Package: MNLR 0.1.0

MNLR: Interactive Shiny Presentation for Working with Multinomial Logistic Regression

An interactive presentation on the topic of Multinomial Logistic Regression. It is helpful to those who want to learn Multinomial Logistic Regression quickly and get a hands on experience. The presentation has a template for solving problems on Multinomial Logistic Regression. Runtime examples are provided in the package function as well as at <https://jarvisatharva.shinyapps.io/MultinomPresentation>.

Authors:Kartikeya Bolar

MNLR_0.1.0.tar.gz
MNLR_0.1.0.zip(r-4.5)MNLR_0.1.0.zip(r-4.4)MNLR_0.1.0.zip(r-4.3)
MNLR_0.1.0.tgz(r-4.4-any)MNLR_0.1.0.tgz(r-4.3-any)
MNLR_0.1.0.tar.gz(r-4.5-noble)MNLR_0.1.0.tar.gz(r-4.4-noble)
MNLR_0.1.0.tgz(r-4.4-emscripten)MNLR_0.1.0.tgz(r-4.3-emscripten)
MNLR.pdf |MNLR.html
MNLR/json (API)

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

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1 exports 0.00 score 104 dependencies 6 mentions 109 downloads

Last updated 5 years agofrom:10ceaecedc

Exports:MNLR

Dependencies:base64encbslibcachemcaretclasscliclockcodetoolscolorspacecommonmarkcpp11crayondata.tablediagramdigestdplyre1071ellipsisevaluatefansifarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2globalsgluegowergtablehardhathighrhtmltoolshttpuvipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrpromisesproxypurrrR6rappdirsRColorBrewerRcpprecipesreshape2rlangrmarkdownrpartsassscalesshapeshinysourcetoolsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitewithrxfunxtableyaml