Generalized competing event model based on Cox PH model and Fine-Gray model.
    This function is designed to develop optimized risk-stratification methods for competing
    risks data, such as described in:
    1. Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F,
    and Mell LK (2014) <DOI:10.1016/j.ijrobp.2014.03.047>.
    2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose
    BS, Shen H, Vaida F, Murphy JD, and Mell LK (2016) <DOI:10.1200/JCO.2015.65.0739>.
    3. Lunn, Mary, and Don McNeil (1995) <DOI:10.2307/2532940>.

Scrapes Google Citation pages and creates data frames of 
    citations over time.

Provides a variety of functions to analyze and model
    geostatistical count data with Gaussian copulas, including
 1) data simulation and visualization; 
 2) correlation structure assessment (here also known as the Normal To Anything); 
 3) calculate multivariate normal rectangle probabilities; 
 4) likelihood inference and parallel prediction at predictive locations.
 Description of the method is available from: Han and DeOliveira (2018) <doi:10.18637/jss.v087.i13>.

Estimation of covariance matrices as solutions of 
             continuous time Lyapunov equations. 
             Sparse coefficient matrix and diagonal noise are estimated 
             with a proximal gradient 
             method for an l1-penalized loss minimization problem.
             Varando G, Hansen NR (2020) <arXiv:2005.10483>.

Orders panels in scatterplot matrices and parallel coordinate
 displays by some merit index. Package contains various indices of merit,
 ordering functions, and enhanced versions of pairs and parcoord which
 color panels according to their merit level.

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.