Carlo Albert <carlo.albert@eawag.ch> and Sören Vogel <soeren.vogel@posteo.ch>

Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood.

Carlo Albert <carlo.albert@eawag.ch>, Sören Vogel <soeren.vogel@posteo.ch>, Oliver Jakoby <oliver.jakoby@rifcon.de> and Alexander Singer <alexander.singer@rifcon.de>

Carlo Albert <carlo.albert@eawag.ch>, Sören Vogel <soeren.vogel@posteo.ch>, Oliver Jakoby <oliver.jakoby@rifcon.de>, Alexander Singer <alexander.singer@rifcon.de> and Dirk Nickisch <dirk.nickisch@rifcon.de>

knitr, rmarkdown, adaptMCMC, xlsx, drc, testthat

Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R. (2016) <doi:10.1371/journal.pcbi.1004978>).

Generalized UniFrac distance for comparing microbial
communities. Permutational multivariate analysis of variance
using multiple distance matrices.
Orphaned on 2014-12-07 as maintainer address
<chenjun@mail.med.upenn.edu> bounced.

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

Generalized UniFrac Distances and Distance-Based Multivariate
Analysis of Variance
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.
matrixStats, Matrix, vegan, ape, parallel, stats, utils, R (>=
3.1.0)

Generalized UniFrac Distances, Distance-Based Multivariate
Methods and Feature-Based Univariate Methods for Microbiome
Data Analysis
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.
Rcpp (>= 0.12.13), matrixStats, Matrix, ape, parallel, stats,
utils, statmod, rmutil, dirmult, MASS