Title: Algorithms for Class Distribution Estimation
person("Andre", "Maletzke", email = "andregustavom@gmail.com", role = c("aut","cre")),
person("Everton", "Cherman", email = "evertoncherman@gmail.com", role = "ctb"),
person("Denis", "dos Reis", email = "denismr@gmail.com", role = "ctb"),
person("Gustavo", "Batista", email = "g.batista@unsw.edu.au", role = "ths"))
Description: Quantification is a prominent machine learning task that has received an
increasing amount of attention in the last years. The objective is to predict the
class distribution of a data sample. This package is a collection of machine learning
algorithms for class distribution estimation. This package include algorithms from
different paradigms of quantification. These methods are described in the paper:
A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set
size in quantification assessment. In Proceedings of the Twenty-Ninth International
Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020.
Packaged: 2020-09-16 16:11:44 UTC; andregustavom

Packaged: 2021-04-10 22:40:11 UTC; andregustavom

Imports: caret, randomForest, stats, FNN
Packaged: 2022-01-20 12:57:28 UTC; andregustavom

Description: Interface to a large number of classification and
regression techniques, including machine-readable parameter
descriptions. Generic resampling, including cross-validation,
bootstrapping and subsampling. Hyperparameter tuning with
modern optimization techniques. Filter and wrapper methods for
feature selection. Extension of basic learners with additional
Imports: BBmisc (>= 1.3-64), parallelMap, codetools
Suggests: testthat, snowfall, ada, adabag, cmaes, DiceKriging,
DiceOptim, e1071, earth, FNN, FSelector, gbm, irace, kernlab,
kknn, klaR, mboost, mda, mlbench, nnet, party, penalized, pls,
randomForest, reshape, robustbase, rpart, rsm, RWeka, ROCR

Description: Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also a n experimental extension for surival analysis and cost-sensitive
learning. Generic resampling, including cross-validation, bootstrapping and
subsampling. Hyperparameter tuning with modern optimization techniques.
Filter and wrapper methods for feature selection. Extension of basic
learners with additional operations common in machine learning.
Depends: R (>= 3.0.0), ParamHelpers (>= 1.2), BBmisc (>= 1.7), stats
Imports: parallelMap (>= 1.1), codetools, survival, checkmate (>= 1.1)
Suggests: testthat, ada, adabag, class, cluster, cmaes, CoxBoost, crs,
DiceKriging, DiceOptim, DiscriMiner, e1071, earth, FNN,
FSelector, gbm, GenSA, ggplot2, glmnet, Hmisc, irace, kernlab,
kknn, klaR, LiblineaR, mboost, mda, mlbench, mRMRe, nnet,
party, penalized, pls, pROC, randomForest, randomForestSRC,
reshape2, rrlda, robustbase, rpart, rsm, RWeka, ROCR, stepPlr