OptimClassifier: Create the Best Train for Classification Models

Patterns searching and binary classification in economic and financial data is a large field of research. There are a large part of the data that the target variable is binary. Nowadays, many methodologies are used, this package collects most popular and compare different configuration options for Linear Models (LM), Generalized Linear Models (GLM), Linear Mixed Models (LMM), Discriminant Analysis (DA), Classification And Regression Trees (CART), Neural Networks (NN) and Support Vector Machines (SVM).

Version: 0.1.5
Depends: R (≥ 3.2.3)
Imports: crayon, dplyr, MASS, lme4, rpart, nnet, e1071, lmtest, nortest, clisymbols, ggplot2
Suggests: testthat, knitr, rmarkdown
Published: 2020-01-14
Author: Agustin Perez-Martin ORCID iD [aut], Agustin Perez-Torregrosa ORCID iD [cre, aut], Marta Vaca-Lamata ORCID iD [aut], Antonio Jose Verdu-Jover ORCID iD [aut]
Maintainer: Agustin Perez-Torregrosa <agustin.perez01 at goumh.umh.es>
BugReports: https://github.com/economistgame/OptimClassifier/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://economistgame.github.io/OptimClassifier
NeedsCompilation: no
Materials: README NEWS
CRAN checks: OptimClassifier results


Reference manual: OptimClassifier.pdf
Vignettes: An Introduction to OptimClassifier
Package source: OptimClassifier_0.1.5.tar.gz
Windows binaries: r-devel: OptimClassifier_0.1.5.zip, r-release: OptimClassifier_0.1.5.zip, r-oldrel: OptimClassifier_0.1.5.zip
macOS binaries: r-release: OptimClassifier_0.1.5.tgz, r-oldrel: OptimClassifier_0.1.5.tgz
Old sources: OptimClassifier archive


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