{ "_id": "6a23b713530b9bc726bd7bec", "Package": "CausalMixGPD", "Type": "Package", "Title": "Bayesian Nonparametric Conditional Density Modeling in Causal\nInference and Clustering with a Heavy-Tail Extension", "Version": "0.8.0", "Maintainer": "Arnab Aich ", "Authors@R": "c(\nperson(given = \"Arnab\", family = \"Aich\", role = c(\"aut\", \"cre\"),\nemail = \"aaich@fsu.edu\",\ncomment = c(ORCID = \"0009-0005-7801-6701\"))\n)", "Description": "The presence of a heavy tail is a feature of many\nscenarios when risk management involves extremely rare events.\nWhile parametric distributions may give adequate representation\nof the mode of data, they are likely to misrepresent heavy\ntails, and completely nonparametric approaches lack a rigorous\nmechanism for tail extrapolation; see Pickands (1975)\n. The statistical methodology\nfollows Aich and Bhattacharya (2026)\n for Bayesian analysis of\nheavy-tailed outcomes by combining Dirichlet process mixture\nmodels for the body of the distribution with optional\ngeneralized Pareto tails. The package implements for\nunconditional and covariate-modulated mixtures, implements MCMC\nestimation using 'nimble', and extends to mixtures of different\narms' outcomes with application to causal inference in the\nRubin (1974) framework. Posterior\nsummaries include density functions, quantiles, expected\nvalues, survival functions, and causal effects, with an\nemphasis on tail quantiles and functional measures sensitive to\nthe tail.", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.3", "Roxygen": "list(markdown = TRUE, roclets = c(\"rd\",\n\"namespace\",\"collate\"))", "Config/testthat/edition": "3", "URL": "https://arnabaich96.github.io/CausalMixGPD/pkgdown/", "BugReports": "https://arnabaich96.github.io/CausalMixGPD/", "Config/pak/sysreqs": "libglpk-dev make libxml2-dev", "Repository": "https://arnabaich96.r-universe.dev", "Date/Publication": "2026-05-07 17:32:03 UTC", "RemoteUrl": "https://github.com/arnabaich96/causalmixgpd", "RemoteRef": "HEAD", "RemoteSha": "e23ce3c0731901b181935e77169aa20f8d1ae058", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-06 05:53:13 UTC", "User": "root" }, "Author": "Arnab Aich [aut, cre] (ORCID: )", "MD5sum": 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consistency; update author information and package description\n\n- Changed the \\code command to use \\detokenize for better handling of code snippets.\n- Revised metadata section to enhance clarity and consistency in author details.\n- Improved the abstract for conciseness and clarity, emphasizing key features of the CausalMixGPD package.\n- Updated the introduction to better articulate the importance of distribution-based causal inference and the role of the package.\n- Enhanced the background section with clearer explanations of related packages and methodologies.\n- Added a table summarizing core exported functions and object classes for better user guidance.\n- Included a new section on software availability with links to the package repository.\n- Made various minor edits for grammatical accuracy and improved readability throughout the document.\n", "time": 1778175123 }, "_maintainer": { "name": "Arnab Aich", "email": "aaich@fsu.edu" }, "_registered": true, "_dependencies": [ { 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"rgammamixgpd" ] }, { "page": "gamma_mix", "title": "Gamma mixture distribution", "concept": [ "gamma kernel families" ], "topics": [ "dGammaMix", "gamma_mix", "pGammaMix", "qGammaMix", "rGammaMix" ] }, { "page": "gamma_mixgpd", "title": "Gamma mixture with a GPD tail", "concept": [ "gamma kernel families" ], "topics": [ "dGammaMixGpd", "gamma_mixgpd", "pGammaMixGpd", "qGammaMixGpd", "rGammaMixGpd" ] }, { "page": "get_kernel_registry", "title": "Get kernel registry", "topics": [ "get_kernel_registry" ] }, { "page": "get_tail_registry", "title": "Get tail registry", "topics": [ "get_tail_registry" ] }, { "page": "gpd", "title": "Generalized Pareto distribution", "concept": [ "base tail distributions" ], "topics": [ "dGpd", "gpd", "pGpd", "qGpd", "rGpd" ] }, { "page": "init_kernel_registry", "title": "Initialize kernel registries", "topics": [ "init_kernel_registry" ] }, { "page": "InvGauss", "title": "Inverse Gaussian (Wald) distribution", "concept": [ "base bulk distributions" ], "topics": [ "dInvGauss", "InvGauss", "pInvGauss", "qInvGauss", "rInvGauss" ] }, { "page": "InvGauss_gpd", "title": "Inverse Gaussian with a GPD tail", "concept": [ "inverse-gaussian kernel families" ], "topics": [ "dInvGaussGpd", "InvGauss_gpd", "pInvGaussGpd", "qInvGaussGpd", "rInvGaussGpd" ] }, { "page": "invgauss_lowercase", "title": "Lowercase vectorized inverse Gaussian distribution functions", "concept": [ "vectorized kernel helpers" ], "topics": [ "dinvgaussgpd", "dinvgaussmix", "dinvgaussmixgpd", "invgauss_lowercase", "pinvgaussgpd", "pinvgaussmix", "pinvgaussmixgpd", "qinvgaussgpd", "qinvgaussmix", "qinvgaussmixgpd", "rinvgaussgpd", "rinvgaussmix", "rinvgaussmixgpd" ] }, { "page": "InvGauss_mix", "title": "Inverse Gaussian mixture distribution", "concept": [ "inverse-gaussian kernel families" ], "topics": [ "dInvGaussMix", "InvGauss_mix", "pInvGaussMix", "qInvGaussMix", "rInvGaussMix" ] }, { "page": "InvGauss_mixgpd", "title": "Inverse Gaussian mixture with a GPD tail", "concept": [ "inverse-gaussian kernel families" ], "topics": [ "dInvGaussMixGpd", "InvGauss_mixgpd", "pInvGaussMixGpd", "qInvGaussMixGpd", "rInvGaussMixGpd" ] }, { "page": "kernel_support_table", "title": "Kernel support matrix", "topics": [ "kernel_support_table" ] }, { "page": "laplace_gpd", "title": "Laplace with a GPD tail", "concept": [ "laplace kernel families" ], "topics": [ "dLaplaceGpd", "laplace_gpd", "pLaplaceGpd", "qLaplaceGpd", "rLaplaceGpd" ] }, { "page": "laplace_lowercase", "title": "Lowercase vectorized Laplace distribution functions", "concept": [ "vectorized kernel helpers" ], "topics": [ "dlaplacegpd", "dlaplacemix", "dlaplacemixgpd", "laplace_lowercase", "plaplacegpd", "plaplacemix", "plaplacemixgpd", "qlaplacegpd", "qlaplacemix", "qlaplacemixgpd", "rlaplacegpd", "rlaplacemix", "rlaplacemixgpd" ] }, { "page": "laplace_mix", "title": "Laplace (double exponential) mixture distribution", "concept": [ "laplace kernel families" ], "topics": [ "dLaplaceMix", 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families" ], "topics": [ "dLognormalMix", "lognormal_mix", "pLognormalMix", "qLognormalMix", "rLognormalMix" ] }, { "page": "lognormal_mixgpd", "title": "Lognormal mixture with a GPD tail", "concept": [ "lognormal kernel families" ], "topics": [ "dLognormalMixGpd", "lognormal_mixgpd", "pLognormalMixGpd", "qLognormalMixGpd", "rLognormalMixGpd" ] }, { "page": "mcmc", "title": "Run posterior sampling from a prepared bundle", "topics": [ "mcmc" ] }, { "page": "nc_pos_tail200_k4", "title": "Positive-Support Tail Data Set With Four Components", "topics": [ "nc_pos_tail200_k4" ] }, { "page": "nc_pos200_k3", "title": "Positive-Support Bulk-Only Data Set With Three Components", "topics": [ "nc_pos200_k3" ] }, { "page": "nc_posX100_p3_k2", "title": "Positive-Support Covariate Data Set With Three Predictors", "topics": [ "nc_posX100_p3_k2" ] }, { "page": "nc_posX100_p4_k3", "title": "Positive-Support Covariate Data Set With Four Predictors", "topics": [ "nc_posX100_p4_k3" ] }, { "page": 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