Multi-environment genomic prediction for training and test 
    environments using penalized factorial regression. Predictions are made 
    using genotype-specific environmental sensitivities as in Millet et al. 
    (2019) <doi:10.1038/s41588-019-0414-y>.

Generates 'RProtobuf' classes for 'FactSet' 'STACH V2' tabular 
    format which represents complex multi-dimensional array of data. These 
    classes help in the 'serialization' and 'deserialization' of 'STACH V2' 
    formatted data. See 'GitHub' repository documentation for more 
    information.

Compute maximum likelihood estimators of parameters in a Gaussian factor model using
  the the matrix-free methodology described in Dai et al. (2020) <doi:10.1080/10618600.2019.1704296>.
  In contrast to the factanal() function from 'stats' package, fad() can handle high-dimensional datasets where
  number of variables exceed the sample size and is also substantially faster than the EM algorithms.

The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a  
   factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model
    (see Friedman et al. (2010)), sparse linear discriminant analysis (see
    Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant
    analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.

Probability distributions that are sometimes useful in hydrology.

An implementation of a clustering algorithm for functional data based on adaptive density peak detection technique, in which the density is estimated by functional k-nearest neighbor density estimation based on a proposed semi-metric between functions. The proposed functional data clustering algorithm is computationally fast since it does not need iterative process. (Alex Rodriguez and Alessandro Laio (2014) <doi:10.1126/science.1242072>; Xiao-Feng Wang and Yifan Xu (2016) <doi:10.1177/0962280215609948>).