Packaged: 2016-09-22 12:26:30 UTC; trimborns

Imports: SparseGrid, numDeriv, VineCopula (>= 2.0.5), methods, stats,
Packaged: 2016-10-01 23:28:34 UTC; trimborns

Depends: R (>= 1.9.0), copula (>= 0.999-15), foreach, parallel, doSNOW,
Description: Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is implemented which supports all of the individual tests in the package, e.g. Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>. Estimation methods for the margins are provided and all the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. For reproducibility of results, the functions support the definition of seeds. Also all the tests support automatized parallelization of the bootstrapping tasks. The package provides an interface to perform new GoF tests by submitting the test statistic.

Imports: foreach, parallel, doSNOW, R.utils, SparseGrid, numDeriv,
VineCopula (>= 2.0.5), methods, stats, MASS, utils, yarrr,

Title: Goodness of Fit Noise Analysis Using Monte Carlo Techniques
Author: Joseph G Kreke, PhD; Harris, Inc.
Sangeet Khemlani, PhD; Naval Research Laboratory.
Greg Trafton, PhD; Naval Research Laboratory.
Maintainer: Joseph G Kreke <jkreke2@gmail.com>
Description: Goodness-of-fit metrics, such as R-Squared, RMSE, etc., share a sensitivity to noise, dependent on the degrees of freedom.  Some metrics, such as R-Squared, decrease with increasing dof and some, such as RMSE, increase with increasing dof.  This package calculates the noise baseline (ceiling) by random sampling, calculating the metric’s value for each sample and counting the number of samples below a desired level, 95% by default. If one’s measure is above (below) the calculation corresponding to the desired level, then the measurement is distinguishable from noise.  In addition, the ratio of the measurement to the calculated level provides a way to compare measurements of different degrees of freedom.
Depends: R (>= 3.3.1), ggplot2, grDevices, scales, stats