knitr, dplyr, rmarkdown, testthat (>= 3.0.0)

Convenience functions for multivariate MCMC using univariate samplers, including slice sampler with stepout and shrinkage (Neal, 2003), and adaptive rejection sampler (Gilks and Wild, 1992).

Convenience Functions for Multivariate MCMC Using Univariate Samplers, including Slice Sampler with Stepout and Shrinkage (Neal, 2003), and Adaptive Rejection Sampler (Gilks and Wild, 1992).

Convenience functions for multivariate MCMC using univariate samplers including:
slice sampler with stepout and shrinkage (Neal, 2003),
adaptive rejection sampler (Gilks and Wild, 1992),
adaptive rejection Metropolis (Gilks et al 1995), and
univariate Metropolis with Gaussian proposal.

Convenience functions for multivariate MCMC using univariate samplers including:
slice sampler with stepout and shrinkage (Neal (2003) <DOI:10.1214/aos/1056562461>),
adaptive rejection sampler (Gilks and Wild (1992) <DOI:10.2307/2347565>),
adaptive rejection Metropolis (Gilks et al (1995) <DOI:10.2307/2986138>), and
univariate Metropolis with Gaussian proposal.

A Model-based Approach to Identify Binding Sites in CLIP-Seq
Data
Cross-linking immunoprecipitation coupled with
high-throughput sequencing (CLIP-Seq) has made it possible to
identify targeting sites of RNA-binding proteins in various
cell culture systems and tissue types on a genome-wide scale.
Here we present MiClip,a novel model-based approach to identify
high-confidence protein-RNA binding sites in CLIP-Seq datasets.
This approach assigns confidence value to each binding site on
a probabilistic basis. The MiClip package can be flexibly
applied to analyze both HITS-CLIP data and PAR-CLIP data.

Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Here we present MiClip,a novel model-based approach to identify high-confidence protein-RNA binding sites in CLIP-Seq datasets. This approach assigns confidence value to each binding site on a probabilistic basis. The MiClip package can be flexibly applied to analyze both HITS-CLIP data and PAR-CLIP data.