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.

The Occluded Surface (OS) algorithm is a widely used approach for analyzing atomic packing in biomolecules as described by Pattabiraman N, Ward KB, Fleming PJ (1995) <doi:10.1002/jmr.300080603>. Here, we introduce 'fibos', an 'R' and 'Python' package that extends the 'OS' methodology, as presented in Soares HHM, Romanelli JPR, Fleming PJ, da Silveira CH (2024) <doi:10.1101/2024.11.01.621530>.

Computes the prior fraction, the per-group pooling or shrinkage
    factor, for hierarchical models, including directly from 'brms' fits. For
    each group-level coefficient the prior fraction is the share of the
    posterior precision contributed by the shrinkage prior relative to the
    likelihood; values near one indicate a coefficient that is prior-dominated
    (the centring/non-centring funnel regime), values near zero indicate a
    likelihood-dominated coefficient that is well identified from the data.
    These quantities are invisible to standard convergence diagnostics such as
    R-hat and effective sample size, and they indicate where a non-centred
    reparameterisation is likely to help. A companion advisor reports the same
    decomposition for changepoint random effects fitted with 'smoothbp'. The
    underlying geometry (the Fisher-metric connection on the base-fiber split,
    for which this connection is flat so the obstruction is statistical rather
    than geometric) is described in Bindoff (2026)
    <doi:10.5281/zenodo.20724550>; code reproducing the paper is in the
    package's source repository.