Title: Modeling physical computer codes with functional outputs using
Suggests: rpart, e1071, randomForest, gbm, klaR, wmtsa, mlegp
Description: Statistical learning with vectorial inputs and smooth 1D
curves as outputs. The main function build a model from n

Suggests: class, rpart, e1071, kernlab, randomForest, klaR, wmtsa,
curves as outputs. The main function builds a model from n

Title: Data Sets Used Useful for Modeling Packages
Description: Data sets used for demonstrating or testing model-related packages are contained in this package.

Description: Data sets used for demonstrating or testing model-related

Title: Data Sets Useful for Modeling Examples
Imports: dplyr, MASS, purrr, rlang, tibble

Description: Uses 'dplyr' and 'tidyeval' to fit statistical models inside the database. It currently supports KMeans and linear regression models.
Imports: dplyr(>= 0.7), rlang, purrr, tibble, tidyr, progress, ggplot2,
Suggests: dbplyr, testthat, knitr, rmarkdown, nycflights13, RSQLite,

Imports: dplyr(>= 0.7), rlang, purrr, tibble, progress, ggplot2

Imports: dplyr(>= 0.7), rlang, purrr, tibble, progress, ggplot2,
Suggests: dbplyr, testthat (>= 2.1.0), knitr, rmarkdown, nycflights13,

person("Max", "Kuhn", email = "max@rstudio.com", role = c("aut", "cre"))

person("Max", "Kuhn", , "max@rstudio.com", role = c("aut", "cre"))
Description: Uses 'dplyr' and 'tidyeval' to fit statistical models inside
the database. It currently supports KMeans and linear regression
Imports: dplyr(>= 0.7), ggplot2, progress, purrr, rlang, tibble,
Suggests: covr, DBI, dbplyr, knitr, methods, nycflights13, rmarkdown,

Title: Provide Tools to Register Models for Use in 'tidymodels'
Description: An developer focused, low dependency package in 'tidymodels' that
provides functions to register how models are to be used. Functions to
register models are complimented with accessor functions to retrieve
registered model information to aid in model fitting and error handling.
Packaged: 2022-10-15 19:07:03 UTC; emilhvitfeldt

Title: Model-free estimation of a psychometric function
Author: Ivan Marin-Franch <imarinfr@indiana.edu>, Kamila Zychaluk <kamila.zychaluk@liverpool.ac.uk>, and David H. Foster <d.h.foster@manchester.ac.uk>
Maintainer: Kamila Zychaluk <kamila.zychaluk@liverpool.ac.uk>, David H.
Description: Local linear estimation of psychometric functions. Provides functions for nonparametric estimation of a psychometric function and for estimation of a derived threshold and slope, and their standard deviations and confidence intervals