Estimate bivariate common mean vector under copula models with known correlation. In the current version, available copulas are the Clayton, Gumbel, Frank, Farlie-Gumbel-Morgenstern (FGM), and normal copulas. See Shih et al. (2019) <doi:10.1080/02331888.2019.1581782> and Shih et al. (2021) <under review> for details under the FGM and general copulas, respectively.

'Java' JAR files for the Apache Commons Mathematics Library for use by users and other packages.

Provides fast, easy feature extraction of human speech and model estimation
	     with hidden Markov models. Flexible extraction of phonetic features and their
	     derivatives, with necessary preprocessing options like feature standardization.
	     Communication can estimate supervised and unsupervised hidden Markov models with
	     these features, with cross validation and corrections for auto-correlation in
	     features. Methods developed in Knox and Lucas (2021) <doi:10.7910/DVN.8BTOHQ>.

Computing comorbidity indices and scores such as the weighted Charlson
  score (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser
  comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>)
  using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>).
  Australian and Swedish modifications of the Charlson Comorbidity Index are available
  as well (Sundararajan, 2004 <doi:10.1016/j.jclinepi.2004.03.012> and Ludvigsson, 2021 
  <doi:10.2147/CLEP.S282475>), together with different weighting algorithms for both
  the Charlson and Elixhauser comorbidity scores.

Using polygenic scores (PGS, or PRS/GRS for binary outcomes), 
    this package allows to investigate shared predisposition between different 
    conditions, and do fast association analysis, export plots and views
    of the PGS distribution using 'ggplot2' object.

Maps of Comoro Islands. Layers include the country coastline, each island coastline and administrative regions boundaries.

Comparison of two ROC curves through the methodology proposed by Ana C. Braga.