similar to: new version of bayesm

Folks- a patch release of bayesm, v0.0-1, is now available on CRAN. This release corrects some errors in the help pages as well as one error in the function rhierLinearModel involving an incorrect default prior setting. peter ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing and Economics Rm

Folks- a patch release of bayesm, v0.0-1, is now available on CRAN. This release corrects some errors in the help pages as well as one error in the function rhierLinearModel involving an incorrect default prior setting. peter ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing and Economics Rm

Version 1.0-1 of bayesm is now available on CRAN. This is our first "production" version which include s much improved documentation as well as five data sets used in our book, Bayesian Statistics and Marketing. peter r ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing and Economics

Version 1.0-1 of bayesm is now available on CRAN. This is our first "production" version which include s much improved documentation as well as five data sets used in our book, Bayesian Statistics and Marketing. peter r ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing and Economics

Version 1.1-0 of bayesm is now available on CRAN. This version includes Bayesian inference for the NBD(Poisson) regression model and a hierarchical version of the same. It also includes an additional dataset with count data and various covariates. Comments and suggestions for improvement are most welcome. ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis

Version 1.1-0 of bayesm is now available on CRAN. This version includes Bayesian inference for the NBD(Poisson) regression model and a hierarchical version of the same. It also includes an additional dataset with count data and various covariates. Comments and suggestions for improvement are most welcome. ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis

We are pleased to announce the release of version 0.0 of bayesm on CRAN. bayesm covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var) Multinomial Logit Multinomial and Multivariate Probit Multivariate Mixtures of Normals Hierarchical Linear Models with a normal prior and covariates

We are pleased to announce the release of version 0.0 of bayesm on CRAN. bayesm covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var) Multinomial Logit Multinomial and Multivariate Probit Multivariate Mixtures of Normals Hierarchical Linear Models with a normal prior and covariates

bayesm version 2.2-0 is now available on CRAN. Major changes include: 1. general density estimation using a Dirichlet Process Prior and a normal base 2. linear instrumental variable models with unknown error distributions (the Bayesian analogue of IV methods). Achieved via DP priors. peter r ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of

Folks- We are pleased to announce the release of version 2.0-1 of bayesm. Highlights of the new version: 1. Bayesian treatment of SUR (seemingly unrelated regression) 2. Added clustering to mixture of normals models 3. Added routines to compute implied univ and bivariate densities from mixture of normals MCMC draws 4. improved input error checking in many routines please get rid of your

Folks- We are pleased to announce the release of version 2.0-1 of bayesm. Highlights of the new version: 1. Bayesian treatment of SUR (seemingly unrelated regression) 2. Added clustering to mixture of normals models 3. Added routines to compute implied univ and bivariate densities from mixture of normals MCMC draws 4. improved input error checking in many routines please get rid of your

Dear All, I am using rhierMnlRwMixture in the bayesm package for the analysis of data from a choice experiment. I am trying to follow the margarine example set out in the bayesm manual (p.28). However, after several attempts I keep getting an error message with regards to my Z matrix as below. > Error in Z %*% t(matrix(olddelta, ncol = nz)) : >requires numeric/complex matrix/vector

Version 2.0-9 of bayesm is now available on CRAN changes include- 1. addition of rhierLinearMixture -- linear hierarchical models with a mixture of normals prior 2. mixDenBi is now fully vectorized and run more than 10 times faster 3. minor documentation corrections have been made 4. rnmixGibbs allows the user to specify only one component peter rossi

Hello! I am using rhierMnlRwMixture from bayesm package. I would like to use it with a categorical covariate (Z). I have 2 clariciation questions: 1. If the covariate is categorical, do I have to represent it as dummy variable(s)? (e.g., 2 dummy variables for a 3-level categorical variable)? 2. Do those dummy variables have to be centered? Help file for rhierMnlRwMixture says: "Z should not

Release 2.1-1 is now available on CRAN. This release includes-- bayesm classes (some compatible with the mcmc class of coda) for output. plot and summary methods for these classes. additional datasets including store-level panel data. peter r ................................ Peter E. Rossi Joseph T. and Bernice S. Lewis Professor of Marketing and Statistics Editor, Quantitative Marketing

Hi all, For my research I have to use a Multinomial Probit model. I saw that there are two packages, that include a method to estimate my parameters. The first one is the MNP-package of Imai and van Dyk. The second one is part of the bayesm-package of Rossi. The results for both packages are not the same using the same data. Does anybody know what the difference is between these two approaches?

Is anyone familiar with a way to account for sampling weights (e.g., in order to cope with selection bias) for individual respondents using the bayesm package (e.g., rhierMnlRwMixture)? In the regular MNL this can easily be done in STATA using the mlogit function with pweights option. However, I am unfamiliar with a way to do it in HB estimation. Any help or hints are appreciated. Best, Klaus

I am a pretty new R user, I am running the latest linux version on xandros, updated with some extra debian packages, and I also run the latest windows version, but prefer linux. I am having trouble downloading "bayesm", it won't do it all from any of the sites on the web, I resorted to this one, http://packages.debian.org/unstable/math/r-cran-bayesm. and got slightly further, but I

Dear r-help- >From reading the CRAN web page, it appears that you should not submit precompiled binary versions of your package, but rather that these are built for you by someone working with CRAN. I submitted my package using R CMD build but without the binary flag on. I'd like to have a binary version available for Windows users. Should I submit a precompiled binary version as well?

Folks- I have developed a package which I am planning on posting to CRAN. I include examples for each of the 40 or so functions in the package. However, since the examples are non-trivial and the package is using MCMC simulation methods, it takes about 20 minutes to run the complete set of examples on a 2.8 Ghz windows PC. The R process reaches a high water mark of about 100 MB of memory as