Packaged: 2020-12-06 16:37:08 UTC; annephilippe

person("Anne", "Philippe", role = c("aut", "cre"), email = "anne.philippe@univ-nantes.fr",  comment = c(ORCID = "0000-0002-5331-5087")),
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")))
Imports: methods, hexbin (>= 1.28.2), KernSmooth (>= 2.23), rjags (>=

Title: Bayesian Analysis of Significantly Mutated Genes in Cancer
Maintainer: Nicholas B. Larson <larson.nicholas@mayo.edu>
Depends: R (>= 2.10), rjags, fields, poibin
Description: This R package is the software implementation of the
algorithm BaySIC, a Bayesian approach toward analysis of
significantly mutated genes in cancer data.

Title: Bayesian Estimation of ARIMAX Model
Authors@R: c(person("Achal", "Lama", role = c("aut", "cre"),
person("Bishal", "Gurung", role = "aut",
Description: 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.

Title: Bayesian Boundary Detection in Images
Description: It estimate the boundaries in an image adaptively to the smoothness
level of the boundary.The method is applicable to images with binary noise,

Title: Bayesian Inference for Image Boundaries
Description: Provides tools for carrying out a Bayesian analysis of image boundaries.  Functions are provided
for both binary (Bernoulli) and continuous (Gaussian) images.  Examples, along with an interactive shiny function
illustrate how to perform simulations, analyze custom data, and plot estimates and credible intervals.

Imports: Rcpp (>= 0.12.5), shiny, mritc, png, jpeg

Title: Bayesian Estimation of Bivariate Volatility Model
Author: Achal Lama, Girish K Jha, K N Singh and Bishal Gurung
Description: The Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models are used for modelling the volatile multivariate data sets. In this package a variant of MGARCH called BEKK (Baba, Engle, Kraft, Kroner) proposed by Engle and Kroner (1995) <http://www.jstor.org/stable/3532933> has been used to estimate the bivariate time series data using Bayesian technique.