Authors@R: c(person("Filippo", "Monari", role = c("aut", "cre"), email = "ingfimo@gmail.com"))
Description: Allows to build and train  different flavors of Neural Networks.
In particular, it can be used to build fairly complex computational graphs,
which ca be automatically differentiated and optimized with gradient descent
Depends: R (>= 4.0.0), methods, R6, LaF, foreach, doParallel
Collate: 'modello.R' 'interfaces.R' 'session.R' 'numbers.R'
'numbers_math.R' 'utils.R' 'graphs.R' 'modules.R' 'optim.R'

Title: Plots to Evaluate the Business Performance of Predictive Models
Description: Plots to assess the quality of predictive models from a business perspective.
Using these plots, it can be shown how implementation of the model will impact business
targets like response on a campaign or return on investment. Different scopes can be selected:
compare models, compare datasets or compare target class values and various plot customization
targets like response on a campaign. Different scopes can be selected: compare models, compare
datasets or compare target class values and various plot customization and highlighting options
dplyr (>= 0.7.7), RColorBrewer (>= 1.1.2), ggfittext (>=
nnet(>= 7.3-12), e1071, h2o, keras, knitr, rmarkdown, testthat,
xgboost, stringr, kableExtra, lattice, ranger, glmnet
Packaged: 2019-04-16 13:21:57 UTC; j.nagelkerke

Packaged: 2020-10-13 00:19:16 UTC; j.nagelkerke

Title: Modelling Functions that Work with the Pipe
Description: Functions for modelling that help you seamlessly integrate
modelling into a pipeline of data manipulation and visualisation.
Imports: magrittr, purrr (>= 0.2.2), lazyeval (>= 0.2.0), tibble,

Suggests: testthat, ggplot2, covr, compiler

Imports: broom, magrittr, purrr (>= 0.2.2), rlang (>= 1.0.6), tibble,
Suggests: compiler, covr, ggplot2, testthat (>= 3.0.0)
Packaged: 2022-11-11 16:14:48 UTC; hadleywickham

Imports: broom, dplyr, magrittr, purrr (>= 0.2.2), tibble, tidyr (>=
Suggests: compiler, covr, ggplot2, testthat

Description: Functions for modelling that help you seamlessly
integrate modelling into a pipeline of data manipulation and
Imports: broom, dplyr, magrittr, purrr (>= 0.2.2), rlang (>= 0.2.0),