Package: shinymodels 0.1.1.9000

Simon Couch

shinymodels: Interactive Assessments of Models

Launch a 'shiny' application for 'tidymodels' results. For classification or regression models, the app can be used to determine if there is lack of fit or poorly predicted points.

Authors:Max Kuhn [aut], Shisham Adhikari [aut], Julia Silge [aut], Simon Couch [aut, cre], Posit Software, PBC [cph, fnd]

shinymodels_0.1.1.9000.tar.gz
shinymodels_0.1.1.9000.zip(r-4.5)shinymodels_0.1.1.9000.zip(r-4.4)shinymodels_0.1.1.9000.zip(r-4.3)
shinymodels_0.1.1.9000.tgz(r-4.4-any)shinymodels_0.1.1.9000.tgz(r-4.3-any)
shinymodels_0.1.1.9000.tar.gz(r-4.5-noble)shinymodels_0.1.1.9000.tar.gz(r-4.4-noble)
shinymodels_0.1.1.9000.tgz(r-4.4-emscripten)shinymodels_0.1.1.9000.tgz(r-4.3-emscripten)
shinymodels.pdf |shinymodels.html✨
shinymodels/json (API)
NEWS

# Install 'shinymodels' in R:

install.packages('shinymodels', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tidymodels/shinymodels/issues

Pkgdown site:https://shinymodels.tidymodels.org

Datasets:

ames_mlp_itr - Iterative optimization of neural network
cars_bag_vfld - Resampled bagged tree results
cell_race - A CART classification tree tuned via racing
scat_fda_bt - Tuned flexible discriminant analysis results
two_class_final - Test set results for logistic regression

On CRAN:

shiny

6.38 score 46 stars 50 scripts 145 downloads 25 exports 125 dependencies

Last updated 2 months agofrom:f4e8798bed. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Dec 23 2024
R-4.5-win	OK	Dec 23 2024
R-4.5-linux	OK	Dec 23 2024
R-4.4-win	OK	Dec 23 2024
R-4.4-mac	OK	Dec 23 2024
R-4.3-win	OK	Dec 23 2024
R-4.3-mac	OK	Dec 23 2024

Exports:%>%display_selected explore first_class_prob_name first_level format_hover organize_data performance_object plot_multiclass_conf_mat plot_multiclass_obs_pred plot_multiclass_pr plot_multiclass_pred_factorcol plot_multiclass_pred_numcol plot_multiclass_roc plot_numeric_obs_pred plot_numeric_res_factorcol plot_numeric_res_numcol plot_numeric_res_pred plot_twoclass_conf_mat plot_twoclass_obs_pred plot_twoclass_pr plot_twoclass_pred_factorcol plot_twoclass_pred_numcol plot_twoclass_roc shiny_models

Dependencies:askpass base64enc bslib cachem class cli clock codetools colorspace commonmark cpp11 crayon crosstalk curl data.table diagram dials DiceDesign digest doFuture dplyr DT evaluate fansi farver fastmap fontawesome foreach fs furrr future future.apply generics ggplot2 globals glue gower GPfit gtable hardhat highr htmltools htmlwidgets httpuv httr ipred isoband iterators jquerylib jsonlite KernSmooth knitr labeling later lattice lava lazyeval lhs lifecycle listenv lubridate magrittr MASS Matrix memoise mgcv mime modelenv munsell nlme nnet numDeriv openssl parallelly parsnip pillar pkgconfig plotly prettyunits prodlim progressr promises purrr R6 rappdirs RColorBrewer Rcpp recipes rlang rmarkdown rpart rsample sass scales sfd shape shiny shinydashboard slider sourcetools sparsevctrs SQUAREM stringi stringr survival sys tailor tibble tidyr tidyselect timechange timeDate tinytex tune tzdb utf8 vctrs viridisLite warp withr workflows xfun xtable yaml yardstick

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Iterative optimization of neural network	ames_mlp_itr
Resampled bagged tree results	cars_bag_vfld
A CART classification tree tuned via racing	cell_race
Explore model results	explore.default explore.tune_results
Tuned flexible discriminant analysis results	scat_fda_bt
Test set results for logistic regression	two_class_final