c(person('Ivan', 'Krylov', role = c('aut', 'cre'), email =
'ikrylov@laser.chem.msu.ru'), person('Timur', 'Labutin', role = 'ths'))
Perform parallel factor analysis (PARAFAC: Hitchcock, 1927)
<doi:10.1002/sapm192761164> on fluorescence excitation-emission
matrices (FEEMs): handle scattering signal and inner filter
effect, scale the dataset, fit the model; perform split-half
validation or jack-knifing. A modified approach called
"randomised split-half" is also available. The package has a low
dependency footprint (only two direct dependencies not in core
or recommended; four total non-core/recommended dependencies)
and has been tested on a wide range of R versions (including R
as old as 3.3.3 from Debian Stretch).
Ivan Krylov [aut, cre],
Timur Labutin [ths]

multiway (>= 1.0-4), pracma, lattice, parallel

multiway (>= 1.0-4), pracma, lattice, parallel, Matrix

c(person('Ivan', 'Krylov', role = c('aut', 'cre'), email =
'ikrylov@laser.chem.msu.ru'), person('Timur', 'Labutin', role = 'ths'),
person('Anastasia', 'Drozdova', role = 'rev'))
multiway (>= 1.0-4), CMLS, pracma, lattice, parallel, Matrix
Perform parallel factor analysis (PARAFAC: Hitchcock, 1927)
<doi:10.1002/sapm192761164> on fluorescence excitation-emission
matrices: handle scattering signal and inner filter effect, scale
the dataset, fit the model; perform split-half validation or
jack-knifing. Modified approaches such as Whittaker interpolation,
randomised split-half, and fluorescence and scattering model
estimation are also available. The package has a low dependency
footprint and has been tested on a wide range of R versions.
Ivan Krylov [aut, cre],
Timur Labutin [ths],
Anastasia Drozdova [rev]

Implements discrete time deterministic and stochastic age-structured population dynamics models described in Erguler and others (2016) <doi:10.1371/journal.pone.0149282> and Erguler and others (2017) <doi:10.1371/journal.pone.0174293>.

Christian E. Galarza <cgalarza88@gmail.com> and Victor H. Lachos <hlachos@ime.unicamp.br>
It provides the density, distribution function, quantile function,
random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for
the three parameter Asymmetric Laplace Distribution defined
in Koenker and Machado (1999) useful for quantile regression.