Description of the dataset | Model fitting and inference | Residual analysis | Influence analysis

Global Influence | hatvalues method | cooks.distance method | Cook's distance | generalized Cook's distance and Q-displacement | Likelihood displacement | influence method | Global influence plots | Local Influence | Curvature | Direction | Perturbation | Parameters | Perturbation schemes with respect to a subset of the covariates | Local influence plots | References

augment, glance and tidy methods | augment | glance | tidy | tidy_local_influence and local_influence_benchmarks methods | autoplot and local_influence_autoplot methods

base-R diagnostic plots | Choosing plots | Titles and subtitles | Colors, sizes, point types and line types | Points to be identified

ggplot2-based diagnostic plots | Choosing plots | Titles and subtitles | Colors, sizes, point types and line types | Points to be identified | Multiple plots

Standard errors for the means | Standard errors for the means using the predict method | Standard errors for the means using the augment method | Confidence intervals for the means | Confidence intervals for the means using the predict method | Confidence intervals for the means using the augment method | Prediction intervals for the response variables | Prediction intervals for the response variables using the predict method | Prediction intervals for the response variables using the augment method

Fitting mixpoissonreg models via direct maximization of the likelihood function | Obtaining estimates without formulas

What is mixpoissonreg for? | Why use the mixpoissonreg package? | glm-like functions to fit and analyze fitted models | Useful plots for fitted models available using R's base graphics or ggplot2 | Estimation of parameters via EM-algorithm | Easy to do inference on fitted models with help of lmtest package | Regression structure for the precision parameter | Several global and local influence measures implemented (along with corresponding plots) | Compatibility with the tidyverse