Provides utilities for reading and processing microdata from Spanish official statistics with R.

A framework for analyzing broth microdilution assays in various
    96-well plate designs, visualizing results and providing descriptive and (simple)
    inferential statistics (i.e. summary statistics and sign test). The functions are 
    designed to add metadata to 8 x 12 tables of absorption values, creating a tidy
    data frame. Users can choose between clean-up procedures via function 
    parameters (which covers most cases) or user prompts (in cases with complex 
    experimental designs). Users can also choose between two validation methods,
    i.e. exclusion of absorbance values above a certain threshold or manual exclusion 
    of samples. A function for visual inspection of samples with their absorption values
    over time for certain group combinations helps with the decision. In addition,
    the package includes functions to subtract the background absorption (usually
    at time T0) and to calculate the growth performance compared to a baseline.
    Samples can be visually inspected with their absorption values displayed across
    time points for specific group combinations. Core functions of this package (i.e.
    background subtraction, sample validation and statistics) were inspired by the
    manual calculations that were applied in Tewes and Muller (2020)
    <doi:10.1038/s41598-020-67600-7>.

A series of statistical and plotting approaches in microbial community ecology based on the R6 class. The classes are designed for data preprocessing, taxa abundance plotting, alpha diversity analysis, beta diversity analysis, differential abundance test, null model analysis, network analysis, machine learning, environmental data analysis and functional analysis.

A downstream bioinformatics tool to construct and assist 
    curation of microhaplotypes from short read sequences.

Most multilevel methodologies can only model macro-micro
    multilevel situations in an unbiased way, wherein group-level predictors
    (e.g., city temperature) are used to predict an individual-level
    outcome variable (e.g., citizen personality). In contrast,
    this R package enables researchers to model micro-macro situations, wherein
    individual-level (micro) predictors (and other group-level predictors) are
    used to predict a group-level (macro) outcome variable in an unbiased way.