The 'DHARMa' package uses a simulation-based approach to create
readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed
models. Currently supported are linear and generalized linear (mixed) models from 'lme4'
(classes 'lmerMod', 'glmerMod'), 'glmmTMB' 'GLMMadaptive' and 'spaMM', generalized additive models
('gam' from 'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and
'lm' model classes. Moreover, 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.
stats, graphics, utils, grDevices, parallel, doParallel,
foreach, gap, lmtest, ape, qgam (>= 1.3.2), lme4
knitr, testthat, rmarkdown, KernSmooth, sfsmisc, MASS, mgcv,
glmmTMB (>= 1.0.1), spaMM (>= 3.2.0), GLMMadaptive

stats, graphics, utils, grDevices, parallel, doParallel,
foreach, gap, lmtest, ape, qgam (>= 1.3.2), lme4, glmmTMB (>=
1.0.1)
knitr, testthat, rmarkdown, KernSmooth, sfsmisc, MASS, mgcv,
spaMM (>= 3.2.0), GLMMadaptive, phyr

stats, graphics, utils, grDevices, Matrix, parallel, gap,
lmtest, ape, qgam (>= 1.3.2), lme4
knitr, testthat, rmarkdown, KernSmooth, sfsmisc, MASS, mgcv,
spaMM (>= 3.2.0), GLMMadaptive, glmmTMB (>= 1.0.1)

knitr, testthat, rmarkdown, KernSmooth, sfsmisc, MASS, mgcv,
mgcViz (>= 0.1.9), spaMM (>= 3.2.0), GLMMadaptive, glmmTMB (>=
1.1.2.3)

Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. These allow faceted, coloured displays of quantitative information for comparison across District Health Boards or Regional Councils. The preprint Lumley (2019) <arXiv:1912.04435> is based on the methods in this package.