Packaged: 2016-04-11 13:01:18 UTC; Frederico Mestre

rgrass7, raster, spatstat, stats, sp, minpack.lm, zipfR, coda,
Author: Frederico Mestre, Fernando Canovas, Benjamin Risk, Ricardo Pita,

rgrass7, raster, spatstat.geom, spatstat.random, stats, sp,
result of climate change). References: Mestre, F., Canovas, F., Pita, R.,
Risk, B., Mira, A., Beja, P., Pita, R. (2017)

Packaged: 2020-06-02 18:13:44 UTC; Frederico

rgrass7, raster, spatstat.geom, spatstat.core, stats, sp,
Packaged: 2021-02-16 01:10:25 UTC; Frederico

Title: Metagenomics Longitudinal Differential Abundant Method
Author: Ahmed Metwally, Patricia Finn, Yang Dai, David Perkins
Description: Identify the time intervals when the microbial features are
differentially abundant between two phenotypic groups using negative binomial
smoothing spline ANOVA (Ahmed Metwally, Patricia Finn, Yang Dai, David Perkins, ACM BCB’17).
Imports: ggplot2, gss, plyr, caTools, parallel, doParallel
Packaged: 2017-07-28 16:22:36 UTC; ahmedmetwally

Title: Metagenomics Longitudinal Differential Abundance Method
Description: Identify time intervals of differentially abundant metagenomic features in longitudinal studies <doi:10.1145/3107411.3107429>.
Imports: ggplot2, gss, plyr, caTools, parallel, doParallel,
Packaged: 2017-10-20 15:23:40 UTC; ahmedmetwally

Description: Identify time intervals of differentially abundant metagenomics features in longitudinal studies <doi:10.1145/3107411.3107429>.
Author: Ahmed Metwally, Yang Dai, Patricia Finn, David Perkins
Packaged: 2017-12-16 23:08:05 UTC; ahmedmetwally

Packaged: 2018-01-21 23:21:19 UTC; ahmedmetwally

Description: Identify time intervals of differentially abundant metagenomics features in longitudinal studies.
Imports: gss, plyr, caTools, ggplot2, parallel, doParallel,
Packaged: 2018-05-26 00:10:06 UTC; ahmedmetwally

Author: Ahmed A. Metwally, Yang Dai, Patricia W. Finn, David L. Perkins
Maintainer: Ahmed A. Metwally <ametwall@stanford.edu>
Description: Identify time intervals of differentially abundant metagenomic features in longitudinal studies (Metwally AA, et al., Microbiome, 2018 <doi:10.1186/s40168-018-0402-y>).
Imports: gss, plyr, zoo, pracma, ggplot2, parallel, doParallel,
Packaged: 2019-04-13 09:02:39 UTC; ahmedmetwally