similar to: [R-pkgs] depmixS4 version 1.3-0 on CRAN

Package news (see below for general description of functionality) 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

depmixS4 has reached some form of maturity and therefore we have bumped its version number to 1.0-0 which is now on CRAN: http://cran.r-project.org/web/packages/depmixS4/index.html depmixS4 fits hidden (latent) Markov models of multivariate, mixed categorical and continuous data, otherwise known as dependent mixture models. Responses or observations can be modeled using GLMs, and additionally

depmixS4 has reached some form of maturity and therefore we have bumped its version number to 1.0-0 which is now on CRAN: http://cran.r-project.org/web/packages/depmixS4/index.html depmixS4 fits hidden (latent) Markov models of multivariate, mixed categorical and continuous data, otherwise known as dependent mixture models. Responses or observations can be modeled using GLMs, and additionally

On Tue, 30 May 2023 17:43:31 +0000 Heather Lucas <hlucas2 at lsu.edu> wrote: > Hello, > > I've been enjoying using the "Mixture and Hidden Markov Models in R" > by Visser & Speekenbrink to learn how to apply these analyses to my > own data using depmixS4. > > I currently have a fitted 4-state mixture model with three emissions > variables and one

I am running a latent class regression with 3 nominal and 2 ordinal variables using depmixS4 but the available response models do not include one for ordinal response.  How do I go about this?   Penny [[alternative HTML version deleted]]

Dear helpers, is there any possible that transition (in depmixS4) is in scale of two variable, e.g transition=~scale(x1,x2)? If it can be, how transition of two variable (covariate time) can be worked in depmixS4-hidden markov model for time series. Many thanks, nglthu -- View this message in context: http://r.789695.n4.nabble.com/depmixS4-transition-tp4572726p4572726.html Sent from the R help

Hello, I've been enjoying using the "Mixture and Hidden Markov Models in R" by Visser & Speekenbrink to learn how to apply these analyses to my own data using depmixS4. I currently have a fitted 4-state mixture model with three emissions variables and one binomial covariate (HS). I am trying to compute confidence intervals using the following code, where fmms4s is the model:

Hi I am trying to use depmixS4 package. Based on the documentation, it seems that depmix allows one to fit an HMM model based on a training data with time-varying co-variates. However, I did not find any routines which can help test the accuracy on the fitted HMM model on out-of-sample data. Can someone confirm if that is indeed the case? Also are there any alternate packages for the same? Thanks

Hi, When running R CMD check I'm getting a number of errors that I don't quite follow and don't know where to start looking for an answer, any hints appreciated. R CMD check trunk * checking for working latex ... OK * using log directory '/Users/ivisser/Documents/projects/ depmixProject/depmixNew/rforge/depmix/trunk.Rcheck' * using R version 2.6.2 (2008-02-08) * checking

I am getting started with using the depmixS4 package. First, I would like to see I am very impressed with its speed and flexibility. The question I have is regarding predicting on new data. I want to fit the model on some sequences with observed responses, and then make predictions on the right end of the sequences where the responses are not observed. I see no prediction functionality anywhere,

We are trying to build a human respiration model. Preliminary analysis of some breathing signals has shown that humans breathe through switching among a finite number of patterns. Hidden Markov seems to be the right approach. Since most of our code is written in R scripting language, finding an R package implementing an HMM that we can use for our prototype would be very helpful. I have been

As an alternative to Latent GOLD, I'm wondering if anyone knows of and R package that can manage Latent Class Analysis with mixed variable types (continuous, ordinal, and nominal/binary). [[alternative HTML version deleted]]

Hi Yves lavaan looks like a very nice package. From the tutorial introduction I see you create path diagrams for some of the models you describe. How did you do this? I don't see a function for this in the package. I know there is a path.diagram function in the sem package that uses dot to draw the diagram, but I've always found the layouts from dot somewhat strange for path diagrams

Hi Guys, Recently I wrote a package for dealing with Markov Switching Regressions in R and it is included in the Rmetrics project. https://r-forge.r-project.org/projects/rmetrics/ Everything works fine when I use it in computer. But, the package depends on the use of optimization functions from the package Rdonlp2, which is not available on CRAN. So, if I have Rdonlp2 in my laptop (or any

Hi all, I have a question, is there any R package dealing with latent transition analysis with both categorical and continuous indicators? So far what I found from GOOGLE are only packages dealing with latent class analysis. So what about the longitudinal situation? Any way we could look at the transition from one class to another across time points? Thank you very much. ya [[alternative

Hi All, Does someone have an idea of how to cleverly convert a categorical timeseries into a transition matrix? Ie, I have something like: x<- c(1,1,2,1,1,2,2,2,1,2), And I want a matrix with counts and/or probabilities: > tr <- matrix(c(2,3,2,2),2,2) > tr [,1] [,2] [1,] 2 2 [2,] 3 2 Meaning that there are two transitions from 1 to 1, two from 1 to 2, three from 2 to 1

Dear R-helpers, When installing a source package I can suppress the compilation of .chm files by using the --docs="normal" option. Is it also possible to suppress the creation and checking of .chm files when calling R CMD check ? best, ingmar

Is there a package in R that handles general linear (in-)equality + box constrained optimization? If it is not there, could anyone advise me which way to go? And/or point me to packages that solve these problems partially? best, ingmar -- Ingmar Visser Department of Psychology, University of Amsterdam Roetersstraat 15, 1018 WB Amsterdam The Netherlands http://users.fmg.uva.nl/ivisser/ tel:

Dear useRs, I'm trying to create a barplot like so: x=matrix(1:10,2,5) barplot(x,leg=c("left","right"),besid=T) The legend is placed in default position topright, however the data are plotted there too. I tried controlling the legend position by adding x="topleft" but this results in an error that x matches multiple formal arguments. Leaving out the legend

Dear R users, Is there any package for Latent Class Analysis (to be used in a clustering application) which supports mixed indicator variables (categorical and continuous)? Alternatively, is there any other clustering algorithm available that supports this type of data? Thanks in advance for your help. Regards, Lars. [[alternative HTML version deleted]]