Description: This package builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum. To see the version history and download the bleeding-edge version (at your own risk!), see the project website at code.google.com below.  See the other links for PDF files describing numerical and exact analytical methods for determining instantaneous frequency, some examples of signals processed with this package, and some examples of the ensemble empirical mode decomposition method.

Packaged: 2015-01-21 15:09:55 UTC; dantayaga

Description: Builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum.
Maintainer: Daniel C. Bowman <danny.c.bowman@gmail.com>
Packaged: 2016-05-18 02:01:44 UTC; dantayaga

Imports: EMD (>= 1.5.5), fields (>= 6.7.6), spatstat.geom,
spatstat.core, spatstat.linnet, spatstat (>= 2.0-0)
Packaged: 2021-03-26 02:56:53 UTC; dantayaga

Packaged: 2022-11-30 02:37:43 UTC; dantayaga

Title: hierarchical Pooled Optimal Design
Maintainer: Wei E. Liang <liang1@usc.edu>
Description: Based on hierarchical modeling, this package provides a
few practical functions to find and present the optimal designs

Title: Individual-Level, Summary-Level and Single-Step Bayesian
person("Lilin", "Yin", email = "ylilin@163.com",
person("Haohao", "Zhang", email = "haohaozhang@whut.edu.cn",
person("Xiaolei", "Liu", email = "xiaoleiliu@mail.hzau.edu.cn",
Description: A user-friendly tool to fit Bayesian regression models. It can fit 3 types of Bayesian models using individual-level, summary-level, and individual plus pedigree-level (single-step) data for both Genomic prediction/selection (GS) and Genome-Wide Association Study (GWAS), it was designed to estimate joint effects and genetic parameters for a complex trait, including:
(1) fixed effects and coefficients of covariates,
(2) environmental random effects, and its corresponding variance,
(6) genomic estimated breeding values (GEBV) for both genotyped and non-genotyped individuals,
(8) phenotype/genetic variance explained (PVE) for single or multiple SNPs,
(9) posterior probability of association of the genomic window (WPPA),
(10) posterior inclusive probability (PIP).
The functions are not limited, we will keep on going in enriching it with more features.
Depends: R (>= 3.3.0), bigmemory, Matrix
LinkingTo: Rcpp, RcppArmadillo (>= 0.9.600.0.0), RcppProgress, BH,