Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster Analysis (SCA) for multivariate data analysis. The package provides comprehensive tools for feature selection, model training, prediction, and evaluation in hydrological and environmental modeling applications. Key functionalities include recursive feature elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag (OOB) validation, and ensemble prediction capabilities. The package supports both single and multivariate response variables, making it suitable for complex environmental modeling scenarios. For more details see Li et al. (2021) <doi:10.5194/hess-25-4947-2021>.

PAM (Partitioning Around Medoids) algorithm application to samples of single cell sequencing techniques
	     with a high number of cells (as many as the computer memory allows). The package uses a binary format to
	     store matrices (either full, sparse or symmetric) in files written in the disk that can contain any data type
	     (not just double) which allows its manipulation when memory is sufficient to load them as int or float, but not
	     as double. The PAM implementation is done in parallel, using several/all the cores of the machine, if it has them.
	     This package shares a great part of its code with packages 'jmatrix' and 'parallelpam' but their functionality is
	     included here so there is no need to install them.

Sometimes it is useful to serve up alternative 'shiny' UIs 
    depending on information passed in the request object, such as the value of 
    a cookie or a query parameter. This packages facilitates such switches.

Analyse single cell RNA sequencing data using entropy to
    calculate heterogeneity and homogeneity of genes amongst the cell
    population. From the work of Michael J. Casey, Ruben J. Sanchez-Garcia 
    and Ben D. MacArthur.

A pipeline for estimating the stellar
        age, mass, and radius given observational 
        effective temperature, [Fe/H], and astroseismic
	parameters. The results are obtained adopting a maximum likelihood
	technique over a grid of pre-computed stellar models, as
	described in Valle et al. (2014) <doi:10.1051/0004-6361/201322210>.