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1.46.0
1.43.17
1.43.15
1.43.14
1.43.10
1.43.6
1.42.1
1.40.4
1.40.0
1.15.6
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1.14.0
1.13.4
1.12.1
1.10.5
1.10.0
1.9.13
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1.9.5
1.9.0
1.8.0
1.7.12
1.7.11
1.7.7
1.7.2
1.7.0
1.6.6
1.6.5
1.6.4
1.6.1
1.6.0
1.5.2
1.5.0
1.3.10
1.3.6
1.0.0
0.13.21
0.13.17
0.13.14
0.13.9
0.13.8
0.13.3
0.12.2
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Install
install.packages('MuMIn')
Monthly Downloads
19,461
Version
1.46.0
License
GPL-2
Maintainer
Kamil Barto
Last Published
February 24th, 2022
Functions in MuMIn (1.46.0)
Search functions
AICc
Second-order Akaike Information Criterion
Information criteria
Various information criteria
coefplot
Plot model coefficients
BGWeights
Bates-Granger minimal variance model weights
bootWeights
Bootstrap model weights
QAIC
Quasi AIC or AICc
MuMIn-package
Multi-model inference
Weights
Akaike weights
QIC
QIC and quasi-Likelihood for GEE
arm.glm
Adaptive Regression by Mixing
loo
Leave-one-out cross-validation
Cement
Cement hardening data
jackknifeWeights
Jackknifed model weights
plot.model.selection
Visualize model selection table
get.models
Retrieve models from selection table
predict.averaging
Predict method for averaged models
Beetle
Flour beetle mortality data
Formula manipulation
Manipulate model formulas
merge.model.selection
Combine model selection tables
Model utilities
Model utility functions
dredge
Automated model selection
model.selection.object
Description of Model Selection Objects
r.squaredLR
Likelihood-ratio based pseudo-R-squared
cos2Weights
Cos-squared model weights
model.sel
model selection table
GPA
Grade Point Average data
stdize
Standardize data
r.squaredGLMM
Pseudo-R-squared for Generalized Mixed-Effect models
nested
Identify nested models
updateable
Make a function return updateable result
model.avg
Model averaging
std.coef
Standardized model coefficients
stackingWeights
Stacking model weights
sw
Per-variable sum of model weights
MuMIn-models
List of supported models
subset.model.selection
Subsetting model selection table
par.avg
Parameter averaging
exprApply
Apply a function to calls inside an expression
pdredge
Automated model selection using parallel computation