Machine learning utilities for fast vectorized model training.
    Methods are based on standard statistical learning references such as
    Hastie et al. (2009) <doi:10.1007/978-0-387-84858-7>.

Provides pedagogical tools for visualization and numerical computation in vector calculus. Includes functions for parametric curves, scalar and vector fields, gradients, divergences, curls, line and surface integrals, and dynamic 2D/3D graphical analysis to support teaching and learning. The implemented methods follow standard treatments in vector calculus and multivariable analysis as presented in Marsden and Tromba (2011) <ISBN:9781429215084>, Stewart (2015) <ISBN:9781285741550>, Thomas, Weir and Hass (2018) <ISBN:9780134438986>, Larson and Edwards (2016) <ISBN:9781285255869>, Apostol (1969) <ISBN:9780471000051>, Spivak (1971) <ISBN:9780805390216>, Schey (2005) <ISBN:9780071369080>, Colley (2019) <ISBN:9780321982384>, Lizarazo Osorio (2020) <ISBN:9789585450103>, Sievert (2020) <ISBN:9780367180165>, and Borowko (2013) <ISBN:9781439870791>.

Allows registered 'VectorSurv' <https://vectorsurv.org/> users access to data through the 'VectorSurv API' <https://api.vectorsurv.org/>. Additionally provides functions for analysis and visualization.

New wavelet methodology (vector wavelet coherence) (Oygur, T., Unal, G, 2020 <doi:10.1007/s40435-020-00706-y>) 
  to handle dynamic co-movements of multivariate time series via extending multiple and quadruple wavelet coherence methodologies. 
  This package can be used to perform multiple wavelet coherence, quadruple wavelet coherence, and n-dimensional vector wavelet coherence analyses.

A minimal columnar query engine with lazy execution on datasets
    larger than RAM. Provides 'dplyr'-like verbs (filter(), select(), mutate(),
    group_by(), summarise(), joins, window functions) and common aggregations
    (n(), sum(), mean(), min(), max(), sd(), first(), last()) backed by a
    pure C11 pull-based execution engine and a custom on-disk format ('.vtr').
    Reads and writes 'GeoTIFF' (including tiled and 'BigTIFF' layouts) and a
    tiled raster format ('.vec') with overview pyramids and time cubes for
    larger-than-RAM raster data. Streams vector operations (spatial transforms,
    point-in-polygon and nearest-feature joins including a two-sided
    grid-partitioned join, select-by-location, clip, erase, dissolve,
    'rasterization', 'polygonization', and contouring) through 'sf', and runs
    raster operations (zonal statistics, focal windows, terrain derivatives,
    resample or 'reproject' warp, polygon masking, map algebra, and 'mosaicking')
    in native C or over the tiled '.vec' format, one batch or tile at a time for
    data larger than RAM.