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 a high-performance framework for deriving bioclimatic and custom summary variables from 
    large-scale climate raster data. The package features a dual-backend architecture that intelligently switches 
    between fast in-memory processing for smaller datasets (via the 'terra' package) and a memory-safe tiled approach 
    for massive datasets that do not fit in RAM (via 'exactextractr' and 'Rfast'). The main functions, 
    derive_bioclim() and derive_statistics(), offer a unified interface with advanced options for 
    custom time periods and static indices, making it suitable for a wide range of ecological and 
    environmental modeling applications. A software note is in preparation. In the meantime, you can 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/>.