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JOPS (version 0.1.19)

Practical Smoothing with P-Splines

Description

Functions and data to reproduce all plots in the book "Practical Smoothing. The Joys of P-splines" by Paul H.C. Eilers and Brian D. Marx (2021, ISBN:978-1108482950).

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Version

Install

install.packages('JOPS')

Monthly Downloads

346

Version

0.1.19

License

GPL-2 | GPL-3

Maintainer

Paul Eilers

Last Published

September 5th, 2023

Functions in JOPS (0.1.19)

Custom theme for ggplot

Bayesian density estimation

Compute a B-spline basis matrix

Suicide Data Set

Two-dimensional P-spline smoothing

Translated number vector to bin index.

Brightness of a variable star.

Sugar Processing Data

Profile of a sanded piece of wood.

Create a matrix of counts.

Fit amplitude coeffcients in the bundle model for expectiles

Compute a circular B-spline basis matrix

Compute a 2D histogram

Compute a second order circular differencing matrix

Spinal bone relative mineral density

Deviance calculation for GLM P-spline fitting.

Smooth a 2D histogram

plot.ps2dsignal

Plotting function for ps2DSignal

Plotting function for psNormal, psPoisson, psBinomial

Fit asymmetry parameters in the expectile bundle model

Plotting function for psSignal

plot.psvcsignal

Plotting function for psVCSignal

Clone a B-spline basis for new x

plot.ps2dnormal

Plotting function for ps2DNormal

Plotting function for ps2DGLM

predict.ps2dsignal

Predict function for ps2DSignal

Predict function for psNormal, psBinomial, psPoisson

predict.ps2dnormal

Predict function for ps2DNormal

predict.ps2dglm

Predict function for ps2DGLM

An X-ray diffractogram.

Inverse link function, used for GLM fitting.

Plotting function for sim_vcpsr

predict.simvcpsr

Predict function for sim_vcpsr

Plotting function for sim_psr

Predict function for sim_psr

Ovarian cancer data

Fit a composite link model

predict.pssignal

Predict function for psSignal

predict.psvcsignal

Predict function for psVCSignal

Smoothing scattered binomial data using P-splines.

Smooth signal (multivariate calibration) regression using P-splines.

Two-dimensional smoothing of scattered normal or non-normal (GLM) responses using tensor product P-splines.

Smoothing scattered (normal) data using P-splines.

Derivative for a P-spline fit of scattered (normal) data.

Two-dimensional penalized signal regression using P-splines.

Smoothing scattered Poisson data using P-splines.

ps2D_PartialDeriv

Partial derivative two-dimensional smoothing scattered (normal) data using P-splines.

Two-dimensional smoothing scattered (normal) data using P-splines.

pspline2d_checker

P-spline 2D tensor product checking algorithm for the GLM.

Observations on the widths of red blood cell distributions (RDW).

pspline_checker

P-spline checking algorithm for the GLM.

P-spline fitting algorithm for the GLM.

Open a graphics window.

Prepare graphics layout for multiple panels

Varying-coefficient single-index signal regression using tensor P-splines.

Single-Index signal regression using P-splines

Compute the row tensor product of two matrices

Varying-coefficient penalized signal regression using P-splines.

Save a plot as a PDF file.

Compute a sparse B-spline basis on evenly spaced knots

Compute a truncated power function.

Themeing functions used to unify ggplot features

Deaths in Greece in 1960.

Custom color ramp.

Joys of P-Splines

Simulation of CGH data

Environmental complaints from the Rijnomond area of The Netherlands

Prices of hard disk drives

Prevalence of Hepatitis among a sample of Bulgarian males.

A section of an ECG (electrocardiogram)

Chromosome G519C18 data