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rmargint
View on CRAN: Click
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Download and install rmargint package within the R console
Install from CRAN:
install.packages("rmargint")
Install from Github:
library("remotes")
install_github("cran/rmargint")
Install by package version:
library("remotes")
install_version("rmargint", "2.0.3")
Attach the package and use:
library("rmargint")
Maintained by
Alejandra Martinez
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-06-28
Latest Update: 2023-10-23
Description:
Three robust marginal integration procedures for additive models based on local
polynomial kernel smoothers. As a preliminary estimator of the multivariate
function for the marginal integration procedure, a first approach uses local
constant M-estimators, a second one uses local polynomials of order 1 over all the
components of covariates, and the third one uses M-estimators based on local
polynomials but only in the direction of interest. For this last approach,
estimators of the derivatives of the additive functions can be obtained. All three
procedures can compute predictions for points outside the training set if desired.
See Boente and Martinez (2017) for details.
How to cite:
Alejandra Martinez (2019). rmargint: Robust Marginal Integration Procedures. R package version 2.0.3, https://cran.r-project.org/web/packages/rmargint. Accessed 21 Dec. 2024.
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imports, suggests or enhances
Complete documentation for rmargint
Functions, R codes and Examples using
the rmargint R package
Some associated functions: deviance.margint . fitted.values.margint . formula.margint . k.epan . kernel10 . kernel4 . kernel6 . kernel8 . margint.cl . margint.rob . my.norm.2 . plot.margint . predict.margint . print.margint . psi.huber . psi.tukey . residuals.margint . rmargint-package . summary.margint .
Some associated R codes: rmargint-fn.R . Full rmargint package functions and examples
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