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. The package 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.

Routines for estimating tree fiber (tracheid) length distributions in the standing tree
           based on increment core samples. Two types of data can be used with the package, increment 
           core data measured by means of an optical fiber analyzer (OFA), e.g. such as the Kajaani 
          Fiber Lab, or measured by microscopy. Increment core data analyzed by OFAs consist of the cell
          lengths of both cut and uncut fibres (tracheids) and fines (such as ray parenchyma cells)
           without being able to identify which cells are cut or if they are fines or fibres. The
          microscopy measured data consist of the observed lengths of the uncut fibres in the increment
         core. A censored version of a mixture of the fine and fiber length distributions is proposed to
         fit the OFA data, under distributional assumptions (Svensson et al., 2006) <doi:10.1111/j.1467-9469.2006.00501.x>. The package offers two choices for the
        assumptions of the underlying density functions of the true fiber (fine) lenghts of those fibers
        (fines) that at least partially appear in the increment core, being the generalized gamma and 
        the log normal densities.