Extend lasso and elastic-net model fitting for ultra
    high-dimensional, multi-gigabyte data sets that cannot be loaded into
    memory. Designed to be more memory- and computation-efficient than existing
    lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing
    the user to analyze big data analysis even on an ordinary laptop.

Calculation of physical (e.g. aerodynamic conductance, surface temperature), 
             and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties
			 from eddy covariance data and accompanying meteorological measurements. Calculations
			 assume the land surface to behave like a 'big-leaf' and return bulk ecosystem/canopy variables.

Regression for data too large to fit in memory.

Regression for data too large to fit in memory. This package functions exactly like the 'biglm' package, but works with later versions of R.

Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), <arXiv:1812.09869>).

Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists. The main functions in the 'bigmatch' package are optcal() to find the optimal caliper, optconstant() to find the optimal number of nearest neighbors, and nfmatch() to find a near-fine balance match with a caliper and a restriction on the number of nearest neighbors. 
    Yu, R., Silber, J. H., and Rosenbaum, P. R. (2020). <DOI:10.1214/19-sts699>.

MDS is a statistic tool for reduction of dimensionality, using as input a distance
    matrix of dimensions n × n. When n is large, classical algorithms suffer from
    computational problems and MDS configuration can not be obtained.
    With this package, we address these problems by means of six algorithms, being two of them 
    original proposals:
        - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004).
        - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021)
        <arXiv:2007.11919> (original proposal).
        - Reduced MDS proposed by Paradis E (2018).
        - Pivot MDS proposed by Brandes U. and C. Pich (2007)
        - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021)
        <arXiv:2007.11919> (original proposal).
        - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006).