# maxstable: Partition-composite likelihood for multivariate max-stable... In HiDimMaxStable: Inference on High Dimensional Max-Stable Distributions

## Description

Computes the partition-composite likelihood for observations sampled from a multivariate max-stable distribution whose spectral random vector is Gaussian, Log-normal or has a clustered copula distribution.

## Usage

 `1` ```maxstable.l.clusters(data,clusters=rep(1,dim(data)[1]),ln=FALSE,spatial=NULL,...) ```

## Arguments

 `data` a matrix representing the data. Each column corresponds to one observation. `clusters` a vector of integers that gives the partition that is used to compute the partition-composite likelihood. Blocks of the partition should be of size smaller or equal to 7 to avoid a too long computing time. `clusters=rep(1,dim(data)[1])` must be used to get the full likelihood. `ln` logical. If `TRUE` log-density is computed. `spatial` argument passed to the `mubz.*` function (where `*` stands for the `category` of the model). `...` further arguments to be passed to `mubz.*` function (where `*` stands for the `category` of the model). In particular, `category` is a character string indicating the model to be used: "`normal`", "`lnormal`" or "`copula`", and `params` gives the values of the parameters for which the likelihood is computed.

`mubz.normal`,`mubz.lnormal`, `mubz.copula`.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```n.site<-5 xy<-matrix(runif(2 * n.site, 0, 0.5), ncol = 2) param<-c(0.5,1.5) n.obs<-2 library(SpatialExtremes) data<-t(rmaxstab(n.obs, xy, "whitmat", nugget = 0, range = param[1], smooth = param[2])) d<-maxstable.l.clusters(data,clusters=c(1,1,1,2,2), params=param, category="normal", spatial=list(sites=xy,family=spatialWhittleMatern)) ```