mainGE: Genotype x Environment models using linear or gaussian kernel
In italo-granato/GEmodels:

Genotype x Environment models using linear or gaussian kernel

1	mainGE <- function(Y, X, XF=NULL, W=NULL, method=c("GK", "G-BLUP"), h=NULL, model = c("SM", "MM", "MDs", "MDe", "Cov"), nIter = 150, burnIn = 50, thin = 5, ...)

`Y`	`data.frame` Phenotypic data with three columns. The first column is a `factor` for assigned environments, the second column is a `factor` for assigned individuals and the third column contains the trait of interest.
`X`	`matrix` Marker matrix with individuals in rows and marker in columns
`XF`	`matrix` Design matrix (n \times p) for fixed effects
`W`	`matrix` Environmental covariance matrix. If `W` is provided, it can be used along with `MM` and `MDs` models. See details
`method`	Kernel to be used. Methods implemented are the gaussian kernel `Gk-EB` and the linear kernel `G-BLUP`
`h`	`numeric` Bandwidth parameter to create the gaussian kernel matrix. For `method` `Gk-EB`, if `h` is not provided, then it is computed following a empirical bayesian selection method. See details
`model`	Specifies the genotype by environment model to be fitted. `SM` is the single-environment main genotypic effect model. `MM` is the multi-environment main genotypic effect model, `MDs` is the multi-environment single variance genotype x environment deviation model, `MDe` is the multi-environment, environment-specific variance genotype x environment deviation model
`nIter`	`integer` Number of iterations.
`burnIn`	`integer` Number of iterations to be discarded as burn-in.
`thin`	`integer` Thinin interval.
`...`	additional arguments to be passed.