modelObj-package: A Model Object Framework for Regression Analysis
In modelObj: A Model Object Framework for Regression Analysis
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.
Details
Package: modelObj
Type: Package
Version: 1.0
Date: 2015-06-10
License: GPL-2
Depends: methods
Often, new statistical methods are developed on the framework of
traditional regression or classification methods. To simplify the
creation of new R implementations of these methods,
researchers and software developers
often make choices regarding the types of models that can be
used by the user; hard-coding the regression method into the library and
limiting or eliminating the ability of the user to modify regression
control parameters. These choices artificially limit the general application of
new methods. In addition, if a new method requires multiple models,
a developer is
often forced to artificially break the method into multiple function calls, each
for a specific regression/classification step, or is forced to provide a
cumbersome and/or confusing interface for the user. modelObj is
an R package developed to facilitate the use of existing and
future R regression and classification libraries that simplifies
the development of general, non-model-specific implementations of new
statistical methods.
Author(s)
Shannon T. Holloway
Maintainer: Shannon T. Holloway <sthollow@ncsu.edu>
modelObj documentation built on May 2, 2019, 5:20 p.m.
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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.
Details
| Package: | modelObj |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2015-06-10 |
| License: | GPL-2 |
| Depends: | methods |
Often, new statistical methods are developed on the framework of traditional regression or classification methods. To simplify the creation of new R implementations of these methods, researchers and software developers often make choices regarding the types of models that can be used by the user; hard-coding the regression method into the library and limiting or eliminating the ability of the user to modify regression control parameters. These choices artificially limit the general application of new methods. In addition, if a new method requires multiple models, a developer is often forced to artificially break the method into multiple function calls, each for a specific regression/classification step, or is forced to provide a cumbersome and/or confusing interface for the user. modelObj is an R package developed to facilitate the use of existing and future R regression and classification libraries that simplifies the development of general, non-model-specific implementations of new statistical methods.
Author(s)
Shannon T. Holloway
Maintainer: Shannon T. Holloway <sthollow@ncsu.edu>
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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