nonet: Weighted Average Ensemble without Training Labels

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.

Version: 0.4.0
Depends: R (≥ 3.5.0)
Imports: caret (≥ 6.0.78), dplyr, randomForest, ggplot2, rlist (≥, glmnet, tidyverse, e1071, purrr, pROC (≥ 1.13.0), rlang (≥ 0.2.1)
Suggests: testthat, knitr, rmarkdown, ClusterR
Published: 2019-01-15
Author: Aviral Vijay [aut, cre], Sameer Mahajan [aut]
Maintainer: Aviral Vijay <aviral.vijay at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: nonet results


Reference manual: nonet.pdf
Vignettes: nonet ensemble classification with nonet plot
nonet ensemble Clustering with nonet plot
nonet ensemble regression with nonet plot
Package source: nonet_0.4.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel: not available
OS X binaries: r-release: nonet_0.4.0.tgz, r-oldrel: not available
Old sources: nonet archive


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