similar to: new packages: caret, caretLSF and caretNWS

New versions of the caret (3.08) and caretLSF (1.12) packages have been released. caret (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for data splitting, pre-processing and model tuning, as well as other miscellaneous functions. In the new versions: - The elasticnet and the lasso (from the enet package)

New versions of the caret (3.08) and caretLSF (1.12) packages have been released. caret (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for data splitting, pre-processing and model tuning, as well as other miscellaneous functions. In the new versions: - The elasticnet and the lasso (from the enet package)

Version 4.06 of the caret package was sent to CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 50 different models for classification and regression. See the package

Version 4.06 of the caret package was sent to CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 50 different models for classification and regression. See the package

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

Hi, I am using the caretNWS package to train some supervised regression models (gbm, lasso, random forest and mars). The problem I have encountered started when my training data set increased in the number of predictors and the number of observations. The training data set has 347 numeric columns. The problem I have is when there are more then 2500 observations the 5 sleigh objects start but do

Hi, I am using caretNWS on a RHEL x86_64 system and I am getting an error message that is nearly identical to the one occuring in http://www.r-project.org/nosvn/R.check/r-release-macosx-ix86/caretNWS-00check.txt Error in socketConnection(serverHost, port = port, open = "a+b", blocking = TRUE) : unable to open connection Calls: system.time ... .local -> tryCatch -> tryCatchList

Version 4.63 of the caret package is now on CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 99 different models for classification and regression. See the package vignettes

Version 4.63 of the caret package is now on CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 99 different models for classification and regression. See the package vignettes

Max and List, Could you advise me if I am using the proper caret syntax to carry out leave-one-out cross validation. In the example below, I use example data from the rda package. I use caret to tune over a grid and select an optimal value. I think I am then using the optimal selection for prediction. So there are two rounds of resampling with the first one taken care of by caret's train

Hi: I'm starting a research of Support Vector Regression. I want to obtain a model to predict a property A with a set of property B, C, D, ... This problem is very common for example in QSAR models. I want to know some examples and package that could help me in this way. I know about caret and e1071. But I' don't know if this package can work with continues variables.?

Dear users I am reposting this and hope it will be accepted this time. I am using copula package to fit my bivariate data and simulation. As explained in package documentation we can use our own data distribution to feed on copula as long as we have d, p and q (pdf, cdf and quantile) functions are available. Hence my code for those are: # Make the functions for data distribution

Dear all, I am a statistician doing research in QSAR, building regression models where the dependent variable is a numerical expression of some chemical activity and input variables are chemical descriptors, e.g. molecular weight, number of carbon atoms, etc. I am building regression models and I am confronted with a widely a technique called Y-RANDOMIZATION for which I have difficulties in

Hello R Users, I am interested in using R to generate quantitative structure-activity relationships (QSARs) for small molecules given a set of molecular descriptors (the X's) and biological data (Y's) (usually tab-delimited data files). Has anyone done this using R ? Would you be willing to share your R scripts (or ideas) for doing this with me ? I am particularly interested in

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Dear developers, I have jutst started working with caret and all the nice features it offers. But I just encountered a problem: I am working with a dataset that include 4 predictor variables in Descr and a two-category outcome in Categ (codified as a factor). Everything was working fine I got the results, confussion matrix etc. BUT for obtaining the AUC and predicted probabilities I had to add

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