The suite of functions includes:

`dredge`

performs automated model selection by generating
subsets of the supplied ‘global’ model and optional
choices of other model properties (such as different link functions).
The set of models can be generated with ‘all possible’
combinations or tailored according to specified conditions.

`model.sel`

creates a model selection table from
selected models.

`model.avg`

calculates model-averaged parameters,
along with standard errors and confidence intervals.
The `predict`

method
produces model-averaged predictions.

`AICc`

calculates the second-order Akaike information
criterion. Some other criteria are provided, see below.

`stdize`

, `stdizeFit`

, `std.coef`

,
`partial.sd`

can be used to standardise data and
model coefficients by standard deviation or partial standard deviation.

For a complete list of functions, use `library(help = "MuMIn")`

.

By default, AIC\(_{c}\) is used to rank models and obtain model
weights, although any information criterion can be used. At least the
following are currently implemented in R:
`AIC`

and `BIC`

in package stats, and
`QAIC`

, `QAICc`

, `ICOMP`

,
`CAICF`

, and Mallows' Cp in MuMIn. There is also a
`DIC`

extractor for MCMC models and a `QIC`

for
GEE.

Many common modelling functions in R are supported. For a complete list,
see the list of supported models.

In addition to “regular” information criteria, model averaging can be
performed using various types of model weighting algorithms:
Bates-Granger,
bootstrapped,
cos-squared,
jackknife,
stacking, or
ARM.
These weighting functions are mainly applicable to `glm`

s.