glmmTMB v0.2.3


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Generalized Linear Mixed Models using Template Model Builder

Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.

Functions in glmmTMB

Name Description
simulate.glmmTMB Simulate from a glmmTMB fitted model
getME.glmmTMB Extract or Get Generalize Components from a Fitted Mixed Effects Model
nbinom2 Family functions for glmmTMB
getGrpVar Get Grouping Variable
numFactor Factor with numeric interpretable levels.
print.VarCorr.glmmTMB Printing The Variance and Correlation Parameters of a glmmTMB
predict.glmmTMB prediction
findReTrmClasses list of specials -- taken from enum.R
fixef Extract fixed-effects estimates
inForm test formula: does it contain a particular element?
formatVC Format the 'VarCorr' Matrix of Random Effects
profile.glmmTMB Compute likelihood profiles for a fitted model
ranef.glmmTMB Extract Random Effects
addForm Combine right-hand sides of an arbitrary number of formulas
tmbroot Compute likelihood profile confidence intervals of a TMB object by root-finding (generalized from TMB::tmbprofile)
getCapabilities List model options that glmmTMB knows about
formula.glmmTMB Extract the formula of a glmmTMB object
.collectDuplicates collapse duplicated observations
vcov.glmmTMB Calculate Variance-Covariance Matrix for a Fitted glmmTMB model
getReStruc Calculate random effect structure Calculates number of random effects, number of parameters, blocksize and number of blocks. Mostly for internal use.
get_cor translate vector of correlation parameters to correlation values, following the definition at if \(L\) is the lower-triangular matrix with 1 on the diagonal and the correlation parameters in the lower triangle, then the correlation matrix is defined as \(\Sigma = D^{-1/2} L L^\top D^{-1/2}\), where \(D = \textrm{diag}(L L^\top)\). For a single correlation parameter \(\theta_0\), this works out to \(\rho = \theta_0/\sqrt{1+\theta_0^2}\).
glmmTMB Fit models with TMB
residuals.glmmTMB Compute residuals for a glmmTMB object
glmmTMBControl Control parameters for glmmTMB optimization
getXReTrms Create X and random effect terms from formula
sigma.glmmTMB Extract residual standard deviation or dispersion parameter
mkTMBStruc Extract info from formulas, reTrms, etc., format for TMB
confint.glmmTMB Calculate confidence intervals
Owls Begging by Owl Nestlings
Salamanders Repeated counts of salamanders in streams
Anova.glmmTMB Downstream methods for glmmTMB objects
epil2 Seizure Counts for Epileptics - Extended
expandAllGrpVar expand interactions/combinations of grouping variables
VarCorr.glmmTMB Extract variance and correlation components
fbx (f)ind (b)ars e(x)tended: recursive
isLMM.glmmTMB support methods for parametric bootstrapping
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License AGPL-3
LinkingTo TMB, RcppEigen
VignetteBuilder knitr
LazyData TRUE
RoxygenNote 6.1.1
NeedsCompilation yes
Encoding UTF-8
Packaged 2019-01-11 15:28:42 UTC; molliebrooks
Repository CRAN
Date/Publication 2019-01-11 16:30:03 UTC

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