Provides tools specifically designed for analyzing longitudinal microbiome data. This tool integrates seven functional modules, providing a systematic framework for microbiome time-series analysis. For more details on inferences involving interspecies interactions see Fisher (2014) <doi:10.1371/journal.pone.0102451>. Details on this package are also described in an unpublished manuscript.

Stand-alone HTTP capable R-package repository,
    that fully supports R's install.packages() and available.packages().
    It also contains API endpoints for end-users to add/update packages.
    This package can supplement 'miniCRAN', which has functions for maintaining
    a local (partial) copy of 'CRAN'.
    Current version is bare-minimum without any access-control or much security.

Downloads and processes health microdata from Brazilian Unified
    Health System ('DataSUS') information systems. It handles the compressed
    'DBC' format internally and provides functions to organize variables and
    add labels to categorical fields from mortality, live births, hospital
    admissions, outpatient care, health facilities, and notifiable diseases
    data. For details, see Saldanha et al. (2019)
    <doi:10.1590/0102-311x00032419>.

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

A series of data analysis approaches for microbiome data 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.

Collect and normalize local microinverter energy and power production
    data through off-cloud API requests. Currently supports 'APSystems', 'Enphase',
    and 'Fronius' microinverters.

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.