Package: AICcmodavg
Type: Package
Title: Model selection and multimodel inference based on (Q)AIC(c)
Version: 1.13
Date: 2010-01-10
Author: Marc J. Mazerolle <marc.mazerolle@uqat.ca>.  Special thanks to
T. Ergon for the original idea of storing candidate models in a
list.
Maintainer: Marc J. Mazerolle <marc.mazerolle@uqat.ca>
Suggests: lme4, MASS, nlme, nnet
Description: This package includes functions to create model selection
tables based on Akaike's information criterion (AIC) and the
second-order AIC (AICc), as well as their quasi-likelihood
counterparts (QAIC, QAICc).  Tables are printed with delta AIC
and Akaike weights.  The package also includes functions to
conduct model averaging (multimodel inference) for a given
parameter of interest or predicted values.  Other handy
functions enable the computation of relative variable
importance, evidence ratios, and confidence sets for the best
model.  The present version works with linear models ('lm'
class), generalized linear models ('glm' class), linear models
fit by generalized least squares ('gls' class), linear mixed
models ('lme' class), generalized linear mixed models ('mer'
class), multinomial and ordinal logistic regressions
('multinom' and 'polr' classes).
License: GPL (>= 2 )
LazyLoad: yes
Packaged: 2011-01-10 20:08:24 UTC; mazerolm
Repository: CRAN
Date/Publication: 2011-01-11 06:41:25

Package: AICcmodavg
Type: Package
Title: Model selection and multimodel inference based on (Q)AIC(c)
Version: 1.14
Date: 2011-03-11
Author: Marc J. Mazerolle <marc.mazerolle@uqat.ca>.  Special thanks to
T. Ergon for the original idea of storing candidate models in a
list.
Maintainer: Marc J. Mazerolle <marc.mazerolle@uqat.ca>
Suggests: lme4, MASS, nlme, nnet
Description: This package includes functions to create model selection
tables based on Akaike's information criterion (AIC) and the
second-order AIC (AICc), as well as their quasi-likelihood
counterparts (QAIC, QAICc).  Tables are printed with delta AIC
and Akaike weights.  The package also includes functions to
conduct model averaging (multimodel inference) for a given
parameter of interest or predicted values.  Other handy
functions enable the computation of relative variable
importance, evidence ratios, and confidence sets for the best
model.  The present version works with linear models ('lm'
class), generalized linear models ('glm' class), linear models
fit by generalized least squares ('gls' class), linear mixed
models ('lme' class), generalized linear mixed models ('mer'
class), multinomial and ordinal logistic regressions
('multinom' and 'polr' classes).
License: GPL (>= 2 )
LazyLoad: yes
Packaged: 2011-03-11 15:19:37 UTC; mazerolm
Repository: CRAN
Date/Publication: 2011-03-11 16:07:50