A Simple Approach to Scale Gene Expression Data Derived from
Different Platforms for Integrated Analyses
Simple method for comparison of gene
expression generated across different experiments, and on
different platforms; that does not require global
renormalization, and is not restricted to comparison of
identical probes. YuGene works on a range of microarray dataset
distributions, such as between manufacturers. The resulting
output allows direct comparisons of gene expression between
experiments and experimental platforms.

Florian Rohart, Kim-Anh Le Cao, Leo McHugh

Simple Ecological Statistics from the NEON Network
Provides simple statistics from instruments and observations at sites in the NEON network, and acts as a simple interface for v0 of the National Ecological Observatory Network (NEON) API.
Statistics are generated for meteorologic and soil-based observations, and are presented for daily, annual, and one-time observations at all available NEON sites.
Users can also retrieve any dataset publicly hosted by NEON. Metadata for NEON sites and data products can be returned, as well as information on data product availability by site and date.
For more information on NEON, please visit <https://www.neonscience.org>. For detailed data product information, please see the NEON data product catalog at <https://data.neonscience.org/data-product-catalog>.
rjson, stringr, magrittr, lubridate, dplyr

Zero-Inflated Beta-Binomial Modeling of Microbiome Count Data
Microbiome count data (Operational Taxonomic Unit, OTUs) is usually overdispersed and has excessive zero counts. The 'ZIBBSeqDiscovery' assumes a zero-inflated beta-binomial model for the distribution of the count data, and employes link functions to adjust interested covariates. To fit the model, two approaches are proposed (i) a free approach which treats the overdispersion parameters for OTUs as independent, and (ii) a constrained approach which proposes a mean-overdispersion relationship to the count data. This package can be used to test the association between the composition of the microbiome counts and the interested covariates.