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 The `spaMM` package was developed first to fit mixed-effect models with spatial correlations, which commonly occur in ecology, but it has since been developed into a more general package for inferences under models with or without spatially-correlated random effects, including multivariate-response models. To make it competitive to fit large data sets, `spaMM` has distinct algorithms for three cases: sparse precision, sparse correlation, and dense correlation matrices, and is efficient to fit geostatistical, autoregressive, and other mixed models on large data sets. Notable features include: 
 
-- 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)). Models for smmetric or antisymmetric **dyadic interactions** (accounting for the fact that the same individual would appear in each of two individual random effects; e.g., [Charpentier et al., 2022]( https://doi.org/10.7554/eLife.79417)) can also be fitted: see `diallel` or `antisym` documentation. Brave users can even define their own parametric correlation models, to be fitted as any other random effect (the `corrFamily` feature).
+- 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)). Models for symmetric or antisymmetric **dyadic interactions** (accounting for the fact that the same individual would appear in each of two individual random effects; e.g., [Charpentier et al., 2022]( https://doi.org/10.7554/eLife.79417)) can also be fitted: see `diallel` or `antisym` documentation. 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). 
 - Allowed response families include zero-truncated variants of the Poisson, two negative binomial families, beta response family, and the Conway-Maxwell-Poisson (`COMPoisson`) family;
 - All the above features combined in multivariate-response models. Previously, more experimental facilities have been available for handling multinomial data only;