similar to: updated package eba 1.4-0

I have recently uploaded a new package to CRAN called eba, which stands for elimination-by-aspects models. It allows for fitting and testing probabilistic choice models, especially the BTL model (Bradley & Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models (Tversky, 1972), and preference tree (Pretree) models (Tversky & Sattath, 1979). I believe that the functions in this

I have recently uploaded a new package to CRAN called eba, which stands for elimination-by-aspects models. It allows for fitting and testing probabilistic choice models, especially the BTL model (Bradley & Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models (Tversky, 1972), and preference tree (Pretree) models (Tversky & Sattath, 1979). I believe that the functions in this

Dear all, I have submitted a new package called mpt to CRAN. It contains functions for fitting and testing multinomial processing tree (MPT) models, a class of statistical models for categorical data that involve latent parameters. These parameters are often interpreted as psychological processing variables. An introduction to these models is: Riefer, D.M., & Batchelder, W.H. (1988).

Dear all, I have submitted a new package called mpt to CRAN. It contains functions for fitting and testing multinomial processing tree (MPT) models, a class of statistical models for categorical data that involve latent parameters. These parameters are often interpreted as psychological processing variables. An introduction to these models is: Riefer, D.M., & Batchelder, W.H. (1988).

On Wed, 4 Jan 2006, Peter Muhlberger wrote: One comment in advance: please use a more meaningful subject. I would have missed this mail if a colleague hadn't pointed me to it. > I'm someone who from time to time comes to R to do applied stats for social > science research. [snip] > I would also prefer not to have to work through a > couple books on R or S+ to learn how to

Dear R users: I'm happy to announce the first CRAN release of the clubSandwich package: https://cran.r-project.org/web/packages/clubSandwich clubSandwich provides several variants of the cluster-robust variance estimator for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator of Bell and McCaffrey (2002). The package includes

Dear R users: I'm happy to announce the first CRAN release of the clubSandwich package: https://cran.r-project.org/web/packages/clubSandwich clubSandwich provides several variants of the cluster-robust variance estimator for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator of Bell and McCaffrey (2002). The package includes

Hello, all R-users! I am working on fitting a survival analysis model using the coxph function for Cox proportional hazards regression model. Data look like usual: ========================== group block death censor Group1 1 4 1 Group1 1 12 1 ... Group2 30 4 1 Group2 30 4 1 ... Group3 57 16

Hi folks, I am trying to perform a Granger causality analysis with panel data. There are some packages around for panel data analysis and Granger causality. However, I have found neither a package for both panel data and Granger causality nor any R procedures (homogenous/heterogenous causality hypotheses, related tests such as Wald, unit root tests etc.). Of course, someone must have

gregmisc 0.10.1 --------------- gregmisc 0.10.1 is now available at ftp://cran.r-project.org/incoming/ and will move to the regular contributed packages location (http://cran.r-project.org/src/contrib/PACKAGES.html#gregmisc) as soon as the CRAN administrators get caught up. Recent Changes --------------- Version 0.10.1 - Fixed bug in textplot() reported by Kevin Wright <kevin.d.wright at

gregmisc 0.10.1 --------------- gregmisc 0.10.1 is now available at ftp://cran.r-project.org/incoming/ and will move to the regular contributed packages location (http://cran.r-project.org/src/contrib/PACKAGES.html#gregmisc) as soon as the CRAN administrators get caught up. Recent Changes --------------- Version 0.10.1 - Fixed bug in textplot() reported by Kevin Wright <kevin.d.wright at

> -----Original Message----- > From: r-help-bounces at stat.math.ethz.ch > [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of Thomas Petzoldt > Sent: 06 September 2005 06:34 > Cc: petzoldt at rcs.urz.tu-dresden.de; R-Help > Subject: Re: [R] model selection vs. H0 based testing > > > Hello, > > I wish to thank Douglas Bates very much for clarification and

Dear R-packages list members, Last month, Peter Muhlberger posted a message to the r-help list that suggested, among other things, a desire for a more convenient method of performing Wald tests for statistical models fit in R. Thanks largely to Achim Zeleis's help, the linear.hypothesis function in the car package has been reworked so that (1) it is applicable to any model object that

Dear R-packages list members, Last month, Peter Muhlberger posted a message to the r-help list that suggested, among other things, a desire for a more convenient method of performing Wald tests for statistical models fit in R. Thanks largely to Achim Zeleis's help, the linear.hypothesis function in the car package has been reworked so that (1) it is applicable to any model object that

I'd like to be able to test linear hypotheses after setting up and running a model using optim or perhaps nlm. One hypothesis I need to test are that the average of several coefficients is less than zero, so I don't believe I can use the likelihood ratio test. I can't seem to find a provision anywhere for testing linear combinations of coefficients after max. likelihood. Cheers

Please reread my point #1: the tests of the (individual) coefficients in the model summary are not the same as the ANOVA tests. There is a certain correspondence between the two (i.e. between the coding of your categorical variables and the type of sum of squares; and for a model with a single predictor, F=t^2), but they are not the same in general. The t-test in the model coefficients is simply

Dear all, I know that my question is somewhat special but I tried several times to solve the problems on my own but I am unfortunately not able to compute the following test statistic using the quantreg package. Well, here we go, I appreciate every little comment or help as I really do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a

At the end of the 'Details:' section of the help on Anova (car package), it states: The standard R `anova' function calculates sequential (type-I) tests. These rarely test meaningful hypotheses. So I thought I'd try it. However, I was perplexed to get this message: Browse[1]> Anova.glm(leaf.glm1, type ="II") Error in terms.formula(formula, data = data) :

This is a follow up to the message I posted 3 days ago about how to estimate mixed ordinal logit models. I hope you don't mind that I am just pasting in the code and comments from an R file for your feedback. Actual estimates are at the end of the post. ### Subject: mixed ordinal logit via "augmented" data setup. ### I've been interested in estimating an ordinal logit model

hello, I work since a few time on R and i wanted to know how to obtain the Wald chi square value when you make a binary logistic regression. In fact, i have the z value and the signification but is there a script to see what is the value of Wald chi square. You can see my model below, Best regards, S??verine Erhel [Previously saved workspace restored] > m3 = glm(reponse2 ~ form +