Collection of functions for quality control (QC) of climatological daily time series (e.g. the ECA&D station data).

For environmental chemists, ecologists, researchers and agricultural scientists to understand the dissipation kinetics, calculate the half-life periods and rate constants of compounds, pesticides, contaminants in different matrices.

A user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) 
    assays in pancreatic beta cells or islets.
    The package allows the user to import several sets of experiments from different spreadsheets 
    and to perform subsequent steps: summarise in a tidy format, visualise data quality 
    and compare experimental conditions without omitting to account for technical confounders 
    such as the date of the experiment or the technician.
    Together, insane is a comprehensive method that optimises pre-processing and analyses of 
    GSIS experiments in a friendly-user interface.
    The Shiny App was initially designed for EndoC-betaH1 cell line following method described 
    in Ndiaye et al., 2017 (<doi:10.1016/j.molmet.2017.03.011>).

Provides tools for probabilistic taxon assignment with informatic sequence classification trees. See Wilkinson et al (2018) <doi:10.7287/peerj.preprints.26812v1>.

David Onstad provided us with this insect disease database, sometimes referred to as the 'Ecological Database of the Worlds Insect Pathogens' or EDWIP. Files have been converted from 'SQL' to csv, and ported into 'R' for easy exploration and analysis. Thanks to the Macroecology of Infectious Disease Research Coordination Network (RCN) for funding and support. Data are also served online in a static format at <https://edwip.ecology.uga.edu/>.

Using embedded sdmx queries, get the data of more than 150 000 insee series from 'bdm' macroeconomic database.

A tool to provide an easy, intuitive and consistent
    access to information contained in various R models, like model
    formulas, model terms, information about random effects, data that was
    used to fit the model or data from response variables. 'insight'
    mainly revolves around two types of functions: Functions that find
    (the names of) information, starting with 'find_', and functions that
    get the underlying data, starting with 'get_'.  The package has a
    consistent syntax and works with many different model objects, where
    otherwise functions to access these information are missing.