Bayesian model and associated tools for generating estimates of 
    total naloxone kit numbers distributed and used from naloxone kit orders 
    data. Provides functions for generating simulated data of naloxone kit use
    and functions for generating samples from the posterior.

Included are two main interfaces, bentcable.ar() and
	bentcable.dev.plot(), for fitting and diagnosing bent-cable
	regressions for autoregressive time-series data (Chiu and
	Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time
	series or otherwise - Chiu, Lockhart and Routledge 2006,
	<doi:10.1198/016214505000001177>). Some components in the package
	can also be used as stand-alone functions. The bent cable
	(linear-quadratic-linear) generalizes the broken stick
	(linear-linear), which is also handled by this package. Version
	0.2 corrected a glitch in the computation of confidence intervals
	for the CTP. References that were updated from Versions 0.2.1 and
	0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved
	robustness of the error-message producing mechanism. Version 0.3.1
	improves the NAMESPACE file of the package. It is the author's
	intention to distribute any future updates via GitHub.

Preprocessing tools and biodiversity measures 
    (species abundance, species richness, population heterogeneity and 
    sensitivity) for analysing marine benthic data. See Van Loon et al.
    (2015) <doi:10.1016/j.seares.2015.05.002> for an application of 
    these tools.

Using a Bayesian estimation procedure, this package fits linear quantile regression models such as linear quantile models, linear quantile mixed models, quantile regression joint models for time-to-event and longitudinal data. The estimation procedure is based on the asymmetric Laplace distribution and the 'JAGS' software is used to get posterior samples (Yang, Luo, DeSantis (2019) <doi:10.1177/0962280218784757>).

Bayesian analysis for exponential random graph models using
    advanced computational algorithms. More information can be found at:
    <https://acaimo.github.io/Bergm/>.