bayesm (3.1-0)

0 users

Bayesian Inference for Marketing/Micro-Econometrics.

http://www.perossi.org/home/bsm-1
http://cran.r-project.org/web/packages/bayesm

Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014).

Maintainer: Peter Rossi
Author(s): Peter Rossi <perossichi@gmail.com>

License: GPL (>= 2)

Uses: Rcpp, knitr, rmarkdown
Reverse depends: BaM, bscr, compositions, mpbart, ocomposition, psbcGroup, siar, VisCov
Reverse suggests: ChoiceModelR

Released about 3 hours ago.


8 previous versions

Ratings

Overall:

  (0 votes)

Documentation:

  (0 votes)

Log in to vote.

Reviews

No one has written a review of bayesm yet. Want to be the first? Write one now.


Related packages: Hmisc, MCMCpack, MNP, VGAM, e1071, mclust, AdMit, cudaBayesreg, MASS, BMA, GLDEX, GSM, HI, LearnBayes, MSBVAR, Matrix, PTAk, Runuran, SparseM, amap(20 best matches, based on common tags.)


Search for bayesm on google, google scholar, r-help, r-devel.

Visit bayesm on R Graphical Manual.