Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) <doi:10.1098/rsif.2005.0058> and the 'Python' library 'PyLMD'.

Several service functions to be used to analyse datasets obtained from diallel experiments within the frame of linear models in R, as described in  Onofri et al (2020) <DOI:10.1007/s00122-020-03716-8>.

A fast dimensionality reduction method scaleable to large numbers of samples.
  Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS,
  but rather than calculating a complete distance matrix between all pairs of samples,
  only the distances between a set of landmarks and the samples are calculated.

Fit linear and generalized linear mixed-effects models.
    The models and their components are represented using S4 classes and
    methods.  The core computational algorithms are implemented using the
    'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the
    'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'.

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>.