Source: r-cran-dharma
Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Uploaders: Michael R. Crusoe <crusoe@debian.org>
Section: gnu-r
Testsuite: autopkgtest-pkg-r
Priority: optional
Build-Depends: debhelper-compat (= 13),
               dh-r,
               r-base-dev,
               r-cran-matrix,
               r-cran-lmtest,
               r-cran-ape,
               r-cran-lme4,
               r-cran-gap,
               r-cran-qgam
Standards-Version: 4.7.0
Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-cran-dharma
Vcs-Git: https://salsa.debian.org/r-pkg-team/r-cran-dharma.git
Homepage: https://cran.r-project.org/package=DHARMa
Rules-Requires-Root: no

Package: r-cran-dharma
Architecture: all
Depends: ${R:Depends},
         ${misc:Depends}
Recommends: ${R:Recommends}
Suggests: ${R:Suggests}
Description: Residual Diagnostics for Hierarchical (Multi-Level / Mixed)
       Regression Models 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'; phylogenetic linear models from 'phylolm' (classes
       'phylolm' and 'phyloglm'); 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, phylogenetic and temporal
       autocorrelation.