Analysing accelerometer data using hidden Markov models
Analysing time-series accelerometer data to quantify length and intensity of physical activity.

Hypothesis Testing and Power Calculations for Comparing
Metagenomic Samples from HMP
Patricio S. La Rosa, Elena Deych, Berkley Shands, William D. Shannon
R (>= 2.10.0), lattice, MCMCpack, maxLik, binomSamSize, Hmisc,
zipfR, vegan, dirmult
This package provides several functions to perform formal hypothesis testing, and power and sample size calculations for human microbiome experiments. Dirichlet-Multinomial distribution is proposed for modeling the species abundance distributions of human microbiota (ranked bacterial taxa counts).

Patricio S. La Rosa, Elena Deych, Berkley Shands, William D.
Shannon
This package provides several functions to perform formal
hypothesis testing, and power and sample size calculations for
human microbiome experiments. Dirichlet-Multinomial
distribution is proposed for modeling the species abundance
distributions of human microbiota (ranked bacterial taxa
counts).

This package provides several functions to perform formal
hypothesis testing, and power and sample size calculations for
human microbiome experiments. Dirichlet-Multinomial
distribution is proposed to model species abundance and ranked
abundance data.

MASS, gplots, doParallel, foreach, parallel
Using Dirichlet-Multinomial distribution to provide several functions for formal hypothesis testing, power and sample size calculations for human microbiome experiments.

Patricio S. La Rosa, Elena Deych, Sharina Carter, Berkley Shands, William D. Shannon
ggplot2, stats, foreach, doParallel, MASS, vegan, gplots,
rpart, rpart.plot, parallel, graphics, lattice

ggplot2, stats, foreach, doParallel, MASS, vegan, gplots,
rpart, rpart.plot, parallel, graphics

Patricio S. La Rosa, Elena Deych, Sharina Carter, Berkley Shands, Dake Yang, William D. Shannon

Statistical Object Oriented Data Analysis of RDP-based Taxonomic
trees from Human Microbiome Data: Modeling, Visualization, and
Two-Group Comparison
In this package, we introduce Object Oriented Data Analysis (OODA) methods to analyze Human Microbiome taxonomic trees directly. We provide tools to model, compare, and visualize populations of taxonomic tree objects. In particular, using a unimodal probability measure in the set of trees, we provide procedures to compute the MLE of the central tree and the LRT statistics for comparing the distributions of two metagenomic populations. Our procedures have been developed to support parallel computations for comparing two groups.