Flexible functions that use 'lme4' as computational engine for 
             fitting models used in Genomic Selection (GS). GS is a technology used for genetic 
             improvement, and it has many advantages over phenotype-based selection. There 
             are several statistical models that adequately approach the statistical 
             challenges in GS, such as in linear mixed models (LMMs). The 'lme4' is the 
             standard package for fitting linear and generalized LMMs in the R-package, 
             but its use for genetic analysis is limited because it does not allow the 
             correlation between individuals or groups of individuals to be defined. The 
             'lme4GS' package is focused on fitting LMMs with covariance structures defined 
             by the user, bandwidth selection, and genomic prediction. The new package is 
             focused on genomic prediction of the models used in GS and can fit LMMs using
             different variance-covariance matrices. Several examples of GS models are 
             presented using this package as well as the analysis using real data. For more
             details see Caamal-Pat et.al. (2021) <doi:10.3389/fgene.2021.680569>.

Provides analytic derivatives and information matrices for
    fitted linear mixed effects (lme) models and generalized least squares (gls) models
    estimated using lme() (from package 'nlme') and gls() (from package 'nlme'), respectively.
    The package includes functions for estimating the sampling variance-covariance of variance
    component parameters using the inverse Fisher information. The variance components include
    the parameters of the random effects structure (for lme models), the variance structure,
    and the correlation structure. The expected and average forms of the Fisher information matrix
    are used in the calculations, and models estimated by full maximum likelihood or
    restricted maximum likelihood are supported. The package also includes a function for estimating
    standardized mean difference effect sizes (Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>)
    based on fitted lme or gls models.