From 3d4b4e15b346ce51ff6e32005fb0c9e96bb49454 Mon Sep 17 00:00:00 2001
From: francois <francois.rousset@umontpellier.fr>
Date: Mon, 10 Jun 2024 06:54:11 +0000
Subject: [PATCH] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 73287b4..243b57c 100644
--- a/README.md
+++ b/README.md
@@ -20,11 +20,11 @@ It might also be used to distribute development versions of `spaMM`. However, us
 
 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)). 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)). "Composite" random effects that combine features of such autocorrelated random effects and of random-coefficient models (say, `Matern(age|...)`) can be 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). 
 - 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 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;
+- All the above features combined in multivariate-response models, including random effects correlated over different response variables;
 - ML and REML fits (see below for comments on likelihood approximations);
 - 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`. 
-- 
GitLab