Multi-block data analysis concerns the analysis of several
    sets of variables (blocks) observed on the same group of individuals.
    The main aims of the RGCCA package are: to study the relationships
    between blocks and to identify subsets of variables of each block
    which are active in their relationships with the other blocks. This
    package allows to (i) run R/SGCCA and related methods,
    (ii) help the user to find out the optimal parameters for R/SGCCA such
    as regularization parameters (tau or sparsity), (iii) evaluate the
    stability of the RGCCA results and their significance, (iv) build predictive
    models from the R/SGCCA. (v) Generic print()
    and plot() functions apply to all these functionalities.

Toolbox for chemometrics analysis of bidimensional gas 
    chromatography data. This package import data for common scientific data
    format (NetCDF) and fold it to 2D chromatogram. Then, it can perform
    preprocessing and multivariate analysis. In the preprocessing algorithms,
    baseline correction, smoothing, and peak alignment are available.
    While in multivariate analysis, multiway principal component analysis is
    incorporated.

Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) <https://revistas.unimilitar.edu.co/index.php/rfcb/article/view/2037> Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico.

Earth Engine <https://earthengine.google.com/> client library for R. All
  of the 'Earth Engine' API classes, modules, and functions are made available. Additional
  functions implemented include importing (exporting) of Earth Engine spatial objects, 
  extraction of time series, interactive map display, assets management interface, 
  and metadata display. See <https://r-spatial.github.io/rgee/> for further details.