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 __spaMM__ is an R package originally designed for fitting ***spa***tial generalized linear ***M***ixed ***M***odels, particularly the so-called geostatistical model allowing prediction in continuous space. But it is now a more general-purpose package for fitting mixed models, spatial or not, and with efficient methods for both geostatistical and autoregressive models. It can fit models with non-gaussian random effects (e.g., Beta- or Gamma-distributed), structured dispersion models (including residual dispersion models with random effects), and implements several variants of Laplace and PQL approximations, including (but not limited to) those discussed in the  _h_-likelihood literature (see References). Some non-GLM response families are now handled. It can also fit multivariate-response models, including some of interest in quantitative genetics. 
 
 ## What to look for (or not) here ?
-This repository provides whatever information I do not try to put into the R package, such as its vignette-like [gentle introduction](https://gitlab.mbb.univ-montp2.fr/francois/spamm-ref/-/blob/master/vignettePlus/spaMMintro.pdf) (latest version: 2023/03/03, in particular correcting outdated parts of the previous version) and the [slides](https://gitlab.mbb.univ-montp2.fr/francois/spamm-ref/-/blob/master/vignettePlus/MixedModels_useR2021.pdf) from the presentation of spaMM at the [useR2021](https://user2021.r-project.org/) conference. 
+This repository provides whatever information I do not try to put into the R package, such as its vignette-like [gentle introduction](https://gitlab.mbb.univ-montp2.fr/francois/spamm-ref/-/blob/master/vignettePlus/spaMMintro.pdf) (latest version: 2023/03/03, in particular correcting outdated parts of the previous version) and the [slides](https://gitlab.mbb.univ-montp2.fr/francois/spamm-ref/-/blob/master/vignettePlus/MixedModels_useR2021.pdf) from the presentation of `spaMM` at the [useR2021](https://user2021.r-project.org/) conference. 
 
 It might also be used to distribute development versions of `spaMM`. However, use a CRAN repository for standard installation of the package, and see the (unofficial) [CRAN github repository](https://github.com/cran/spaMM) for an archive of sources for all versions of spaMM previously published on CRAN.
 
@@ -22,7 +22,7 @@ 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`, see `diallel` or `antisym` documentations)  
+- 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;
 - 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;