Nonparametric Regression via Smoothing Splines
Extract Smooth Model Coefficients
Bin Sample a Vector, Matrix, or Data Frame
Bootstrap a Fit Smooth
Adds Color Legend to Plot Margin
Smooth Model Deviance
Plot Nonparametric Regression Diagnostics
Extract Smooth Model Fitted Values
Fit a Generalized Smooth Model
Construct Design Matrix for Fit Model
Matrix (Inverse?) Square Root
Family Function for Negative Binomial
Nominal Smoothing Spline Basis and Penalty
Internal Functions for "npreg"
Map Numbers to Colors
Ordinal Smoothing Spline Basis and Penalty
Plot Effects for Generalized Smooth Model Fits
Plot Effects for Smooth Model Fits
Plot method for Smoothing Spline Fit and Bootstrap
Generic X-Y Plotting with Confidence Intervals
Polynomial Smoothing Spline Basis and Penalty
Predict method for Generalized Smooth Model Fits
Predict method for Smooth Model Fits
Predict method for Smoothing Spline Fits
Pseudo-Solve a System of Equations
Extract Model Residuals
Fit a Smooth Model
Nonparametric Regression Deletion Diagnostics
Nonparametric Regression Diagnostics
Spherical Spline Basis and Penalty
Fit a Smoothing Spline
Startup Message for npreg
Summary methods for Fit Models
MLE of Theta for Negative Binomial
Thin Plate Spline Basis and Penalty
Variable Importance Indices
Variance Inflation Factors
Calculate Variance-Covariance Matrix for a Fitted Smooth Model
Extract Smooth Model Weights
Weighted Arithmetic Mean
Weighted Quantiles
Weighted Variance and Standard Deviation
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 (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.