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 open-discussion General Discussion 2 2 2008-05-28 07:32 No Moderation
 help Get Public Help 19 39 2015-03-08 19:59 No Moderation
 developers Project Developer Discussion 2 4 2008-03-03 10:58 No Moderation
 version-0.1-1-on-cran Version 0.1-1 is released on CRAN. Changes mainly involve issues to make depmixS4 compatible with R version 2.7.0 0 0 1970-01-01 00:00 No Moderation
 depmixs4-version-0.2-0-now-on-cran Most important changes include addition of more GLM distributions, addition of mix class to fit mixtures and latent class models, ie without dependencies, addition of a simulate function to generate new data from (fitted) models; finally, the help files have undergone major restructuring (as well as the internal structure of R files). 0 0 1970-01-01 00:00 No Moderation
 depmixs4-0.2-2-submitted-to-cran This new release mostly has some minor changes and an important bug fix in multinomial response models with covariates (giving odd results and violating the baseline constraint in baseline multinomial logit models). 0 0 1970-01-01 00:00 No Moderation
 depmixs4---version-0.3-0-on-cran The 0.3-0 version of depmixS4 contains an example of a model with multivariate normal distributions on the ?makeDepmix help page and some minor improvements in code and documentation. 0 0 1970-01-01 00:00 No Moderation
 depmixs4---version-1.0-0-on-cran---paper-on-jstatsoft.org depmixS4 has reached some form of maturity and therefore we have bumped its version number to 1.0-0. The reason for this is that the paper illustrating the possibilities of depmixS4 has now been published in the Journal of Statistical Software and can be found there: http://www.jstatsoft.org/v36/i07 Except for a few minor issues there are no large changes since the previous version except the addition of a vignette which has the same contents as the JSS paper. 0 0 1970-01-01 00:00 No Moderation
 depmixs4---version-1.0-1-submitted-to-cran A new version of depmixS4 is submitted to CRAN which fixes a number of bugs. One related to multinomial observation models with n>1 and a logit link function, and one concerning the generation of starting values. 0 0 1970-01-01 00:00 No Moderation
 depmixs4---version-1.1-0-on-cran The main change in this version is that the main loop for computing the forward and backward variables has been moved into C resulting in considerable speed improvements. Depending on model specifics this results in a 2-4 fold speed increase when fitting models. Another noteworthy change is the addition of several examples such as a Poisson change point model (with artificial data, see ?responses), and an example of a financial time series (logged returns from the S&P 500, thanks to Chen Haibo for providing the example, see ?depmix). 0 0 1970-01-01 00:00 No Moderation
 depmixs4-version-1.2-0-on-cran--support-for-missing-data Version 1.2-0 has been released on CRAN. The major change in this version is that it supports missing data in the responses (missing covariates result in an error). The vignette accompanying the package has a brief explanation of how missing data is treated within the EM algorithm and using direct optimization of the loglikelihood. 0 0 1970-01-01 00:00 No Moderation
 depmixs4-version-1.3-0-on-cran depmixS4 version 1.3-0 has been released on CRAN. See the NEWS file for an overview of all changes. The most important user-visible changes are: 1) more compact pretty-printing of parameters in print/summary of (dep)mix objects (following lm/glm style of presenting results) 2) some speed improvements in the EM algorithm, most notable in large data/models 3) EM has an optional argument to use the classification likelihood instead of the usual likelihood; this can be useful as a means of starting value generation; use with caution as results are often unstable. Best, happy mixing, Ingmar 0 0 1970-01-01 00:00 No Moderation
 depmixs4-version-1.3-0-on-cran depmixS4 version 1.3-0 has been released on CRAN. See the NEWS file for an overview of all changes. The most important user-visible changes are: 1) more compact pretty-printing of parameters in print/summary of (dep)mix objects (following lm/glm style of presenting results) 2) some speed improvements in the EM algorithm, most notable in large data/models 3) EM has an optional argument to use the classification likelihood instead of the usual likelihood; this can be useful as a means of starting value generation; use with caution as results are often unstable. Best, happy mixing, Ingmar 0 0 1970-01-01 00:00 No Moderation
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