A statistical hypothesis test for conditional independence.
    Given residuals from a sufficiently powerful regression, it tests whether 
    the covariance of the residuals is vanishing. It can be applied to both
    discretely-observed functional data and multivariate data. 
    Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas
    Peters (2022) <doi:10.1111/rssb.12544>.

The goal of 'GHCNr' is to provide a fast and friendly interface with the Global Historical Climatology Network daily (GHCNd) database, which contains daily summaries of weather station data worldwide (<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>). GHCNd is accessed through the web API <https://www.ncei.noaa.gov/access/services/data/v1>. 'GHCNr' main functionalities consist of downloading data from GHCNd, filter it, and to aggregate it at monthly and annual scales.

Colour palettes inspired by Studio Ghibli <https://en.wikipedia.org/wiki/Studio_Ghibli> 
    films, ported to R for your enjoyment.

Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.

A 'GraphQL' client, with an R6 interface for initializing a
    connection to a 'GraphQL' instance, and methods for constructing
    queries, including fragments and parameterized queries. Queries are
    checked with the 'libgraphqlparser' C++ parser via the 'graphql'
    package.

A collection of commonly used visualizations of temporal and
    spatio-temporal health data including case counts, incidence rates, and
    covariates. The available plot types include time series, heatmaps,
    seasonality plots, maps and more. The package supports standard data
    transformations such as temporal and spatial aggregations, while
    offering extensive customization options for the resulting figures.