  One of the main concerns of silviculture and forest management focuses on
finding fast, cost-efficient and non-destructive ways of measuring wood
properties in standing trees. This paper presents an R package \verb+fiberLD+
that provides functions for estimating tree fiber length distributions in the
standing tree based on increment core samples. The methods rely on increment
core data measured by means of an optical fiber analyzer (OFA) or measured by
microscopy. Increment core data analyzed by OFAs consist of the cell lengths of
both cut and uncut fibers (tracheids) and fines (such as ray parenchyma cells)
without being able to identify which cells are cut or if they are fines or
fibers. The microscopy measured data consist of the observed lengths of the
uncut fibers 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. Two choices for the assumptions of the underlying
density functions of the true fiber (fine) lengths of those fibers (fines) that
at least partially appear in the increment core are considered, such as the
generalized gamma and the log normal densities. Maximum likelihood estimation
is used for estimating the model parameters for both the OFA analyzed data and
the microscopy measured data.
