anytime, futile.logger, htmlTable, lubridate, Matrix, plotrix,
readxl, Rlabkey, rlang, shiny, stringi, utils, WriteXLS
covr, dplyr, ggplot2, grid, kableExtra, knitr, markdown,
pkgdown, png, rmarkdown, roxygen2 (>= 7.0.0), shinyBS,
shinyWidgets, testthat

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors, interactions between smoothers of mixed types, and eight different methods for smoothing parameter selection.

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors, interactions between smoothers of mixed types, and eight different methods for smoothing parameter selection.

Nonparametric Regression via Smoothing Splines

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors, interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for prediction and inference.

Nonparametric Estimation of the Derivatives of a Regression
Function
c(person("Olga", "Savchuk", role = c("aut", "cre"), email = "olga.y.savchuk@gmail.com"),
person("Alex", "Volinsky", role = "aut", email = "volinsky@usf.edu"))
Estimating the first and second derivatives of a regression function by the method of Wang and Lin (2015) <http://www.jmlr.org/papers/v16/wang15b.html>.
Olga Savchuk [aut, cre],
Alex Volinsky [aut]

Nonparametric Estimation of Regression Models with
Factor-by-Curve Interactions
A method for obtain nonparametric estimates of regression models
with or without factor-by-curve interactions using local polynomial
kernel smoothers. Additionally, a parametric model (allometric model)
can be estimated.