Likelihood inference in Gaussian copula marginal
        regression models.

An implementation of the International Bureau of Weights and Measures (BIPM) generalized consensus estimators used to assign the reference value in a key comparison exercise. This can also be applied to any interlaboratory study. Given a set of different sources, primary laboratories or measurement methods this package provides an evaluation of the variance components according to the selected statistical method for consensus building. It also implements the comparison among different consensus builders and evaluates the participating method or sources against the consensus reference value. Based on a diverse set of references, DerSimonian-Laird (1986) <doi:10.1016/0197-2456(86)90046-2>, for a complete list of references look at the reference section in the package documentation.

Data sets used in the book "R Graphics Cookbook" by Winston
    Chang, published by O'Reilly Media.

Run a Gibbs sampler for a multivariate Bayesian sparse group selection model with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on the traits. This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) <doi:10.1002/sim.8855>.

Easy wrangling and model-free analysis of
    microbial growth curve data, as commonly output by plate readers.
    Tools for reshaping common plate reader outputs into 'tidy' formats and
    merging them with design information, making data easy to work with using 
    'gcplyr' and other packages. Also streamlines common growth curve
    processing steps, like smoothing and calculating derivatives, and
    facilitates model-free characterization and analysis of growth data.
    See methods at <https://mikeblazanin.github.io/gcplyr/>.

Analyze the default risk of credit portfolios. Commonly known models, 
		like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings.
		The portfolio loss distribution can be achieved either by simulation or analytically 
		in case of the classic CreditRisk+ model. Models are only implemented to respect losses
		caused by defaults, i.e. migration risk is not included. The package structure is kept
		flexible especially with respect to distributional assumptions in order to quantify the
		sensitivity of risk figures with respect to several assumptions. Therefore the package
		can be used to determine the credit risk of a given portfolio as well as to quantify
		model sensitivities.