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DHARMa
View on CRAN: Click
here
Download and install DHARMa package within the R console
Install from CRAN:
install.packages("DHARMa")
Install from Github:
library("remotes")
install_github("cran/DHARMa")
Install by package version:
library("remotes")
install_version("DHARMa", "0.4.7")
Attach the package and use:
library("DHARMa")
Maintained by
Florian Hartig
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-08-26
Latest Update: 2022-09-08
Description:
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.
How to cite:
Florian Hartig (2016). DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.4.7, https://cran.r-project.org/web/packages/DHARMa. Accessed 05 May. 2025.
Previous versions and publish date:
0.1.0 (2016-08-26 19:06), 0.1.1 (2016-11-16 23:54), 0.1.2 (2016-11-19 18:44), 0.1.3 (2016-12-12 00:48), 0.1.4 (2017-03-08 00:10), 0.1.5 (2017-03-11 00:03), 0.1.6 (2018-03-18 21:41), 0.2.0 (2018-06-05 23:59), 0.2.1 (2019-01-17 16:20), 0.2.2 (2019-01-20 17:10), 0.2.3 (2019-02-12 07:47), 0.2.4 (2019-03-06 09:30), 0.2.5 (2019-11-18 19:10), 0.2.6 (2019-11-26 21:50), 0.2.7 (2020-02-06 21:00), 0.3.0 (2020-04-20 17:30), 0.3.1 (2020-05-12 08:20), 0.3.2.0 (2020-06-18 23:20), 0.3.3.0 (2020-09-08 22:20), 0.3.4 (2021-03-23 17:30), 0.4.0 (2021-03-28 08:51), 0.4.1 (2021-04-08 23:10), 0.4.2 (2021-07-05 12:20), 0.4.3 (2021-07-07 14:30), 0.4.4 (2021-09-28 15:00), 0.4.5 (2022-01-16 19:52), 0.4.6 (2022-09-08 23:33)
Other packages that cited DHARMa R package
View DHARMa citation profile
Other R packages that DHARMa depends,
imports, suggests or enhances
Complete documentation for DHARMa
Functions, R codes and Examples using
the DHARMa R package
Some associated functions: DHARMa.ecdf . DHARMa . benchmarkRuntime . checkDots . checkModel . checkSimulations . createDHARMa . createData . ensureDHARMa . ensurePredictor . getFitted . getFixedEffects . getObservedResponse . getPossibleModels . getQuantile . getRandomState . getRefit . getResiduals . getSimulations . hist.DHARMa . hurricanes . outliers . plot.DHARMa . plot.DHARMaBenchmark . plotConventionalResiduals . plotQQunif . plotResiduals . plotSimulatedResiduals . print.DHARMa . recalculateResiduals . residuals.DHARMa . runBenchmarks . simulateLRT . simulateResiduals . testCategorical . testDispersion . testGeneric . testOutliers . testOverdispersion . testOverdispersionParametric . testPDistribution . testQuantiles . testResiduals . testSimulatedResiduals . testSpatialAutocorrelation . testTemporalAutocorrelation . testUniformity . testZeroInflation . transformQuantiles .
Some associated R codes: DHARMa.R . compatibility.R . createData.R . data.R . helper.R . imports.R . plots.R . random.R . runBenchmarks.R . simulateLRT.R . simulateResiduals.R . startup.R . tests.R . transformQuantiles.R . Full DHARMa package functions and examples
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