Bayesian analysis of luminescence data and C-14 age estimates. Bayesian models are based on the following publications: Combes, B. & Philippe, A. (2017) <doi:10.1016/j.quageo.2017.02.003> and Combes et al (2015) <doi:10.1016/j.quageo.2015.04.001>. This includes, amongst others, data import, export, application of age models and palaeodose model.
stats, graphics, grDevices, methods, hexbin, KernSmooth, rjags
(>= 4-6), Luminescence (>= 0.8.2), ArchaeoPhases

stats, graphics, grDevices, methods, hexbin, KernSmooth, rjags
(>= 4-10), runjags (>= 2.0.4), Luminescence (>= 0.8.2),
ArchaeoPhases (>= 1.5)

Claire Christophe [aut],
Anne Philippe [aut, cre] (<https://orcid.org/0000-0002-5331-5087>),
Sebastian Kreutzer [aut] (<https://orcid.org/0000-0002-0734-2199>),
Guillaume Guerin [aut] (<https://orcid.org/0000-0001-6298-5579>),
Frederik Harly Baumgarten [ctb]
(<https://orcid.org/0000-0002-4374-5948>)
c(
person("Claire", "Christophe", role = c("aut")),
person("Anne", "Philippe", role = c("aut", "cre"), email = "anne.philippe@univ-nantes.fr",  comment = c(ORCID = "0000-0002-5331-5087")),
person("Sebastian", "Kreutzer", role = "aut", comment = c(ORCID = "0000-0002-0734-2199")),
person("Guillaume", "Guerin", role = "aut", comment = c(ORCID = "0000-0001-6298-5579")),
person("Frederik Harly", "Baumgarten", role = "ctb", comment = c(ORCID = "0000-0002-4374-5948")))
methods, hexbin (>= 1.28.2), KernSmooth (>= 2.23), rjags (>=
4-12), runjags (>= 2.2.0), Luminescence (>= 0.9.18),
ArchaeoPhases (>= 1.5)

Bayesian Analysis of Significantly Mutated Genes in Cancer
This R package is the software implementation of the
algorithm BaySIC, a Bayesian approach toward analysis of
significantly mutated genes in cancer data.

c(person("Achal", "Lama", role = c("aut", "cre"),
email = "achal.lama@icar.gov.in"),
person("Kn", "Singh", role = "aut"),
person("Bishal", "Gurung", role = "aut",
email = "Bishal.Gurung@icar.gov.in"))
Achal Lama [aut, cre],
Kn Singh [aut],
Bishal Gurung [aut]
The Autoregressive Integrated Moving Average (ARIMA) model is very popular univariate time series model. Its application has been widened by the incorporation of exogenous variable(s) (X) in the model and modified as ARIMAX by Bierens (1987) <doi:10.1016/0304-4076(87)90086-8>. In this package we estimate the ARIMAX model using Bayesian framework.