Imports: Rcpp, Rdpack, stats, utils, pracma, flare

Description: An implementation of four stochastic methods of integrating in R, including:
1. Stochastic Point Method (or Monte Carlo Method);
It can be used to estimate one-dimension or multi-dimension integration by Monte Carlo methods. And the estimated variance (precision) is given.
Packaged: 2018-04-17 11:07:42 UTC; dujinhong

Packaged: 2018-09-23 01:28:54 UTC; dujinhong

Title: Stable Isotope Bayesian Ellipses in R
Imports: rjags, mnormt, hdrcde, graphics, stats, grDevices
Author: Andrew Jackson and Andrew Parnell
Description: Fits bi-variate ellipses to stable isotope data using Bayesian
inference with the aim being to describe and compare their isotopic
Packaged: 2016-01-27 11:42:48 UTC; andrewjackson

Packaged: 2016-02-16 23:13:10 UTC; andrewjackson

Packaged: 2016-08-03 21:05:53 UTC; andrewjackson

Description: Fits bivariate ellipses to stable isotope data using Bayesian
Packaged: 2015-08-13 13:14:06 UTC; andrewjackson

Packaged: 2017-02-21 20:57:46 UTC; andrewjackson

Imports: hdrcde, graphics, grDevices, mnormt, rjags, spatstat, stats,
Packaged: 2017-03-15 15:48:32 UTC; andrewjackson

Imports: hdrcde, graphics, grDevices, mnormt, rjags, spatstat.utils,
stats, viridis, tidyr, dplyr, ggplot2, magrittr
Packaged: 2017-04-28 14:12:45 UTC; andrewjackson

Imports: coda, ellipse, hdrcde, graphics, grDevices, mnormt, rjags,
spatstat, spatstat.utils, stats, viridis, tidyr, dplyr,

Imports: hdrcde, graphics, grDevices, mnormt, rjags, spatstat,
spatstat.utils, stats, tidyr, dplyr, ggplot2, magrittr, purrr
Suggests: coda, ellipse, knitr, rmarkdown, viridis

Imports: hdrcde, graphics, grDevices, mnormt, rjags, spatstat.geom,
Suggests: coda, ellipse, knitr, rmarkdown, tidyverse, viridis

Title: Systematic Identification of Bimodally Expressed Genes Using
Description: Provides models to identify bimodally expressed genes from
RNAseq data based on the Bimodality Index. SIBERG models the  RNAseq data in
the finite mixture modeling framework and incorporates mechanisms  for
dealing with RNAseq normalization. Three types of mixture models are
implemented,  namely, the mixture of log normal, negative binomial, or
generalized poisson distribution. See Tong et al. (2013)