Adds a high-level interface for 'keras' neural nets. kms() fits neural net and accepts R formulas to aid data munging and hyperparameter selection. kms() can optionally accept a compiled keras_sequential_model() from 'keras'.
kms() accepts a number of parameters (like loss and optimizer) and splits the data into (optionally sparse) test and training matrices. kms() facilitates setting advanced hyperparameters (e.g., regularization). kms() returns a single object with predictions, a confusion matrix, and function call details.

c(
person("Turgut", "Abdullayev", role = c("aut", "cre"),
email = "turqut.a.314@gmail.com"),
person(family = "Google Inc.", role = c("cph"))
)
'Keras Tuner' <https://keras-team.github.io/keras-tuner/> is a hypertuning framework made for humans.
It aims at making the life of AI practitioners, hypertuner
algorithm creators and model designers as simple as possible by
providing them with a clean and easy to use API for hypertuning.
'Keras Tuner' makes moving from a base model to a hypertuned one quick and
easy by only requiring you to change a few lines of code.
reticulate, tensorflow, rstudioapi, plotly, data.table,
RJSONIO, rjson, tidyjson, dplyr
knitr, rmarkdown, covr, stats, testthat, keras, tfdatasets,
purrr
Turgut Abdullayev [aut, cre],
Google Inc. [cph]

reticulate, tensorflow, rstudioapi, plotly, data.table,
RJSONIO, rjson, tidyjson, dplyr, echarts4r, magick, crayon,
keras
knitr, rmarkdown, tfdatasets, testthat, purrr

reticulate, tensorflow, rstudioapi, plotly, data.table,
RJSONIO, rjson, tidyjson, dplyr, echarts4r, crayon, keras,
magick
knitr, tfdatasets, testthat, purrr, rmarkdown

Nonparametric kernel estimation of the distribution function.
Bandwidth selection and estimation of related functions.
Alejandro Quintela del Rio and Graciela Estevez Perez
Nonparametric kernel distribution function estimation is
performed. Three automatic bandwidth selection methods for
nonparametric kernel distribution function estimation are
implemented: the plug-in of Altman and Leger, the plug-in of
Polansky and Baker, and the modified cross-validation of
Bowman, Hall and Prvan. The exceedence function, the mean
return period and the return level are also computed.