Title: Rapid Implementation of Machine Learning Algorithms for Genomic
person("Daniel", "Tylee", email = "dantylee@gmail.com", role = "ctb")
Description: Supervised machine learning has an increasingly important role in biological
studies. However, the sheer complexity of classification pipelines poses a significant
barrier to the expert biologist unfamiliar with machine learning. Moreover,
many biologists lack the time or technical skills necessary to establish their own
pipelines. This package introduces a framework for the rapid implementation of
high-throughput supervised machine learning built with the biologist user in mind.
Written by biologists, for biologists, this package provides a user-friendly interface
that empowers investigators to execute state-of-the-art binary and multi-class
classification, including deep learning, with minimal programming
Imports: cluster, MASS, e1071, lattice, methods, mRMRe, nnet,
pathClass, plyr, stats, randomForest, ROCR, sampling
Suggests: Biobase, GEOquery, h2o, golubEsets, knitr, limma, magrittr,

Imports: affy, Biobase, cluster, MASS, e1071, lattice, methods, mRMRe,
nnet, pathClass, plyr, stats, randomForest, ROCR, sampling
Suggests: GEOquery, h2o, golubEsets, knitr, limma, magrittr, rmarkdown,

Description: Supervised machine learning has an increasingly important role in data
analysis. This package introduces a framework for rapidly implementing supervised
machine learning in a high-throughput manner. This package provides a user-friendly
interface that empowers investigators to execute state-of-the-art binary and
multi-class classification, including deep learning, with minimal programming
Imports: cluster, MASS, e1071, lattice, magrittr, methods, nnet, plyr,
Suggests: Biobase, edgeR, GEOquery, h2o, knitr, limma, mRMRe,

Title: Rapid Deployment of Machine Learning Algorithms
analysis. This package introduces a framework for rapidly building and deploying
supervised machine learning in a high-throughput manner. This package provides a
user-friendly interface that empowers investigators to execute state-of-the-art
binary and multi-class classification, as well as regression, with minimal
Imports: cluster, MASS, e1071, lattice, methods, nnet, plyr, stats,
Suggests: Biobase, edgeR, GEOquery, h2o, knitr, limma, magrittr, mRMRe,