Performs backward elimination with similar syntax to 
    the stepAIC() function from the 'MASS' package. A bounding algorithm is 
    used to avoid fitting unnecessary models, making it much faster.

Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.

A fast method for approximating time-varying infectious disease
	transmission rates from disease incidence time series and other
	data, based on a discrete time approximation of an SEIR model, as
	analyzed in Jagan et al. (2020) <doi:10.1371/journal.pcbi.1008124>.

Provides an efficient tool for creating custom-time bioclimatic and derived environmental 
    summary variables from user-supplied raster data for user-defined timeframes. The package overcomes 
    computational bottlenecks by automatically switching between an in-memory framework using the 'terra' 
    package to maximize speed for smaller datasets, and an on-disk tiling framework for rasters that exceed 
    available RAM, leveraging 'exactextractr' and 'Rfast' to process data in chunks. The core functions, 
    derive_bioclim() and derive_statistics(), offer a unified interface with flexibility for custom time 
    periods beyond standard quarters and the use of fixed temporal indices, facilitating the creation of 
    temporally-matched environmental variables for ecological and biogeographical modeling. Visit 
    the package website <https://gepinillab.github.io/fastbioclim/> to find tutorials in English and Spanish.

This is a two-in-one package which provides interfaces to
        both R and 'Python'. It implements fast hierarchical, agglomerative
        clustering routines. Part of the functionality is designed as drop-in
        replacement for existing routines: linkage() in the 'SciPy' package
        'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the
        'flashClust' package. It provides the same functionality with the
        benefit of a much faster implementation. Moreover, there are
        memory-saving routines for clustering of vector data, which go beyond
        what the existing packages provide. For information on how to install
        the 'Python' files, see the file INSTALL in the source distribution.
        Based on the present package, Christoph Dalitz also wrote a pure 'C++'
        interface to 'fastcluster':
        <https://lionel.kr.hs-niederrhein.de/~dalitz/data/hclust/>.