Description: Generalized UniFrac distance for comparing microbial
communities. Permutational multivariate analysis of variance
X-CRAN-Comment: Orphaned on 2014-12-07 as maintainer address

Description: Generalized UniFrac distances for comparing microbial communities. Permutational multivariate analysis of variance using multiple distance matrices.
Depends: vegan, ape, matrixStats, stats, R (>= 3.1.0)

Title: Generalized UniFrac Distances and Distance-Based Multivariate
Description: Implementation of generalized UniFrac distances for comparing microbial communities and three extensions to the PERMANOVA procedure.  The three extensions are: (1) PERMANOVA using the Freedman-Lane permutation scheme; (2) PERMANOVA omnibus test using multiple matrices; and (3) Analytical approach to approximating PERMANOVA p-value.
Depends: matrixStats, Matrix, vegan, ape, parallel, stats, utils, R (>=

Title: Generalized UniFrac Distances, Distance-Based Multivariate
Methods and Feature-Based Univariate Methods for Microbiome
Description: A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances,  Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and  (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based permutation tests for differential abundance analysis of zero-inflated compositional data.
Imports: Rcpp (>= 0.12.13), matrixStats, Matrix, ape, parallel, stats,

Author: Jun Chen, Xianyang Zhang, Huijuan Zhou
Description: A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances,  Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and  (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.
Imports: Rcpp (>= 0.12.13), vegan, ggplot2, matrixStats, Matrix, ape,
parallel, stats, utils, statmod, rmutil, dirmult, MASS,