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modelObj
View on CRAN: Click
here
Download and install modelObj package within the R console
Install from CRAN:
install.packages("modelObj")
Install from Github:
library("remotes")
install_github("cran/modelObj") Install by package version:
library("remotes")
install_version("modelObj", "4.3") Attach the package and use:
library("modelObj")
Maintained by
Shannon T. Holloway
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-06-10
Latest Update: 2025-05-03
Description:
A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions. In this way, a user can easily go from linear to non-linear models within the same package.
How to cite:
Shannon T. Holloway (2015). modelObj: A Model Object Framework for Regression Analysis. R package version 4.3, https://cran.r-project.org/web/packages/modelObj. Accessed 23 Jul. 2026.
Previous versions and publish date:
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Complete documentation for modelObj
Functions, R codes and Examples using
the modelObj R package
Some associated functions: buildModelObj . fit . fitObject . model . modelObj . modelObjFit-class . predict . predictor . predictorArgs . solver . solverArgs .
Some associated R codes: buildModelObj.R . methodObj.R . methodObjPredict.R . methodObjSolver.R . methodObjSolverFormula.R . methodObjSolverXY.R . modelObj.R . modelObjFit.R . modelObjFormula.R . modelObjXY.R . warnMsg.R . Full modelObj package functions and examples
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