PLRModels: Statistical inference in partial linear regression models

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This package provides statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.

Author
German Aneiros Perez and Ana Lopez Cheda
Date of publication
2014-01-01 21:37:58
Maintainer
German Aneiros Perez <ganeiros@udc.es>
License
GPL
Version
1.1

View on CRAN

Man pages

best.arima
Best Arima model according some information criterion
Epanechnikov
The Epanechnikov kernel
gaussian
The gaussian kernel
np.ancova
Nonparametric analysis of covariance
np.cv
Cross-validation bandwidth selection in nonparametric...
np.est
Nonparametric estimate of the regression function
np.gcv
Generalized cross-validation bandwidth selection in...
np.gof
Goodness-of-Fit tests in nonparametric regression models
par.ancova
Parametric analysis of covariance (based on linear models)
par.ci
Confidence intervals estimation in linear regression models
par.est
Estimation in linear regression models
par.gof
Goodness-of-Fit tests in linear regression models
plrm.ancova
Semiparametric analysis of covariance (based on PLR models)
plrm.beta
Semiparametric estimate for the parametric component of the...
plrm.ci
Confidence intervals estimation in partial linear regression...
plrm.cv
Cross-validation bandwidth selection in PLR models
plrm.est
Semiparametric estimates for the unknown components of the...
plrm.gcv
Generalized cross-validation bandwidth selection in PLR...
plrm.gof
Goodness-of-Fit tests in PLR models
PLRModels
Statistical inference in partial linear regression models
quadratic
The quadratic kernel
symsolve
Solution of a system of linear equations
triweight
The triweight kernel
uniform
The uniform kernel
var.cov.matrix
Estimated variance-covariance matrix from time series
var.cov.sum
Estimated sum of autocovariances from time series

Files in this package

PLRModels
PLRModels/NAMESPACE
PLRModels/data
PLRModels/data/barnacles2.txt.gz
PLRModels/data/barnacles1.txt.gz
PLRModels/R
PLRModels/R/par.ancova.R
PLRModels/R/plrm.gcv.R
PLRModels/R/np.est.R
PLRModels/R/par.gof.R
PLRModels/R/var.cov.sum.R
PLRModels/R/plrm.cv.R
PLRModels/R/var.cov.matrix.R
PLRModels/R/plrm.ancova.R
PLRModels/R/plrm.beta.R
PLRModels/R/par.ci.R
PLRModels/R/np.cv.R
PLRModels/R/best.arima.R
PLRModels/R/plrm.est.R
PLRModels/R/np.gof.R
PLRModels/R/np.gcv.R
PLRModels/R/kind.of.kernel.R
PLRModels/R/symsolve.R
PLRModels/R/plrm.ci.R
PLRModels/R/par.est.R
PLRModels/R/plrm.gof.R
PLRModels/R/np.ancova.R
PLRModels/MD5
PLRModels/DESCRIPTION
PLRModels/man
PLRModels/man/np.cv.Rd
PLRModels/man/np.gof.Rd
PLRModels/man/plrm.gcv.Rd
PLRModels/man/quadratic.Rd
PLRModels/man/par.ancova.Rd
PLRModels/man/barnacles1.rd
PLRModels/man/plrm.cv.Rd
PLRModels/man/gaussian.Rd
PLRModels/man/triweight.Rd
PLRModels/man/barnacles2.rd
PLRModels/man/best.arima.Rd
PLRModels/man/plrm.ci.Rd
PLRModels/man/plrm.est.Rd
PLRModels/man/np.gcv.Rd
PLRModels/man/plrm.beta.Rd
PLRModels/man/var.cov.matrix.Rd
PLRModels/man/PLRModels.Rd
PLRModels/man/plrm.ancova.Rd
PLRModels/man/uniform.Rd
PLRModels/man/par.gof.Rd
PLRModels/man/var.cov.sum.Rd
PLRModels/man/Epanechnikov.Rd
PLRModels/man/np.ancova.Rd
PLRModels/man/par.est.Rd
PLRModels/man/np.est.Rd
PLRModels/man/symsolve.Rd
PLRModels/man/plrm.gof.Rd
PLRModels/man/par.ci.Rd