Alignment of Phonetic Sequences Using the 'ALINE' Algorithm
Functions are provided to calculate the 'ALINE' Distance between words. The score is based on phonetic features represented using the Unicode-compliant International Phonetic Alphabet (IPA). Parameterized features weights are used to determine the optimal alignment and functions are provided to estimate optimum values.

c(person('Sean', 'Downey', role = c("aut","cre"),email="sean@codexdata.com"),
person('Guowei', 'Sun', role = "aut",email="gwsun@umd.edu"),
person('Greg', 'Kondrak', role = "cph", comment="Copyright holder of original ALINE algorithm C++ source code included in package."))
Functions are provided to calculate the 'ALINE' Distance between words as per
(Kondrak 2000) and (Downey, Hallmark, Cox, Norquest, & Lansing, 2008,
<doi:10.1080/09296170802326681>). The score is based on phonetic features represented
using the Unicode-compliant International Phonetic Alphabet (IPA). Parameterized
features weights are used to determine the optimal alignment and functions are
provided to estimate optimum values using a genetic algorithm and supervised
learning. See (Downey, Sun, and Norquest 2017,
<https://journal.r-project.org/archive/2017/RJ-2017-005/index.html>.
Sean Downey [aut, cre],
Guowei Sun [aut],
Greg Kondrak [cph] (Copyright holder of original ALINE algorithm C++
source code included in package.)

Pre-Processing XY Data from Experimental Methods
c(person(given = "Nicolas",
family = "Frerebeau",
role = c("aut", "cre"),
email = "nicolas.frerebeau@u-bordeaux-montaigne.fr",
comment = c(ORCID = "0000-0001-5759-4944",
affiliation = "Université Bordeaux Montaigne")),
person(given = "Brice",
family = "Lebrun",
role = "ctb",
email = "brice.lebrun@u-bordeaux-montaigne.fr",
comment = c(ORCID = "0000-0001-7503-8685")))
A lightweight, dependency-free toolbox for pre-processing XY data
from experimental methods (i.e. any signal that can be measured along a
continuous variable). This package provides methods for baseline estimation
and correction, smoothing, normalization, integration and peaks detection.
Baseline correction methods includes polynomial fitting as described in
Lieber and Mahadevan-Jansen (2003) <doi:10.1366/000370203322554518>,
Rolling Ball algorithm after Kneen and Annegarn (1996)
<doi:10.1016/0168-583X(95)00908-6>, SNIP algorithm after Ryan et al.
(1988) <doi:10.1016/0168-583X(88)90063-8>, 4S Peak Filling after
Liland (2015) <doi:10.1016/j.mex.2015.02.009> and more.
knitr, Matrix, rmarkdown, testthat (>= 3.0.0)
'AllGenerics.R' 'alkahest-package.R' 'baseline.R' 'data.R'
'helpers.R' 'integrate.R' 'peaks.R' 'replace.R' 'resample.R'
'rescale.R' 'signal.R' 'smooth.R' 'windows.R'
Nicolas Frerebeau [aut, cre] (<https://orcid.org/0000-0001-5759-4944>,
Université Bordeaux Montaigne),
Brice Lebrun [ctb] (<https://orcid.org/0000-0001-7503-8685>)