similar to: Release 2.1-1 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

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

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 1.1-0 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

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

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

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

I can successfully "check" a package with source under 2.5.1, including compiling source files and running examples with no errors or warnings. when I try with R2.6.2, I get make errors: making bayesmc.d from bayesmc.c make[3]:gcc-sjlj: Command not found etc. my gcc is version 3.4.2 I'm using Windows XP. Any thoughts? thanks! peter r ................................ Peter

I have developed a package, bayesm, which uses existing classes of objects. I would like to add a new class corresponding to objects from this package. I have been reading about classes and all sources tell me that I should use so-called "new" or S4 classes. However, a major purpose of defining a class for my package would be to add methods to the existing generic functions: print,

I have a set of n vectors, x_1, ..., x_n, of the same length. I would like to form the vector of dot products -- x_1'x_1, ..., x_n'x_n the fastest way I can think to do this is to put the vectors into a matrix and do diag(crossprod(X)) however, this seems to be very wasteful since this computes n(n+1)/2-n unnecessary dot products. Is there a better way using existing functions in R?

Writing R Ext says to treat R objects that are arguments to .Call as read only (i.e. don't modify). I have a long list of lists that and I want to avoid the overhead of a copy in my C code. I would just like to modify some of the elements of list by replacing them with elements of exactly the same size/type. below is an example of the essence of the problem. This seems to work. Is this

I have noticed dramatic differences in the run-time for the execution of one of my functions depending on whether or not R was restarted. Immediately after restart of R GUI, exec time = 2.8 min. If I then repeat the execution of the function in the same R session, exec time = 7.1 min. Removing all objects via rm(list=(all=TRUE)) and initiating gc (gc(reset=TRUE)) helps, but only slightly (exec

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?

Version 2.0-2 of bayesm is available on CRAN. This version includes bug fixes for rhierMnlRwMixture and rhierLinearModel. peter rossi _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-packages

Version 2.0-2 of bayesm is available on CRAN. This version includes bug fixes for rhierMnlRwMixture and rhierLinearModel. peter rossi _______________________________________________ R-packages mailing list R-packages at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-packages

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?