Suggests: knitr, testthat, graphite, NFPdata, rmarkdown

person("Yang", "Cao",email = "caoyang.name@gmail.com",role = c("aut","cre" )),

Title: The Standard Distribution Functions for the 3D NFW Profile
Description: Density, distribution function, quantile function and random generation for the 3D Navarro, Frenk & White (NFW) profile. For details see Robotham & Howlett (2018) <arXiv:1805.09550>.

Title: Bayesian Variable Selection for SNP Data using Normal-Gamma
Authors@R: c(person("Abdulaziz", "Alenazi", role = c("aut", "cre"), email = "a.alenazi@nbu.edu.sa"))
Description: Posterior distribution of case-control fine-mapping. Specifically, Bayesian variable selection for single-nucleotide polymorphism (SNP) data using the normal-gamma prior. Alenazi A.A., Cox A., Juarez M,. Lin W-Y. and Walters, K. (2019) Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies, Genetic Epidemiology. <doi:10.1002/gepi.22213>.

Title: Interactive 3D Visualization of Molecular Structures
person("Niels", "van der Velden", role = c("aut", "cre"), email = "n.s.j.vandervelden@gmail.com"),
person("Alexander", "Rose", role = "cph", comment = 'NGL.js library'))
Maintainer: Niels van der Velden <n.s.j.vandervelden@gmail.com>
Description: Provides an 'htmlwidgets' <https://www.htmlwidgets.org/> interface to 'NGL.js' <http://nglviewer.org/ngl/api/>.
'NGLvieweR' can be used to visualize and interact with protein databank ('PDB') and structural files in R and Shiny applications.
It includes a set of API functions to manipulate the viewer after creation in Shiny.
Imports: htmlwidgets, magrittr, tools, shiny
Suggests: knitr, webshot, markdown, rmarkdown
Packaged: 2021-05-31 10:37:06 UTC; nvandervelden
Author: Niels van der Velden [aut, cre],

Title: Non-Gaussian State-Space with Exact Marginal Likelihood
Author: Thiago Rezende dos Santos <thiagords@est.ufmg.br>, Dani Gamerman <dani@im.ufrj.br>, Glaura da Conceicao Franco <glaura@est.ufmg.br>
Maintainer: T. R. dos Santos <thiagords@est.ufmg.br>
Description: Due to a large quantity of non-Gaussian time series and reliability data, the R-package non-Gaussian state-space with exact marginal likelihood is useful for modeling and forecasting non-Gaussian time series and reliability data via non-Gaussian state-space models with the exact marginal likelihood easily, see Gamerman, Santos and Franco (2013) <doi:10.1111/jtsa.12039> and Santos, Gamerman and Franco (2017) <doi:10.1109/TR.2017.2670142>. The package gives codes for formulating and specifying the non-Gaussian state-space models in the R language. Inferences for the parameters of the model can be made under the classical and Bayesian. Furthermore, prediction, filtering, and smoothing procedures can be used to perform inferences for the latent parameters. Applications include, e.g., count, volatility, piecewise exponential, and software reliability data.
Imports: mvtnorm, fields, compiler, dlm, car, interp
Collate: 'FilteringF.r' 'gridfunction.r' 'GridP.r' 'LikeF.r' 'LikeF2.r'