Commit 60bb5308 authored by francois's avatar francois
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Update README.md

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## General features
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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:
- 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)).
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