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R provides a very powerful package called laplacesdemon for bayesian inference using the laplace distribution. I was wondering if there is any equivalent package for Matlab?
Thanks!
The question was a bit short so I'm not sure if this is exactly what you are asking for but here goes:
No - there is no Laplacedemon equivalent in Matlab
Yes - there are lots of Matlab packages that partly overlap with LaplaceDemon. As I don't know exactly what you want to do my recommendation is likely to have a high variance. Having established that I suggest that you take a look at http://becs.aalto.fi/en/research/bayes/gpstuff/ :). If you feel comfortable with using MCMC directly, you can take a look at http://helios.fmi.fi/~lainema/mcmc/. (I believe that matlab has some functions for this directly in their statistics or/and econometrics toolboxes)
You could also run R directly from Matlab http://neurochannels.blogspot.se/2010/05/how-to-run-r-code-in-matlab.html or http://rwiki.sciviews.org/doku.php?id=tips:callingr:matlab
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I am looking to run diagnostics for categorical variable classifications in R. I am particularly interested in getting User's and Producer's accuracy and kappa statistics. There used to be a package (nnDiag) in R that calculated all of these metrics. It appears as if the package was removed from CRAN. Does anyone know why it was discontinued and if there is a comparable package in R? I am running R 3.1.1.
Caret does Cohen's Kappa and summary stats quite well.
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Is there any package that you would recommend which can be used to calculate the precision, F1, recall for multi class classification task in R. I tried to use ROCR but it states that:
ROCR currently supports only evaluation of binary classification tasks
I know that you were looking for a solution in R. That said, this is a link to a nice solution library in Python, using scikit-learn version 0.14. Python is very similar to R in a lot of respects (if you haven't used it before), and this could be a good place to start.
Another place you might want to look, if you are focused on R, is the the PerfMeas package. As I quote, this "Package implements different performance measures for
classification and ranking tasks. AUC, precision at a given recall, F-score for single and multiple classes are available."
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I am looking for a free software for mathematical modeling.
Here is a list of things I might be willing to achieve with this software: Integrating functions, solving differential equations, graph theory, analyzing infinite series, local stability analysis, Taylor series, get eigenvectors, compute the long term behaviour of a system of equations, etc...
Here is a related SE post. I am surprised that nobody is suggesting R. I am currently a R user and already use R for graph theory. Therefore I would appreciate to use R also to make other mathematical modeling. Is R less efficient that Sage, SimPy, Mathematica and others for mathematical modeling? Why? Do you know a manual providing exaplanation for how to make mathematical modeling with R?
Thank you
Sounds like R is your first way to go. It does not make to good sense to compare R with any other tool in such a braod way you are asking for. R packages differ largely in efficiency, some are in fact C tools while others are written in the R language. As a start R can hardly be any wrong and is free.
Matlab might be a stable alternative, Julia is rising but still pre alpha.
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I'm doing a study on time series data of protein phosphorylation events, and I want to use dynamic bayesian network to learn the network structure. I found your Bayes net toolbox can be helpful for my study.But I'm more familiar with R. Is there any R packages equivalent to Matlab's Bayes net toolbox, which can learn the network structure using time series data? Thank you!
try the following R packages to perform dynamic bayesian networks inference:
http://cran.r-project.org/web/packages/G1DBN/index.html
and
http://cran.r-project.org/web/packages/ebdbNet/index.html
enjoy!
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I'm looking for a simple MCMC Bayesian network Inference function/package in R. Essentially, I just want a function that accepts the matrix containing my samples x my variables ( + optional parameters like burn-in and iteration counts) and returns the adjacency matrix of the inferred network.
I had been using the Matlab toolkit "BayesNet", which offers a simple 'learn_struct_mcmc' function which offers most of what I'm looking for. I'm looking for an equivalent in R.
I've been looking through the packages in http://cran.r-project.org/web/views/Bayesian.html, but haven't seen anything that quite does what I'm looking for. I wasn't trained as a statistician, and many of the packages I've looked at on that list either lack documentation or have more complicated statistics than I'm comfortable wiring together myself. I just need a simple function with "reasonable" defaults to get started.
Bonus points for something that leverages Rmpi or snow.
This gave me 132 possible relevant functions.
library(sos)
findFn("bayesian network")
How about this package.
http://cran.r-project.org/web/packages/MCMCpack/index.html
The closest thing to what I had in mind that I've found is the hc() function in the blearn package. They have a variety of other Bayesian network inference functions, as well, some of which can use snow.