Ivan Marin-Franch <imarinfr@indiana.edu>, Kamila Zychaluk
<kamila.zychaluk@liverpool.ac.uk>, and David H. Foster
<d.h.foster@manchester.ac.uk>

A Framework for Creating, Managing and Training Multiple Caret
Models
A minimalistic but flexible framework that facilitates the creation,
management and training of multiple 'caret' models. A model grid consists of
two components: (1) a set of settings that is shared by all models by
default, and (2) specifications that apply only to the individual models.
When the model grid is trained, model and training specifications are first
consolidated from the shared and the model specific settings into complete
'caret' model configurations. These models are then trained with the 'train'
function from the 'caret' package.
caret, purrr, dplyr, magrittr, ggplot2, lattice
testthat, knitr, rmarkdown, recipes, randomForest

Functions to Assess the Business Impact of Churn Prediction
Models
Calculate the financial impact of using a churn model in terms of cost, revenue, profit and return on investment.

A Collection of Useful Custom Tools for D&A and Modelling
person("Alex", "Stringer", email = "alex@alexstringer.ca", role = c("aut", "cre"))
Useful functions for performing common
data analysis tasks that I could not find in other packages. For
example, flexible functions for discretizing ("binning") continuous
variables are included, since this is a common technique used in
industry that is not well appreciated in academia. See the vignette
for a more detailed high-level explanation, or the documentation
for full details.
dplyr, ggplot2, stringr, foreach, magrittr, scales

c(person("Filippo", "Monari", role = c("aut", "cre"), email = "ingfimo@gmail.com"))
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
methods.
R (>= 4.0.0), methods, R6, LaF, foreach, doParallel
'modello.R' 'interfaces.R' 'session.R' 'numbers.R'
'numbers_math.R' 'utils.R' 'graphs.R' 'modules.R' 'optim.R'
'dataLoaders.R'