Provides functions to estimate, predict and interpolate areal
        data. For estimation and prediction we assume areal data is an average
        of an underlying continuous spatial process as in Moraga et
        al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020)
        <doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020)
        <doi:10.1093/biostatistics/kxy041>. The interpolation methodology is
        (mostly) based on Goodchild and Lam (1980, ISSN:01652273).

This tiny package contains one function smirnov() which
        calculates two scaled taxonomic coefficients, Txy (coefficient
        of similarity) and Txx (coefficient of originality). These two
        characteristics may be used for the analysis of similarities
        between any number of taxonomic groups, and also for assessing
        uniqueness of giving taxon. It is possible to use smirnov()
        output as a distance measure: convert it to distance by
        "as.dist(1 - smirnov(x))".

Constructs a yield curve by the Smith-Wilson method from a table of
              libor and swap rates. Now updated to take bond coupons and prices 
              in the same table.

Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID).
  It allow sequence-space-time host and viral population data storage, indexation and querying.

Visualisation tools for 'SMITIDstruct' package.
  Allow to visualize host timeline, transmission tree, index diversities
  and variant graph using 'HTMLwidgets'. It mainly using 'D3JS' javascript framework.

Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014)<doi:10.1080/01621459.2013.879531> proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion.