models. Currently supported are all 'merMod' classes from 'lme4' ('lmerMod', 'glmerMod'),
'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model
classes. Alternatively, externally created simulations, e.g. posterior predictive simulations
from Bayesian software such as 'JAGS', 'STAN', or 'Bugs' can be processed as well.
The resulting residuals are standardized to values between 0 and 1 and can be interpreted
as intuitively as residuals from a linear regression. The package also provides a number of
plot and test functions for typical model misspecification problem, such as
over/underdispersion, zero-inflation, and spatial and temporal autocorrelation.

from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well.
over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.

readily interpretable scaled (quantile) residuals for fitted generalized linear mixed
models. Currently supported are generalized linear mixed models from 'lme4'
(classes 'lmerMod', 'glmerMod'), generalized additive models ('gam' from 'mgcv'),
plot and test functions for typical model misspecification problems, such as
sfsmisc, MASS, doParallel, foreach, lme4, mgcv

Authors@R: c(person("Florian", "Hartig", email = "florian.hartig@biologie.uni-regensburg.de", role = c("aut", "cre"), comment = "Theoretical Ecology, University of Regensburg, Regensburg, Germany"))
Author: Florian Hartig [aut, cre] (Theoretical Ecology, University of

readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed
models. Currently supported are (generalized) linear mixed models from 'lme4'
(classes 'lmerMod', 'glmerMod') and 'glmmTMB', generalized additive models ('gam' from 'mgcv'),
classes. Moreover, externally created simulations, e.g. posterior predictive simulations
Imports: stats, graphics, utils, grDevices, parallel, doParallel,
foreach, gap, qrnn, lmtest, ape, sfsmisc, MASS, lme4, mgcv,

(classes 'lmerMod', 'glmerMod'), 'glmmTMB' and 'spaMM', generalized additive models ('gam' from
'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model