Natalya Pya Arnqvist[aut, cre],
Sara Sjöstedt de Luna [aut]
Konrad Abramowicz [aut]
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.
stats, graphics, Matrix, VGAM, parallel, foreach, doParallel

Natalya Pya Arnqvist[aut, cre],
Sara Sjöstedt de Luna [aut],
Konrad Abramowicz [aut]
stats, graphics, Matrix, VGAM, parallel, foreach, doParallel,
Rcpp (>= 0.12.7)

Focused Information Criteria for Model Comparison
c(person("Christopher","Jackson", role=c("cre","aut"),
email="chris.jackson@mrc-bsu.cam.ac.uk",
comment="package design and programming"),
person("Gerda","Claeskens", role="aut",
email="gerda.claeskens@kuleuven.be",
comment="method development and design advice"),
person("Howard","Thom",email="howard.thom@bristol.ac.uk", role="ctb")
)
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).
stats, numDeriv, mvtnorm, ggplot2, scales, survival, tensor,
abind
knitr, rmarkdown, testthat, msm(>= 1.6.6), flexsurv, sn,
gapminder, GGally
Christopher Jackson [cre, aut] (package design and programming),
Gerda Claeskens [aut] (method development and design advice),
Howard Thom [ctb]