Packaged: 2018-03-17 04:47:19 UTC; Kyun-SeopBae

Packaged: 2018-04-16 12:00:56 UTC; Kyun-SeopBae

Packaged: 2018-05-17 04:45:28 UTC; Kyun-SeopBae

Packaged: 2018-06-20 21:06:26 UTC; Kyun-SeopBae

Packaged: 2018-07-10 10:22:21 UTC; Kyun-SeopBae

Description: Conduct a noncompartmental analysis with industrial strength.

Packaged: 2020-05-06 23:59:03 UTC; Kyun-SeopBae

Packaged: 2021-06-10 03:56:44 UTC; Kyun-SeopBae

Packaged: 2021-07-15 02:21:44 UTC; Kyun-SeopBae

Packaged: 2022-02-26 02:21:37 UTC; Kyun-SeopBae

Packaged: 2022-06-15 03:32:51 UTC; Kyun-SeopBae

Packaged: 2022-07-14 23:48:44 UTC; Kyun-SeopBae

Title: Multivariate Lomax (Pareto Type II) and Its Related
Description: Implements calculation of probability density function, cumulative distribution function, equicoordinate quantile function and survival function, and random numbers generation for the following multivariate distributions: Lomax (Pareto Type II), generalized Lomax, Mardia’s Pareto of Type I, Logistic, Burr, Cook-Johnson’s uniform, F and Inverted Beta. See Tapan Nayak (1987) <doi:10.2307/3214068>.

Title: a Non-Parametric Statistical Significance Test for Rolling
Authors@R: c(person(given = "Josue M.", family = "Polanco-Martinez", role = c("aut", "cph", "cre"), email = "josue.m.polanco@gmail.com", comment = c(ORCID = "0000-0001-7164-0185")), person(given = "Jose L.", family = "Lopez-Martinez", role = c("ctb"), email = "jose.lopez@correo.uady.mx", comment = c(ORCID = "0000-0002-2489-7559")))
Maintainer: Josue M. Polanco-Martinez <josue.m.polanco@gmail.com>
Depends: R (>= 3.5.0), gtools, pracma, colorspace, doParallel
Description: Estimates and plots (as a heat map) the statistical significance of rolling window correlation coefficients, which is carried out through a non-parametric computing-intensive method. This method addresses the effects due to the multiple testing (inflation of the Type I error) when the statistical significance is estimated for the rolling window correlation coefficients. The method is based on Monte Carlo simulations by permuting one of the variables (dependent) under analysis and keeping fixed the other variable (independent). We improve the computational efficiency of this method to reduce the computation time through parallel computing. The 'NonParRolCor' package also provides examples with synthetic and real-life ecological time series to exemplify its use. Methods derived from R. Telford (2013) <https://quantpalaeo.wordpress.com/2013/01/04/> and J.M. Polanco-Martinez (2020) <doi:10.1016/j.ecoinf.2020.101163>.
Author: Josue M. Polanco-Martinez [aut, cph, cre]
Jose L. Lopez-Martinez [ctb] (<https://orcid.org/0000-0002-2489-7559>)