Package: nonet 0.4.0

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.

Authors:Aviral Vijay [aut, cre], Sameer Mahajan [aut]

nonet_0.4.0.tar.gz
nonet_0.4.0.zip(r-4.5)nonet_0.4.0.zip(r-4.4)nonet_0.4.0.zip(r-4.3)
nonet_0.4.0.tgz(r-4.5-any)nonet_0.4.0.tgz(r-4.4-any)nonet_0.4.0.tgz(r-4.3-any)
nonet_0.4.0.tar.gz(r-4.5-noble)nonet_0.4.0.tar.gz(r-4.4-noble)
nonet_0.4.0.tgz(r-4.4-emscripten)nonet_0.4.0.tgz(r-4.3-emscripten)
nonet.pdf |nonet.html
nonet/json (API)

# Install 'nonet' in R:
install.packages('nonet', repos = c('https://aviralvijay-gslab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gslabdev/nonet/issues

Datasets:

On CRAN:

3.41 score 1 stars 17 scripts 206 downloads 2 exports 146 dependencies

Last updated 6 years agofrom:304487e495. Checks:1 OK, 7 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 19 2025
R-4.5-winNOTEFeb 19 2025
R-4.5-macNOTEFeb 19 2025
R-4.5-linuxNOTEFeb 19 2025
R-4.4-winNOTEFeb 19 2025
R-4.4-macNOTEFeb 19 2025
R-4.3-winNOTEFeb 19 2025
R-4.3-macNOTEFeb 19 2025

Exports:nonet_ensemblenonet_plot

Dependencies:askpassbackportsbase64encbitbit64blobbroombslibcachemcallrcaretcellrangerclassclicliprclockcodetoolscolorspaceconflictedcpp11crayoncurldata.tableDBIdbplyrdiagramdigestdplyrdtplyre1071evaluatefansifarverfastmapfontawesomeforcatsforeachfsfuturefuture.applygarglegenericsggplot2glmnetglobalsgluegoogledrivegooglesheets4gowergtablehardhathavenhighrhmshtmltoolshttridsipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmodelrmunsellnlmennetnumDerivopensslparallellypillarpkgconfigplyrprettyunitspROCprocessxprodlimprogressprogressrproxypspurrrR6raggrandomForestrappdirsRColorBrewerRcppRcppEigenreadrreadxlrecipesrematchrematch2reprexreshape2rlangrlistrmarkdownrpartrstudioapirvestsassscalesselectrshapesparsevctrsSQUAREMstringistringrsurvivalsyssystemfontstextshapingtibbletidyrtidyselecttidyversetimechangetimeDatetinytextzdbutf8uuidvctrsviridisLitevroomwithrxfunXMLxml2yaml

nonet_ensemble classification with nonet_plot

Rendered fromnonet_ensemble_class.Rmdusingknitr::rmarkdownon Feb 19 2025.

Last update: 2019-01-15
Started: 2018-12-31

nonet_ensemble Clustering with nonet_plot

Rendered fromnonet_ensemble_clustering.Rmdusingknitr::rmarkdownon Feb 19 2025.

Last update: 2019-01-15
Started: 2018-12-31

nonet_ensemble regression with nonet_plot

Rendered fromnonet_ensemble_reg.Rmdusingknitr::rmarkdownon Feb 19 2025.

Last update: 2019-01-15
Started: 2018-12-31