From 94f146ef5b7fd564c311059f86cc75dff50a0ecb Mon Sep 17 00:00:00 2001
From: francois <francois.rousset@umontpellier.fr>
Date: Wed, 16 Nov 2022 17:50:03 +0000
Subject: [PATCH] Update README.md

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 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

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@@ -20,7 +20,7 @@ It will also include a few selected versions of spaMM. However, use a CRAN repos
 
 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)). `diallel` models for **dyadic interactions** (accounting for the fact that the same individual would appear in each of two individual random effects) can also fe fitted. 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)). `diallel` models for **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 fe fitted. 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;
-- 
GitLab