Type: Package
Title: S-Type Estimators
Version: 0.1.0
Description: Implements the S-type estimators, novel robust estimators for general linear regression models, addressing challenges such as outlier contamination and leverage points. This package introduces robust regression techniques to provide a robust alternative to classical methods and includes diagnostic tools for assessing model fit and performance. The methodology is based on the study, "Comparison of the Robust Methods in the General Linear Regression Model" by Sazak and Mutlu (2023). This package is designed for statisticians and applied researchers seeking advanced tools for robust regression analysis.
License: MIT + file LICENSE
Encoding: UTF-8
Depends: R (≥ 4.0.0)
Imports: datasets, stats
Suggests: knitr, rmarkdown
URL: https://github.com/filizkrdg/S-type.est
BugReports: https://github.com/filizkrdg/S-type.est/issues
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-08-25 13:04:52 UTC; pc
Author: Hakan Savas Sazak ORCID iD [aut], Filiz Karadag ORCID iD [cre], Olgun Aydin ORCID iD [aut]
Maintainer: Filiz Karadag <filiz.karadag@ege.edu.tr>
Repository: CRAN
Date/Publication: 2025-08-29 17:50:07 UTC

Fit a regression model using the S-type estimators.

Description

This function fits a regression model using the S-type estimators.

Usage

regstype(y, x)

Arguments

y

Dependent variables (Dataframe, vector).

x

Explanatory variables (Dataframe, matrix).

Value

A list containing the model coefficients and diagnostics.

Examples

library(datasets)
data(airquality)
str(airquality)
cleanairquality=na.omit(airquality)
Y1=cleanairquality$Ozone
X1=cleanairquality$Temp
X2=cleanairquality$Wind
X3=cleanairquality$Solar.R
x=data.frame("X1"=X1,"X2"=X2,"X3"=X3)
y=data.frame("Y"=Y1)
regstype(y,x)

Weighted regression analysis.

Description

This function performs weighted regression analysis.

Usage

regweighteds(y, x, W)

Arguments

y

Dependent variables (Dataframe, vector)

x

Explanatory variables (Dataframe, matrix)

W

A numeric vector of weights.

Value

A list containing the regression model results.

Examples

library(datasets)
data(airquality)
str(airquality)
cleanairquality=na.omit(airquality)
Y1=cleanairquality$Ozone
X1=cleanairquality$Temp
X2=cleanairquality$Wind
X3=cleanairquality$Solar.R
 x=data.frame("X1"=X1,"X2"=X2,"X3"=X3)
 y=data.frame("Y"=Y1)
 W=runif(111, min = 0, max = 1)
regweighteds(y,x,W)