Title: Robust Inference for Absolute Abundance in Microbiome Analysis
parallel (>= 3.3.0), doParallel (>= 1.0.11), future (>=

Description: A robust 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. False discovery rate is automatically controlled by this approach.
Imports: qlcMatrix (>= 0.9.7), methods (>= 3.3.0), mathjaxr (>= 1.0-1),
doParallel (>= 1.0.11), future (>= 1.12.0), glmnet, stats

Description: IFAA is a robust 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. High-dimensional covariates are handled with regularization. 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. False discovery rate is automatically controlled by this approach. Zeros do not need to be imputed by a positive value for the analysis. The IFAA package also offers the 'MZILN' function for estimating and testing associations of abundance ratios with covariates.

Description: This package offers a robust 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. High-dimensional covariates are handled with regularization. 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. False discovery rate is automatically controlled by this approach. Zeros do not need to be imputed by a positive value for the analysis. The IFAA package also offers the 'MZILN' function for estimating and testing associations of abundance ratios with covariates.
parallel (>= 3.3.0), doParallel (>= 1.0.11), parallelly ,
glmnet, stats, utils, SummarizedExperiment, stringr, S4Vectors,
biocViews: Software, Technology, Sequencing, Microbiome, Regression