{ "_id": "6a1ed945b401979e7340f2e0", "Package": "bayesm", "Version": "3.1-7", "Type": "Package", "Title": "Bayesian Inference for Marketing/Micro-Econometrics", "Date": "2025-11-09", "Authors@R": "c(person(given = \"Peter\", family = \"Rossi\", role = c(\"aut\", \"cre\"), \nemail = \"perossichi@gmail.com\"),\nperson(given = \"Robert\", family = \"McCulloch\", role = \"ctb\"),\nperson(given = \"Wayne\", family = \"Taylor\", role = \"ctb\"),\nperson(given = \"Dan\", family = \"Yavorsky\", role = \"ctb\")\n)", "Maintainer": "Peter Rossi ", "License": "GPL (>= 2)", "VignetteBuilder": "knitr", "Description": "Covers many important models used in marketing and\nmicro-econometrics applications. The package includes: Bayes\nRegression (univariate or multivariate dep var), Bayes\nSeemingly Unrelated Regression (SUR), Binary and Ordinal\nProbit, Multinomial Logit (MNL) and Multinomial Probit (MNP),\nMultivariate Probit, Negative Binomial (Poisson) Regression,\nMultivariate Mixtures of Normals (including clustering),\nDirichlet Process Prior Density Estimation with normal base,\nHierarchical Linear Models with normal prior and covariates,\nHierarchical Linear Models with a mixture of normals prior and\ncovariates, Hierarchical Multinomial Logits with a mixture of\nnormals prior and covariates, Hierarchical Multinomial Logits\nwith a Dirichlet Process prior and covariates, Hierarchical\nNegative Binomial Regression Models, Bayesian analysis of\nchoice-based conjoint data, Bayesian treatment of linear\ninstrumental variables models, Analysis of Multivariate Ordinal\nsurvey data with scale usage heterogeneity (as in Rossi et al,\nJASA (01)), Bayesian Analysis of Aggregate Random Coefficient\nLogit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For\nfurther reference, consult our book, Bayesian Statistics and\nMarketing by Rossi, Allenby and McCulloch (Wiley second edition\n2024) and Bayesian Non- and Semi-Parametric Methods and\nApplications (Princeton U Press 2014).", "RoxygenNote": "6.0.1", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-20 09:34:14 UTC", "User": "root" }, "Author": "Peter Rossi [aut, cre], Robert McCulloch [ctb], Wayne Taylor\n[ctb], Dan Yavorsky [ctb]", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2025-11-11 06:10:47 UTC", "RemoteUrl": "https://github.com/cran/bayesm", "RemoteRef": "HEAD", "RemoteSha": "453ae30857cf09a0c537f4deab64ed193493c8c9", "MD5sum": "858318b2b8c173209960de07110e1732", "_user": "cran", "_type": "src", "_file": "bayesm_3.1-7.tar.gz", "_fileid": "c643697f59a6ff2405224d5a564950a96590b36b260c48ea4acb8fe57a8828e2", "_filesize": 1960012, "_sha256": 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Mixture", "concept": [ "normal mixture", "marginal distribution", "density" ], "topics": [ "mixDen" ] }, { "page": "mixDenBi", "title": "Compute Bivariate Marginal Density for a Normal Mixture", "concept": [ "normal mixture", "marginal distribution", "density" ], "topics": [ "mixDenBi" ] }, { "page": "mnlHess", "title": "Computes -Expected Hessian for Multinomial Logit", "concept": [ "multinomial logit", "hessian" ], "topics": [ "mnlHess" ] }, { "page": "mnpProb", "title": "Compute MNP Probabilities", "concept": [ "MNP", "Multinomial Probit Model", "GHK", "market share simulator" ], "topics": [ "mnpProb" ] }, { "page": "momMix", "title": "Compute Posterior Expectation of Normal Mixture Model Moments", "concept": [ "mcmc", "normal mixture", "posterior moments" ], "topics": [ "momMix" ] }, { "page": "nmat", "title": "Convert Covariance Matrix to a Correlation Matrix", "topics": [ "nmat" ] }, { "page": "numEff", "title": "Compute Numerical Standard Error and Relative Numerical 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{ "page": "rmnlIndepMetrop", "title": "MCMC Algorithm for Multinomial Logit Model", "concept": [ "MCMC", "multinomial logit", "Metropolis algorithm", "bayes" ], "topics": [ "rmnlIndepMetrop" ] }, { "page": "rmnpGibbs", "title": "Gibbs Sampler for Multinomial Probit", "concept": [ "bayes", "multinomial probit", "MCMC", "Gibbs Sampling" ], "topics": [ "rmnpGibbs" ] }, { "page": "rmultireg", "title": "Draw from the Posterior of a Multivariate Regression", "concept": [ "bayes", "multivariate regression", "simulation" ], "topics": [ "rmultireg" ] }, { "page": "rmvpGibbs", "title": "Gibbs Sampler for Multivariate Probit", "concept": [ "bayes", "multivariate probit", "MCMC", "Gibbs Sampling" ], "topics": [ "rmvpGibbs" ] }, { "page": "rmvst", "title": "Draw from Multivariate Student-t", "concept": [ "multivariate t distribution", "student-t", "simulation" ], "topics": [ "rmvst" ] }, { "page": "rnegbinRw", "title": "MCMC Algorithm for Negative Binomial Regression", "concept": [ "MCMC", "NBD 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