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DHARMa  

Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models
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]
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)
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