Anton Gagin and Igor Levin with contributions from Charles R. Hogg III
Bayesian Estimation of Incoherent Neutron scattering Backgrounds
In this package we implemented a Bayesian-statistics approach for
subtraction of incoherent scattering from neutron total-scattering data.
In this approach, the estimated background signal associated with
incoherent scattering maximizes the posterior probability, which combines
the likelihood of this signal in reciprocal and real spaces with the prior
that favors smooth lines.
DEoptim, aws, grid, ggplot2, reshape2, shiny, methods

Bayesian Estimation of Incoherent Neutron Scattering Backgrounds
We implemented a Bayesian-statistics approach for
subtraction of incoherent scattering from neutron total-scattering data.
In this approach, the estimated background signal associated with
incoherent scattering maximizes the posterior probability, which combines
the likelihood of this signal in reciprocal and real spaces with the prior
that favors smooth lines.

DEoptim, wmtsa, grid, ggplot2, reshape2, shiny, methods

We implemented a Bayesian-statistics approach for
subtraction of incoherent scattering from neutron total-scattering data.
In this approach, the estimated background signal associated with
incoherent scattering maximizes the posterior probability, which combines
the likelihood of this signal in reciprocal and real spaces with the prior
that favors smooth lines. The description of the corresponding approach
could be found at Gagin and Levin (2014) <DOI:10.1107/S1600576714023796>.

Benthic Biotic Indices Calculation from Composition Data
person("Tristan", "Cordier", email = "tristan.cordier@gmail.com",
role = c("aut", "cre"))
Set of functions to calculate Benthic Biotic Indices from
composition data, obtained whether from morphotaxonomic inventories or
sequencing data. Based on reference ecological weights publicly available for
a set of commonly used marine biotic indices, such as AMBI (A Marine Biotic Index, Borja et al., 2000) <doi:10.1016/S0025-326X(00)00061-8>
NSI (Norwegian Sensitivity Index) and ISI (Indicator Species Index) (Rygg 2013, <ISBN:978-82-577-6210-0>). It provides the ecological quality status of the samples based on each BBI as well as the normalized Ecological Quality Ratio.