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: 0.1.2
Depends: R (≥ 3.2.3)
Imports: crayon, dplyr, MASS, lme4, rpart, nnet, e1071, car, nortest, clisymbols
Suggests: testthat, knitr, rmarkdown
Published: 2018-03-07
Author: Agustín Pérez-Martín ORCID iD [aut], Agustín Pérez-Torregrosa ORCID iD [cre], Marta Vaca-Lamata ORCID iD [aut], Antonio José Verdú-Jover ORCID iD [aut]
Maintainer: Agustín Pérez-Torregrosa <agustin.perez01 at goumh.umh.es>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: OptimClassifier results


Reference manual: OptimClassifier.pdf
Package source: OptimClassifier_0.1.2.tar.gz
Windows binaries: r-devel: OptimClassifier_0.1.2.zip, r-release: OptimClassifier_0.1.2.zip, r-oldrel: OptimClassifier_0.1.2.zip
OS X El Capitan binaries: r-release: OptimClassifier_0.1.2.tgz
OS X Mavericks binaries: r-oldrel: not available
Old sources: OptimClassifier archive


Please use the canonical form https://CRAN.R-project.org/package=OptimClassifier to link to this page.