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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "nonet" in publications use:'
type: software
license: MIT
title: 'nonet: Weighted Average Ensemble without Training Labels'
version: 0.4.0
doi: 10.32614/CRAN.package.nonet
abstract: 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.
authors:
- family-names: Vijay
  given-names: Aviral
  email: aviral.vijay@gslab.com
- family-names: Mahajan
  given-names: Sameer
  email: sameer.mahajan@gslab.com
repository: https://aviralvijay-gslab.r-universe.dev
repository-code: https://github.com/GSLabDev/nonet
commit: 304487e49552d1e00b9f3e0f20227c960b9767b6
url: https://open.gslab.com/nonet/
date-released: '2019-01-15'
contact:
- family-names: Vijay
  given-names: Aviral
  email: aviral.vijay@gslab.com