Author: Andreas Mayr, Benjamin Hofner, Leonie Weinhold, Matthias Schmid
Description: Implements boosting beta regression for potentially high-dimensional data (Mayr et al., 2018 <doi:10.1093/ije/dyy093>).
The 'betaboost' package uses the same parametrization as 'betareg' (Cribari-Neto and Zeileis, 2010 <doi:10.18637/jss.v034.i02>) to make results directly comparable.
The underlying algorithms are implemented via the R add-on packages 'mboost' (Hofner et al., 2014 <doi:10.1007/s00180-012-0382-5>) and
Suggests: gamlss.dist, knitr, rmarkdown, betareg
URL: For source code, development versions and issue tracker see

Author: Telmo M Silva Filho and Meelis Kull
Maintainer: Telmo M Silva Filho <tmfilho@gmail.com>
Description: Fit beta calibration models and obtain calibrated probabilities from

Title: Estimators of Multiple-Site Dissimilarity Indices for the
Author: Mathieu Fortin, Anna Kondratyeva, Rosalinde Van Couwenberghe
Description: Implement the multiple-site dissimilarity indices of Simpson, Sorensen
and nestedness, which can be used to assess the beta diversity of a population.
These indices were adapted from those developed by Baselga (2010)
size-independent. All the details behind the calculation and estimation of
these adapted indices can be found in Fortin et al. (2020) <doi:10.1111/geb.13080>.
Packaged: 2020-06-02 17:14:33 UTC; donenrique

Packaged: 2020-07-24 13:40:49 UTC; donenrique

Title: Detecting rare variants for quantitative traits using nuclear
Description: To detecting rare variants for quantitative traits using
nuclear families, the linear combination methods are proposed
using the estimated regression coefficients from the multiple
regression and regularized regression as the weights.

Title: Functions for Working with the Beta Probability Density
Description: Package providing a number of functions for working with the Two- and Four-
parameter Beta distribution, including alternative parameterizations and calculation
of moments. Includes functions for estimating classification accuracy and diagnostic
performance statistics using what's known as the Livingston and Lewis approach in the