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@@ -23,7 +23,7 @@ The `spaMM` package was developed first to fit mixed-effect models with spatial
 - Fitting spatial and non-spatial correlation models: **geostatistical** models with random-effect terms following the `Matern` as well as the much less known `Cauchy` correlation models, **autoregressive** models described by an `adjacency` matrix, AR(_p_) and ARMA(_p_,_q_) time-series models (`ARp` and `ARMA`), or an **arbitrary given** precision or correlation matrix (`corrMatrix`). Conditional spatial effects can be fitted,  as in (say) `Matern(female|...) + Matern(male|...)` to fit distinct random effects for females and males (e.g., [Tonnabel et al., 2021](https://doi.org/10.1111/mec.15833)). Brave users can even define their own parametric correlation models, to be fitted as any other random effect (the `corrFamily` feature).
 - A further class of spatial correlation models, "Interpolated Markov Random Fields" (`IMRF`) covers widely publicized approximations of Matérn models ([Lindgren et al. 2011](http://doi.org/10.1111/j.1467-9868.2011.00777.x)) and the multiresolution model of [Nychka et al. 2015](https://doi.org/10.1080/10618600.2014.914946). 
 - Symmetric and antisymmetric **dyadic interaction** effects (such as considered in so-called Bradley-Terry models or in diallel experiments) can be fitted as fixed or as random effects (see e.g. `X.antisym`, `diallel` or `antisym` documentations)  
-- Allowed response families include zero-truncated variants of the Poisson, two negative binomial families, beta response family, and the Conway-Maxwell-Poisson (`COMPoisson`) family;
+- Allowed response families include beta response, beta-binomial, the Conway-Maxwell-Poisson (`COMPoisson`), and two negative binomial families. Zero-truncated variants of the `poisson` and negative-binomial families are handled;
 - All the above features combined in multivariate-response models;
 - A replacement function for `glm`, useful when the latter (or even `glm2`) fails to fit a model;
 - A syntax close to that of `glm` or [`g`]`lmer`. It includes a growing list of extractor methods similar to those in `stats` or `nlme`/`lmer`, and functions for inference beyond the fits, such as `confint()` for confidence intervals of fixed-effect parameters, `predict()` and related functions for point prediction and prediction variances, and compatibility with functions from other packages such as `multcomp::glht()` and `lmerTest` procedures providing F tests using Satterthwaite method (see `` `post-fit` `` and `anova` documentation items);