mlr_2.1:
- mlr now supports multi-criteria tuning
- mlr now supports cluster analysis (experimental)
- improve makeWeightedClassesWrapper: Hyperparams for class weighting are now supported, too.
- removed fix.factors option from randomForest, but added it in general to makeLearner,
  so it now works for all learners. 
  Helps when feature factor levels where dropped in newdata prediction data.frames
- more consistent results for tuning algorithms and parameters with "trafos" : 
  we always return the optimal settings on the transformed scale, but in the opt.path in
  the original scale.
- fix a bug when feature filtering resulted in a NoFeatureModel 
- resample now returns a data.frame "err.mgs" or error messages that might have occurred during resampling
- stratified resampling for survival

- new learners:
-- classif.cforest
-- classif.glmnet
-- classif.plsdaCaret
-- regr.cforest
-- regr.glmnet
-- regr.svm
-- surv.cforest
-- cluster.SimpleKMeans
-- cluster.EM
-- cluster.XMeans

- new measures
-- bac
-- db, dunn, g1, g2, silhouette

- new functions
-- makeClusterTask
-- removeHyperPars
-- tuneParamsMultiCrit
-- makeTuneMultiCritControlGrid, makeTuneMultiCritControlRandom, makeTuneMultiCritControlNSGA2
-- plotTuneMultiCritResult 
-- getFailureModelMsg

mlr_2.0:
- mlr now supports survival analysis models (experimental)
- mlr now supports cost-sensitive learning with example-specific costs (experimental)
- mlr now suppo
- added FeatSelWrapper and getFeatSelResult
- performance functions now allows to compute multiple measures
- added multiclass.roc performance measure
- observation weights can now also be specified in the task
- added option on.learner.warning to configureMlr to suppress warnings in learners
- fixed a bug in stratified CV where elements where not distributed as evenly as possible
  when the split number did not divide the number of observation
- added class.weights param for classif.svm
- add fix.factors option to randomForest
- generic standard error estimation in randomForest and BaggingWrapper
- added fixup.data option to task constructors, so basic data cleanup can be performed
- show.info is now an option in configureMlr
- learners now support taggable properties that can be queried and changed. also see below.
- listLearners(forTask) was unified
- removed tuning via R' optim method (makeTuneControlOptim), as the optimizers in there really make
  no sense for tuning
- Grid search was improved so one does not have to discretize parameters manually anymore
  (although this is still possible). Instead one now passes a 'resolution' argument. Internally we 
  now use ParamHelpers::generateGridDesign for this.
- toy tasks were added for convenient usage: iris.task, sonar.task, bh.task
  they also also have corresponding resampling instances, so you directly start working, e.g., iris.rin

- new learners:
-- classif.knn
-- classif.IBk
-- classif.LiblineaRBinary
-- classif.LiblineaRLogReg
-- classif.LiblineaRMultiClass
-- classif.linDA
-- classif.plr
-- classif.plsDA
-- classif.rrlda
-- regr.crs
-- regr.IBk
-- regr.mob
-- surv.CoxBoost
-- surv.coxph
-- surv.glmboost
-- surv.glmnet
-- surv.penalized
-- surv.randomForestSRC

- new measures
-- multiauc
-- cindex
--  meancosts, mcp

- new functions
-- removeConstantFeatures, normalizeFeatures, dropFeatures
-- getTaskNFeats
-- hasProperties, getProperties, setProperties, addProperties, removeProperties
-- showHyperPars
-- setId
-- listMeasures
-- isFailureModel
-- plotLearnerPrediction
-- plotThreshVsPerf
-- holdout, subsample, crossval, repcv, bootstrapOOB, bootstrapB632, bootstrapB632plus
-- listFilterMethods, getFilterValues, filterFeatures, makeFilterWrapper, plotFilterValues
-- benchmark
-- getPerformances, getAggrPerformances, getPredictions, getFilterResult, getTuneResult, getFeatSelResult
-- oversample, undersample, makeOversampleWrapper, makeUndersampleWrapper
-- smote, makeSmoteWrapper
-- downsample, makeDownsampleWrapper
-- makeWeightedClassesWrapper
-- makeTuneControlGenSA
-- makeModelMultiplexer, makeModelMultiplexerParamSet
-- makeCostSensTask, makeCostSensClassifWrapper, makeCostSensRegrWrapper, makeCostsSensWeightedPairsLearner
-- makeSurvTask
-- normalizeFeatures
-- createDummyFeatures
-- impute, reimpute, makeImputeWrapper, lots of impute<Method>, makeImputeMethod 

mlr_1.1:
- Initial release to CRAN
