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mlr : Machine Learning in R

Package: mlr
Title: Machine Learning in R
Description: Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also an experimental extension for survival analysis, clustering and
general, example-specific cost-sensitive learning. Generic resampling,
including cross-validation, bootstrapping and subsampling. Hyperparameter
tuning with modern optimization techniques, for single- and multi-objective
problems. Filter and wrapper methods for feature selection. Extension of
basic learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be parallelized.
Authors@R: c(person("Bernd", "Bischl", email = "bernd_bischl@gmx.net", role =
c("aut", "cre")), person("Michel", "Lang", email =
"michellang@gmail.com", role = "aut"), person("Jakob", "Richter", email =
"code@jakob-r.de", role = "aut"), person("Jakob", "Bossek",
email = "jakob.bossek@tu-dortmund.de", role = "aut"), person("Leonard",
"Judt", email = "leonard.judt@tu-dortmund.de", role = "aut"),
person("Tobias", "Kuehn", email = "tobi.kuehn@gmx.de", role = "aut"),
person("Erich", "Studerus", email = "erich.studerus@upkbs.ch", role =
"aut"), person("Lars", "Kotthoff", email =
"larsko@cs.ubc.ca", role = "aut"), person("Zachary", "Jones", email =
"zmj@zmjones.com", role = "ctb"), person("Schiffner", "Julia", email =
"schiffner@math.uni-duesseldorf.de", role = "aut"))
URL: https://github.com/mlr-org/mlr
BugReports: https://github.com/mlr-org/mlr/issues
License: BSD_2_clause + file LICENSE
Encoding: UTF-8
Depends: R (>= 3.0.2), BBmisc (>= 1.9), ggplot2, ParamHelpers (>= 1.7),
stats
Imports: checkmate (>= 1.7.1), ggvis, methods, parallelMap (>= 1.3),
plyr, reshape2, shiny, survival
Suggests: ada, adabag, bartMachine, brnn, bst, care, caret (>= 6.0-57),
class, clue, cluster, clusterSim, clValid, cmaes, CoxBoost,
crs, Cubist, deepnet, DiceKriging, DiceOptim, DiscriMiner,
e1071, earth, elasticnet, elmNN, emoa, extraTrees, flare,
fields, FNN, fpc, frbs, FSelector, gbm, GenSA, glmnet, Hmisc,
irace (>= 1.0.7), kernlab, kknn, klaR, knitr, kohonen, laGP,
LiblineaR, lqa, MASS, mboost, mco, mda, mlbench, modeltools,
mRMRe, nnet, nodeHarvest (>= 0.7-3), neuralnet, numDeriv, pamr,
party, penalized, pls, PMCMR, pROC (>= 1.8), randomForest,
randomForestSRC (>= 2.0.5), ranger (>= 0.3.0), RCurl, rFerns,
rjson, rknn, rmarkdown, robustbase, ROCR, rotationForest,
rpart, rrlda, rsm, RSNNS, RWeka, sda, sparsediscrim, sparseLDA,
stepPlr, SwarmSVM, testthat, tgp, TH.data, xgboost, XML
LazyData: yes
ByteCompile: yes
Version: 2.8
VignetteBuilder: knitr
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-02-12 14:47:04 UTC; bischl
Author: Bernd Bischl [aut, cre],
Michel Lang [aut],
Jakob Richter [aut],
Jakob Bossek [aut],
Leonard Judt [aut],
Tobias Kuehn [aut],
Erich Studerus [aut],
Lars Kotthoff [aut],
Zachary Jones [ctb],
Schiffner Julia [aut]
Maintainer: Bernd Bischl <bernd_bischl@gmx.net>
Repository: CRAN
Date/Publication: 2016-02-13 08:37:35

● Data Source: CranContrib
● Cran Task View: MachineLearning
● 0 images, 185 functions, 2 datasets
Reverse Depends: 3

cmaesr : Covariance Matrix Adaptation Evolution Strategy

Package: cmaesr
Type: Package
Title: Covariance Matrix Adaptation Evolution Strategy
Description: Pure R implementation of the Covariance Matrix Adaptation -
Evolution Strategy (CMA-ES) with optional restarts (IPOP-CMA-ES).
Version: 1.0.1
Date: 2016-01-18
Authors@R: c(person("Jakob", "Bossek", email = "j.bossek@gmail.com", role =
c("aut", "cre")))
Maintainer: Jakob Bossek <j.bossek@gmail.com>
URL: https://github.com/jakobbossek/cmaesr
BugReports: https://github.com/jakobbossek/cmaesr/issues
License: BSD_2_clause + file LICENSE
Depends: ParamHelpers (>= 1.3), BBmisc (>= 1.6), checkmate (>= 1.1),
smoof (>= 1.1)
Imports: ggplot2
Suggests: testthat
ByteCompile: yes
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-01-19 13:06:25 UTC; jboss
Author: Jakob Bossek [aut, cre]
Repository: CRAN
Date/Publication: 2016-01-19 15:08:02

● Data Source: CranContrib
● 0 images, 16 functions, 0 datasets
● Reverse Depends: 0

smoof : Single and Multi-Objective Optimization Test Functions

Package: smoof
Type: Package
Title: Single and Multi-Objective Optimization Test Functions
Description: Provides generators for a high number of both single- and multi-
objective test functions which are frequently used for the benchmarking of
(numerical) optimization algorithms. Moreover, it offers a set of convenient
functions to generate, plot and work with objective functions.
Version: 1.3
Date: 2016-02-23
Authors@R: c(person("Jakob", "Bossek", email = "j.bossek@gmail.com", role =
c("aut", "cre")), person("Pascal", "Kerschke", email = "kerschke@uni-muenster.de",
role = "ctb"))
URL: https://github.com/jakobbossek/smoof
BugReports: https://github.com/jakobbossek/smoof/issues
License: BSD_2_clause + file LICENSE
Depends: ParamHelpers (>= 1.3), BBmisc (>= 1.6), checkmate (>= 1.1)
Imports: ggplot2, RColorBrewer, plot3D, mco, Rcpp (>= 0.11.0)
Suggests: testthat, rPython (>= 0.0-5)
LazyData: yes
ByteCompile: yes
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-02-23 15:36:32 UTC; jboss
Author: Jakob Bossek [aut, cre],
Pascal Kerschke [ctb]
Maintainer: Jakob Bossek <j.bossek@gmail.com>
Repository: CRAN
Date/Publication: 2016-03-01 17:15:35

● Data Source: CranContrib
● 0 images, 133 functions, 0 datasets
Reverse Depends: 1