Title: Providing Fast and Flexible Functions for Distance Correlation
Authors@R: c(person("Dominic", "Edelmann", email = "dominic.edelmann@dkfz-heidelberg.de", role = c("aut", "cre")), person("Jochen", "Fiedler", email = "jochen.fiedler@itwm.fraunhofer.de", role = c("aut")))
Description: Providing functionalities for calculating association measures and dependence tests based on distance covariance and distance correlation.
Depends: Rdpack, R (>= 3.3.3), Rcpp (>= 0.11.0), Rfast
Imports: ggplot2, rlc, pheatmap, Hmisc, stats

Imports: ggplot2, pheatmap, Hmisc, stats

Title: A Fast Implementation of Distance Covariance
Description: Efficient methods for computing distance covariance and relevant statistics. See Székely et al.(2007) <doi:10.1214/009053607000000505>; Székely and Rizzo (2013) <doi:10.1016/j.jmva.2013.02.012>; Székely and Rizzo (2014) <doi:10.1214/14-AOS1255>; Huo and Székely (2016) <doi:10.1080/00401706.2015.1054435>.
Packaged: 2020-06-24 12:39:10 UTC; weiqianghang

Packaged: 2020-06-25 19:00:18 UTC; weiqianghang

Title: Utility Functions for Davidian Curves
Description: A Davidian curve defines a seminonparametric density, whose flexibility can be tuned
by a parameter. Since a special case of a Davidian curve is the standard normal density, Davidian curves
can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001)
<doi:10.1111/j.0006-341X.2001.00795.x>. This package provides the density function, the gradient of
the loglikelihood and a random generator for Davidian curves.

Description: A Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by
easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density,
Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001)

Packaged: 2020-11-03 19:19:27 UTC; oguzhanogreden

Title: Decision Curve Analysis for Model Evaluation
Description: Diagnostic and prognostic models are typically evaluated with
measures of accuracy that do not address clinical consequences.
Decision-analytic techniques allow assessment of clinical outcomes,
but often require collection of additional information may be
cumbersome to apply to models that yield a continuous result. Decision
curve analysis is a method for evaluating and comparing prediction
models that incorporates clinical consequences, requires only the data
set on which the models are tested, and can be applied to models that
have either continuous or dichotomous results. See the following references
Author: Daniel D. Sjoberg [aut, cre, cph]
Maintainer: Daniel D. Sjoberg <danield.sjoberg@gmail.com>