Title: PARAFAC Analysis of Fluorescence Excitation-Emission Matrices
Authors@R: c(person('Timur', 'Labutin', role = 'aut'),
person('Ivan', 'Krylov', role = 'cre', email = 'ikrylov@laser.chem.msu.ru'))
Imports: multiway (>= 1.0-4), pracma, lattice
Description: Perform parallel factor analysis (PARAFAC: Harshman, 2005)
<doi:10.1121/1.1977523>) on fluorescence excitation-emission matrices
(FEEMs): handle scattering signal (Bahram, 2007) <doi:10.1002/cem.978>
and inner filter effect (Kothawala, 2013) <doi:10.4319/lom.2013.11.616>,
scale the dataset, fit the model; perform split-half validation
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

Authors@R: c(person('Ivan', 'Krylov', role = c('aut', 'cre'), email =
'ikrylov@laser.chem.msu.ru'), person('Timur', 'Labutin', role = 'ths'))
Description: Perform parallel factor analysis (PARAFAC: Hitchcock, 1927)
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

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

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

'ikrylov@laser.chem.msu.ru'), person('Timur', 'Labutin', role = 'ths'),
person('Anastasia', 'Drozdova', role = 'rev'))
Imports: multiway (>= 1.0-4), CMLS, pracma, lattice, parallel, Matrix
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.