Impute general multivariate missing data with the fractional hot deck imputation based on Jaekwang Kim (2011) <doi:10.1093/biomet/asq073>.

Useful tools for conveniently downloading FHIR resources in xml format 
    and converting them to R data.frames. Uses FHIR-search to download bundles 
    from a FHIR server, provides functions to save and read xml-files containing such bundles 
    and allows flattening the bundles to data.frames using XPath expressions. FHIR® is the registered trademark 
    of HL7 and is used with the permission of HL7. Use of the FHIR trademark does not constitute endorsement of this product by HL7.

Fitting (hierarchical) hidden Markov models to financial data
    via maximum likelihood estimation. See Oelschläger, L. and Adam, T.
    "Detecting Bearish and Bullish Markets in Financial Time Series Using 
    Hierarchical Hidden Markov Models" (2021, Statistical Modelling) 
    <doi:10.1177/1471082X211034048> for a reference on the method. A user guide 
    is provided by the accompanying software paper "fHMM: Hidden Markov Models 
    for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R.
    (2024, Journal of Statistical Software)  <doi:10.18637/jss.v109.i09>.

Functions to compare two or more survival curves with:
             a) The Fleming-Harrington test for right-censored data based on permutations and on counting processes.
             b) An extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.

Provide functions for forest inventory calculations. Common volumetric equations (Smalian, Newton and Huber) as well stacking factor and form.

Provides three S7 classes — streamline, bundle, and bundle_set —
    for representing diffusion MRI tractography data in R, together with a
    concise set of methods for computing shape descriptors (arc-length,
    curvature, torsion, sinuosity), the Hausdorff distance between streamlines,
    arc-length reparametrization of streamlines and bundles onto uniform grids,
    combination of streamlines or bundles into a single bundle, combination of
    bundles from multiple subjects or sessions into a bundle_set, and coercion
    to and from the dwiFiber S4 class of the 'dti' package. See Dell'Acqua, F.,
    Descoteaux, M. and Leemans, A. (2024) "Handbook of Diffusion MR Tractography"
    <doi:10.1016/C2018-0-02520-7> for more about the mathematical and computational
    underpinnings of diffusion MRI tractography.