The 'DHARMa' package uses a simulation-based approach to create 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'), '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 problems, such as over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation.

Documentation

Manual: DHARMa.pdf
Vignette: Vignette for the DHARMa package

Maintainer: Florian Hartig <florian.hartig at biologie.uni-regensburg.de>

Author(s): Florian Hartig*

Install package and any missing dependencies by running this line in your R console:

install.packages("DHARMa")

Depends R (>= 3.0.2)
Imports stats, graphics, utils, grDevices, gap, qrnn, lmtest, ape, sfsmisc, MASS, doParallel, foreach, lme4, mgcv
Suggests knitr, testthat
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Package DHARMa
Materials
URL http://florianhartig.github.io/DHARMa/
Task Views
Version 0.1.5
Published 2017-03-11
License GPL (>= 3)
BugReports https://github.com/florianhartig/DHARMa/issues
SystemRequirements
NeedsCompilation no
Citation
CRAN checks DHARMa check results
Package source DHARMa_0.1.5.tar.gz