# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "CausalMixGPD" in publications use:' type: software license: GPL-3.0-only title: 'CausalMixGPD: Bayesian Nonparametric Conditional Density Modeling in Causal Inference and Clustering with a Heavy-Tail Extension' version: 0.8.0 doi: 10.32614/CRAN.package.CausalMixGPD identifiers: - type: doi value: 10.32614/CRAN.package.CausalMixGPD - type: url value: https://arnabaich96.github.io/CausalMixGPD/pkgdown/ abstract: The presence of a heavy tail is a feature of many scenarios when risk management involves extremely rare events. While parametric distributions may give adequate representation of the mode of data, they are likely to misrepresent heavy tails, and completely nonparametric approaches lack a rigorous mechanism for tail extrapolation; see Pickands (1975) . The statistical methodology follows Aich and Bhattacharya (2026) for Bayesian analysis of heavy-tailed outcomes by combining Dirichlet process mixture models for the body of the distribution with optional generalized Pareto tails. The package implements for unconditional and covariate-modulated mixtures, implements MCMC estimation using 'nimble', and extends to mixtures of different arms' outcomes with application to causal inference in the Rubin (1974) framework. Posterior summaries include density functions, quantiles, expected values, survival functions, and causal effects, with an emphasis on tail quantiles and functional measures sensitive to the tail. authors: - family-names: Aich given-names: Arnab email: aaich@fsu.edu orcid: https://orcid.org/0009-0005-7801-6701 preferred-citation: type: manual title: 'CausalMixGPD: Bayesian Nonparametric Conditional Density Modeling in Causal Inference and Clustering with a Heavy-Tail Extension' authors: - family-names: Aich given-names: Arnab email: aaich@fsu.edu orcid: https://orcid.org/0009-0005-7801-6701 year: '2026' notes: R package version 0.8.0 doi: 10.32614/CRAN.package.CausalMixGPD repository: https://arnabaich96.r-universe.dev commit: e23ce3c0731901b181935e77169aa20f8d1ae058 url: https://arnabaich96.github.io/CausalMixGPD/ date-released: '2026-05-07' contact: - family-names: Aich given-names: Arnab email: aaich@fsu.edu orcid: https://orcid.org/0009-0005-7801-6701 references: - type: article title: A Semiparametric Bayesian Approach for Estimating Extreme Conditional Quantile Treatment Effect for Heavy-tailed Data authors: - family-names: Aich given-names: Arnab - family-names: Bhattacharya given-names: Indrabati year: '2026' journal: Zenodo doi: 10.5281/zenodo.19672760