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Update README.md

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## What is spaMM ?
__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. Its latest major addition is the ability to fit multivariate-response models.
__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. Its latest major addition is the ability to 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: 2021/04/12) 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: 2021/09/13) 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 will also include a few selected 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.
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<img align="right" width="407" height="290" src="https://raw.githubusercontent.com/courtiol/IsoriX/master/image/image_intro-.gif">
Initial stimulus for spaMM development came from work by Lee and Nelder on h-likelihood (e.g. [Lee, Nelder & Pawitan](https://doi.org/10.1201/9781420011340), 2006; [Lee & Lee](http://dx.doi.org/10.1007/s11222-011-9265-9) 2012; see also [Molas and Lesaffre](http://dx.doi.org/10.1002/sim.3852), 2010), and it retains from that work several distinctive features, such as the ability to fit models with non-gaussian random effects (e.g., Beta- or Gamma-distributed), structured dispersion models (including residual dispersion models with random effects), and implementation of several variants of Laplace and PQL approximations. But it often relies on alternatives to the iterative algorithms considered by Lee and Nelder to jointly fit all model parameters, and on alternative implementations of the most expensive matrix computations. 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:
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 as a more general package for inferences under models with or without spatially-correlated random effects. It can fit multivariate-response models (its latest major addition). Initial development drew inspiration from work by Lee and Nelder on $h$-likelihood (e.g. [Lee, Nelder & Pawitan](https://doi.org/10.1201/9781420011340), 2006; [Lee & Lee](http://dx.doi.org/10.1007/s11222-011-9265-9) 2012; see also [Molas and Lesaffre](http://dx.doi.org/10.1002/sim.3852), 2010), and it retains from that work several distinctive features, such as the ability to fit models with non-gaussian random effects (e.g., Beta- or Gamma-distributed), structured dispersion models (including residual dispersion models with random effects), and implementation of several variants of Laplace and PQL approximations. However, some of the computations considered in such works are expensive. Hence, to make `spaMM` competitive to fit large data sets, recent versions have increasingly relied on alternative algorithms when possible, without sacrificing any of its distinctive features. `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 geostatistical models with random-effect terms following the `Matern` as well as the much less known `Cauchy` correlation models, or autoregressive models described by an `adjacency` matrix or `AR1` model, 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)).
- 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).
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