Add to ORKGDescription Automated model selection and model-averaging. Provides a wrapper for glm and other functions, automatically generating all possible models (under constraints set by the user) with the specified response and explanatory variables, and finding the best models in terms of some Information Criterion (AIC, AICc or BIC). Can handle very large numbers of candidate models. Features a Genetic Algorithm to find the best models when an exhaustive screening of the candidates is not feasible
Plus signs (+) represent the inclusion of covariate use as smooth terms in the global GAM models. Ye...
Model selection methods provide a way to select one model among a set of models in a statistically v...
Depends R (> = 2.2.1), gamlss, survival Description This is an add on package to GAMLSS. It allow...
We introduce glmulti, an R package for automated model selection and multi-model inference with glm ...
Description This package performs model selection based on non-local priors, includ-ing MOM, eMOM an...
Bayesian Generalized Nonlinear Models (BGNLM) offer a flexible alternative to GLM while still provid...
Description Implements structural estimators to correct for the sample selection bias from observed ...
The function bestglm selects the best subset of inputs for the glm family. The selec-tion methods av...
<p>Models tested using the GLM procedure and the associated Akaike Information Criterion (AIC) obtai...
This paper describes the GLMSELECT procedure, a new procedure in SAS/STAT software that performs mod...
The main functions mask call to mixed models by least squares or REML by adding r() func-tion to for...
This paper derives several model selection criteria for generalized linear models (GLMs) following t...
AIC, Genetic Algorithm, Graphical model, Log-linear model, Model selection, Undirected graph,
Selecting the optimal model from a set of competing models is an essential task in statistics. The f...
To compute norms from reference group test scores, continuous norming is preferred over traditional ...
Plus signs (+) represent the inclusion of covariate use as smooth terms in the global GAM models. Ye...
Model selection methods provide a way to select one model among a set of models in a statistically v...
Depends R (> = 2.2.1), gamlss, survival Description This is an add on package to GAMLSS. It allow...
We introduce glmulti, an R package for automated model selection and multi-model inference with glm ...
Description This package performs model selection based on non-local priors, includ-ing MOM, eMOM an...
Bayesian Generalized Nonlinear Models (BGNLM) offer a flexible alternative to GLM while still provid...
Description Implements structural estimators to correct for the sample selection bias from observed ...
The function bestglm selects the best subset of inputs for the glm family. The selec-tion methods av...
<p>Models tested using the GLM procedure and the associated Akaike Information Criterion (AIC) obtai...
This paper describes the GLMSELECT procedure, a new procedure in SAS/STAT software that performs mod...
The main functions mask call to mixed models by least squares or REML by adding r() func-tion to for...
This paper derives several model selection criteria for generalized linear models (GLMs) following t...
AIC, Genetic Algorithm, Graphical model, Log-linear model, Model selection, Undirected graph,
Selecting the optimal model from a set of competing models is an essential task in statistics. The f...
To compute norms from reference group test scores, continuous norming is preferred over traditional ...
Plus signs (+) represent the inclusion of covariate use as smooth terms in the global GAM models. Ye...
Model selection methods provide a way to select one model among a set of models in a statistically v...
Depends R (> = 2.2.1), gamlss, survival Description This is an add on package to GAMLSS. It allow...