DESCRIPTION

Package: nonet
Title: Weighted Average Ensemble without Training Labels
Version: 0.3.0
Authors@R: c(person("Aviral", "Vijay", role = c("aut", "cre"), email = "aviral.vijay@gslab.com"), person("Sameer", "Mahajan", role = "aut", email = "sameer.mahajan@gslab.com"))
Description: It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions.
Depends: R (>= 3.5.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: TRUE
Imports: caret (>= 6.0.78), dplyr, randomForest, ggplot2, rlist (>=
        0.4.6.1), glmnet, tidyverse, e1071, purrr, pROC (>= 1.13.0),
        rlang (<= 0.3.0.1),
RoxygenNote: 6.1.1
Suggests: testthat, knitr, rmarkdown, ClusterR
URL: https://aviralvijay-gslab.github.io/nonet/
BugReports: https://github.com/AviralVijay-GSLab/nonet/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-12-20 10:12:41 UTC; GS-1854
Author: Aviral Vijay [aut, cre],
  Sameer Mahajan [aut]
Maintainer: Aviral Vijay <aviral.vijay@gslab.com>
Repository: CRAN
Date/Publication: 2018-12-31 22:20:03 UTC