original papers implementation in the [`inst/`\nfolder](https://github.com/tmsalab/slcm/tree/main/inst) of the package.\n\n## Installation\n\nYou can install the released version of slcm from\n[CRAN](https://CRAN.R-project.org) with:\n\n``` r\ninstall.packages(\"slcm\")\n```\n\nOr, you can be on the cutting-edge development version on\n[GitHub](https://github.com/) using:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"tmsalab/slcm\")\n```\n\n## Usage\n\nTo use `slcm`, load the package using:\n\n``` r\nlibrary(\"slcm\")\n```\n\nFrom here, the SLCM model can be estimated using:\n\n``` r\nmodel_slcm = slcm::slcm(\n  y = \u003cdata\u003e,\n  k = \u003ck\u003e\n)\n```\n\n## Authors\n\nJames Joseph Balamuta and Steven Andrew Culpepper\n\n## Citing the `slcm` package\n\nTo ensure future development of the package, please cite `slcm` package\nif used during an analysis or simulation study. Citation information for\nthe package may be acquired by using in *R*:\n\n``` r\ncitation(\"slcm\")\n```\n\n## License\n\nGPL (\\\u003e= 2)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftmsalab%2Fslcm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftmsalab%2Fslcm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftmsalab%2Fslcm/lists"}