Two pipelines are provided to study microbial turnover along a gradient, including the beta diversity and microbial abundance change. The 'betaturn' class consists of the steps of community dissimilarity matrix generation, matrix conversion, differential test and visualization. The workflow of 'taxaturn' class includes the taxonomic abundance calculation, abundance transformation, abundance change summary, statistical analysis and visualization. Multiple statistical approaches can contribute to the analysis of microbial turnover.

Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) <arXiv:1601.03501>.

Provides an extensive collection of datasets related to medicine, 
    diseases, treatments, drugs, and public health. This package covers topics 
    such as drug effectiveness, vaccine trials, survival rates, 
    infectious disease outbreaks, and medical treatments. The included datasets 
    span various health conditions, including AIDS, cancer, bacterial infections, 
    and COVID-19, along with information on pharmaceuticals and vaccines. These 
    datasets are sourced from the R ecosystem and other R packages, remaining 
    unaltered to ensure data integrity. This package serves as a valuable 
    resource for researchers, analysts, and healthcare professionals interested 
    in conducting medical and public health data analysis in R.

Multi Calculator of different scores to measure adherence to Mediterranean Diet, to compute them in nutriepidemiological data. Additionally, a sample dataset of this kind of data is provided, and some other minor tools useful in epidemiological studies.

'MedDRA' data is used for defining adverse events in clinical studies. You
    can load and merge the data for use in categorizing the adverse events
    using this package. The package requires the data licensed from 'MedDRA'
    <https://www.meddra.org/>.

Two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm are implemented in this package: 1) a Bayesian hierarchical model with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes model that estimates the ratio of the observed count of harm to the count that would be expected if error characteristics and harm were independent. In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions.