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.

Compares how well different models estimate a quantity of interest (the "focus") so that different models may be preferred for different purposes.  Comparisons within any class of models fitted by maximum likelihood are supported, with shortcuts for commonly-used classes such as generalised linear models and parametric survival models.  The methods originate from Claeskens and Hjort (2003) <doi:10.1198/016214503000000819> and Claeskens and Hjort (2008, ISBN:9780521852258).

Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) <doi:10.1109/TSP.2014.2356442> and Miettinen et al. (2017) <doi:10.1016/j.sigpro.2016.08.028>. The package is described in Miettinen, Nordhausen and Taskinen (2018) <doi:10.32614/RJ-2018-046>.