Package: npreg 1.1.0

Nathaniel E. Helwig

npreg: 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 (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.

Authors:Nathaniel E. Helwig <helwig@umn.edu>

npreg_1.1.0.tar.gz
npreg_1.1.0.tar.gz(r-4.5-noble)npreg_1.1.0.tar.gz(r-4.4-noble)
npreg_1.1.0.tgz(r-4.4-emscripten)npreg_1.1.0.tgz(r-4.3-emscripten)
npreg.pdf |npreg.html✨
npreg/json (API)

# Install 'npreg' in R:
install.packages('npreg', repos = 'https://cloud.r-project.org')

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 577 downloads 1 mentions 150 exports 0 dependencies

Last updated 1 years agofrom:2338761f4c. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 31 2025
R-4.5-linuxOKMar 31 2025
R-4.4-linuxOKMar 31 2025

Exports:basis_nombasis_ordbasis_polybasis_sphbasis_tpsbasis.nombasis.ordbasis.polybasis.sphbasis.tpsbin.samplebootboot.gsmboot.smboot.ssbuild_depebuild_depe2build_rkhscharkrocheck_controlcheck_familycheck_knotcheck_typecheck_type2coef.gsmcoef.smcoef.sscolor.legendcooks.distance.gsmcooks.distance.smcooks.distance.sscov.ratiocubkern0cubkern1cubkern2deviance.gsmdeviance.smdeviance.ssdf2lambdadfbeta.gsmdfbeta.smdfbeta.ssdfbetas.gsmdfbetas.smdfbetas.ssdiagnostic.plotsfamily.gsmfit_gsmfit_smfit_ssifitted.gsmfitted.smfitted.ssgsmhatvalues.gsmhatvalues.smhatvalues.ssinfluence.gsminfluence.sminfluence.ssknot1samplinkern0linkern1linkinvdmodel.matrix.gsmmodel.matrix.smmodel.matrix.ssmsqrtNegBinnpregStartupMessagenumber2colorordkernpenalty_nompenalty_ordpenalty_polypenalty_sphpenalty_tpspenalty.nompenalty.ordpenalty.polypenalty.sphpenalty.tpsplot.boot.ssplot.gsmplot.smplot.ssplotcipred_depepred_rkhspredict.gsmpredict.smpredict.ssprint.boot.gsmprint.boot.smprint.boot.ssprint.gsmprint.smprint.ssprint.summary.gsmprint.summary.smprint.summary.sspsolveq2funq4funq6funquikern0quikern1quikern2residuals.gsmresiduals.smresiduals.ssrexpfamrowkrorstandard.gsmrstandard.smrstandard.ssrstudent.gsmrstudent.smrstudent.sssmsmooth.influencesmooth.influence.measuressssummary.gsmsummary.smsummary.sstheta.gradtheta.infotheta.mletune.acv.sstune.aic.sstune.deep.gsmtune.deep.smtune.gacv.sstune.gcv.sstune.gsmtune.mle.sstune.ocv.ssvarimpvarinfvcov.gsmvcov.smvcov.ssweights.gsmweights.smweights.sswtd.meanwtd.quantilewtd.sdwtd.var

Dependencies:

Citation

To cite package ‘npreg’ in publications use:

Helwig NE (2024). npreg: Nonparametric Regression via Smoothing Splines. R package version 1.1.0, https://CRAN.R-project.org/package=npreg.

ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.

Corresponding BibTeX entry: 

  @Manual{,
    title = {npreg: Nonparametric Regression via Smoothing Splines},
    author = {Nathaniel E. Helwig},
    year = {2024},
    note = {R package version 1.1.0},
    url = {https://CRAN.R-project.org/package=npreg},
  }

Readme and manuals

Help Manual

Help pageTopics
Bin Sample a Vector, Matrix, or Data Framebin.sample
Bootstrap a Fit Smoothboot boot.gsm boot.sm boot.ss
Extract Smooth Model Coefficientscoef.gsm coef.sm coef.ss
Adds Color Legend to Plot Margincolor.legend
Smooth Model Deviancedeviance.gsm deviance.sm deviance.ss
Plot Nonparametric Regression Diagnosticsdiagnostic.plots
Extract Smooth Model Fitted Valuesfitted.gsm fitted.sm fitted.ss
Fit a Generalized Smooth Modelfamily.gsm gsm
Construct Design Matrix for Fit Modelmodel.matrix.gsm model.matrix.sm model.matrix.ss
Matrix (Inverse?) Square Rootmsqrt
Family Function for Negative BinomialNegBin
Nominal Smoothing Spline Basis and Penaltybasis.nom basis_nom nominal penalty.nom penalty_nom
Map Numbers to Colorsnumber2color
Ordinal Smoothing Spline Basis and Penaltybasis.ord basis_ord ordinal penalty.ord penalty_ord
Plot Effects for Generalized Smooth Model Fitsplot.gsm
Plot Effects for Smooth Model Fitsplot.sm
Plot method for Smoothing Spline Fit and Bootstrapplot.boot.ss plot.ss
Generic X-Y Plotting with Confidence Intervalsplotci
Polynomial Smoothing Spline Basis and Penaltybasis.poly basis_poly penalty.poly penalty_poly polynomial
Predict method for Generalized Smooth Model Fitspredict.gsm
Predict method for Smooth Model Fitspredict.sm
Predict method for Smoothing Spline Fitspredict.ss
Pseudo-Solve a System of Equationspsolve
Extract Model Residualsresiduals.gsm residuals.sm residuals.ss
Fit a Smooth Modelsm
Nonparametric Regression Diagnosticsinfluence.gsm influence.sm influence.ss smooth.influence
Nonparametric Regression Deletion Diagnosticscooks.distance.gsm cooks.distance.sm cooks.distance.ss cov.ratio dfbeta.gsm dfbeta.sm dfbeta.ss dfbetas.gsm dfbetas.sm dfbetas.ss hatvalues.gsm hatvalues.sm hatvalues.ss rstandard.gsm rstandard.sm rstandard.ss rstudent.gsm rstudent.sm rstudent.ss smooth.influence.measures
Spherical Spline Basis and Penaltybasis.sph basis_sph penalty.sph penalty_sph spherical
Fit a Smoothing Spliness
Startup Message for npregnpregStartupMessage StartupMessage
Summary methods for Fit Modelsprint.summary.gsm print.summary.sm print.summary.ss summary summary.gsm summary.sm summary.ss
MLE of Theta for Negative Binomialtheta.mle
Thin Plate Spline Basis and Penaltybasis.tps basis_tps penalty.tps penalty_tps thinplate
Variable Importance Indicesvarimp
Variance Inflation Factorsvarinf
Calculate Variance-Covariance Matrix for a Fitted Smooth Modelvcov.gsm vcov.sm vcov.ss
Extract Smooth Model Weightsweights.gsm weights.sm weights.ss
Weighted Arithmetic Meanwtd.mean
Weighted Quantileswtd.quantile
Weighted Variance and Standard Deviationwtd.sd wtd.var