description update

Author: florianhartig

DHARMa/DESCRIPTION

@@ -5,7 +5,7 @@ Date: 2019-01-19
 Authors@R: c(person("Florian", "Hartig", email = "florian.hartig@biologie.uni-regensburg.de", role = c("aut", "cre"), comment = "Theoretical Ecology, University of Regensburg, Regensburg, Germany"))
 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 (generalized) linear mixed models from 'lme4' 
+    models. Currently supported are linear and generalized linear (mixed) models from 'lme4' 
     (classes 'lmerMod', 'glmerMod'), 'glmmTMB' 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 

DHARMa/R/DHARMa.R

@@ -1,7 +1,7 @@
 #' @title DHARMa - Residual Diagnostics for HierArchical (Multi-level / Mixed) Regression Models
 #' @name DHARMa
 #' @docType package
-#' @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 (generalized) linear mixed models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB' 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.
+#' @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' 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.
 #' @details See index / vignette for details
 #' @seealso \code{\link{simulateResiduals}}
 #' @examples 

DHARMa/vignettes/DHARMa.Rmd

@@ -9,7 +9,8 @@ vignette: >
   %\VignetteIndexEntry{Vignette for the DHARMa package}
   %\VignetteEngine{knitr::rmarkdown}
   \usepackage[utf8]{inputenc}
-abstract: "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') and 'glmmTMB', 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.\n \n \n"
+abstract: "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' 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. 
+\n \n \n"
 editor_options: 
   chunk_output_type: console
 ---