Description: Contains techniques for mining large and high-dimensional data
sets by using the concept of Intrinsic Dimension (ID). Here the ID is

not necessarily an integer. It is extended to fractal dimensions. And

Author: Luis F. Duque <lfduquey@gmail.com>
Maintainer: Luis F. Duque <lfduquey@gmail.com>
Description: Computes characteristics of independent rainfall events (duration, total rainfall depth, and intensity)
extracted from a sub-daily rainfall time series based on the inter-event time definition (IETD) method. To have a
reference value of IETD, it also analyzes/computes IETD values through three methods: autocorrelation analysis, the
average annual number of events analysis, and coefficient of variation analysis. Ideal for analyzing the sensitivity
of IETD to characteristics of independent rainfall events.

Title: Robust Analysis for Absolute Abundance in Microbiome
Description: A novel approach to make inference on the association of covariates with the absolute abundance (AA) of 'microbiome' in an ecosystem. It can be also directly applied to relative abundance (RA) data to make inference on AA because the ratio of two RA is equal ratio of their AA. This algorithm can estimate and test the associations of interest while adjusting for potential 'confounders'. The estimates of this method have easy interpretation like a typical regression analysis. High-dimensional covariates are handled with regularization and it is implemented by parallel computing. This algorithm finds optimal reference 'taxa/OTU (Operational Taxonomic Unit)/ASV (Amplicon Sequence Bariant)' and uses permutation to control FDR (False Discovery Rate) as described in Zhigang Li, et al. (2020) <arXiv:1909.10101v3>, Zhigang Li, et al. (2018) <doi:10.1007/s12561-018-9219-2>.

Description: A novel approach to make inference on the association of covariates with the absolute abundance (AA) of 'microbiome' in an ecosystem. It can be also directly applied to relative abundance (RA) data to make inference on AA (even if AA data is not available) because the ratio of two RA is equal ratio of their AA. This algorithm can estimate and test the associations of interest while adjusting for potential 'confounders'. The estimates of this method have easy interpretation like a typical regression analysis. High-dimensional covariates are handled with regularization and it is implemented by parallel computing. This algorithm finds optimal reference 'taxa/OTU (Operational Taxonomic Unit)/ASV (Amplicon Sequence Bariant)' and uses permutation to control FDR (False Discovery Rate).
Imports: qlcMatrix (>= 0.9.7), mathjaxr (>= 1.0-1), methods (>= 3.3.0),