About: Regularization for semiparametric additive hazards regression

Fetched by r-cran-robot on 2013-05-01 00:00:04.295389

About: Mining Association Rules and Frequent Itemsets

Fetched by r-cran-robot on 2013-05-01 00:00:04.485553

About: Big Random Forests

Fetched by r-cran-robot on 2013-05-01 00:00:04.813177

About: A wrapper algorithm for all-relevant feature selection

Fetched by r-cran-robot on 2013-05-01 00:00:04.966599

About: A comprehensive data mining environment, with a variety of machine learning components.

Modifications following feedback from Knime main Author.

About: A descriptive and programming language independent format and API for the simplified configuration, documentation, and design of computer experiments.

Initial Announcement on mloss.org.

About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.

2013-04-24 Version 4.1

New features:

• Multinomial probit classification with nested-EP. Jaakko Riihimäki, Pasi Jylänki and Aki Vehtari (2013). Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood. Journal of Machine Learning Research 14:75-109, 2013.
• Marginal posterior corrections for latent values. Cseke & Heskes (2011). Approximate Marginals in Latent Gaussian Models. Journal of Machine Learning Research 12 (2011), 417-454
  • Laplace: cm2 and fact
  • EP: fact

Improvements

• lgpdens ignores now NaNs instead of giving error
• gp_cpred has a new option 'target' accpeting values 'f' or 'mu'
• unified gp_waic and gp_dic
  • by default return mlpd
  • option 'form' accetps now values 'mean' 'all' 'sum' and 'dic'
• improved survival demo demo_survival_aft (accalerated failure time)
  • renamed and improved from demo_survival_weibull
• rearranged some files to more logical directories
• bug fixes

New files

• gp_predcm: marginal posterior corrections for latent values.
• demo_improvedmarginals: demonstration of marginal posterior corrections
• demo_improvedmarginals2: demonstration of marginal posterior corrections
• lik_multinomprobit: multinomial probit likelihood
• demo_multiclass_nested_ep: demonstration of nested EP with multinomprobit

About: HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making.

• New and improved HDDM model with the following changes:
  • Priors: by default model will use informative priors (see http://ski.clps.brown.edu/hddm_docs/methods.html#hierarchical-drift-diffusion-models-used-in-hddm) If you want uninformative priors, set informative=False.
  • Sampling: This model uses slice sampling which leads to faster convergence while being slower to generate an individual sample. In our experiments, burnin of 20 is often good enough.
  • Inter-trial variablity parameters are only estimated at the group level, not for individual subjects.
  • The old model has been renamed to HDDMTransformed.
  • HDDMRegression and HDDMStimCoding are also using this model.
• HDDMRegression takes patsy model specification strings. See http://ski.clps.brown.edu/hddm_docs/howto.html#estimate-a-regression-model and http://ski.clps.brown.edu/hddm_docs/tutorial_regression_stimcoding.html#chap-tutorial-hddm-regression
• Improved online documentation at http://ski.clps.brown.edu/hddm_docs
• A new HDDM demo at http://ski.clps.brown.edu/hddm_docs/demo.html
• Ratcliff's quantile optimization method for single subjects and groups using the .optimize() method
• Maximum likelihood optimization.
• Many bugfixes and better test coverage.
• hddm_fit.py command line utility is depracated.

About: The apcluster package implements Frey's and Dueck's Affinity Propagation clustering in R. The package further provides leveraged affinity propagation, exemplar-based agglomerative clustering, and various tools for visual analysis of clustering results.

• re-implementation of heatmap() method: dendrograms can now be plotted even for APResult and ExClust objects as well as for cluster hierarchies based on prior clusterings; color bars can now be switched off and colors can be changed by user (by new 'sideColor' argument); dendrograms can be switched on and off (by 'Rowv' and 'Colv' arguments);
• added as.hclust() and as.dendrogram() methods
• added new arguments 'base', 'showSamples', and 'horiz' to the plot() method with signature (x="AggExResult", y="missing"); moreover, parameters for changing the appearance of the height axis are now respected as well
• streamlining of methods (redundant definition of inherited methods removed)
• various minor improvements of code and documentation