'HDF5' is a data model, library and file format for storing
    and managing large amounts of data. This package provides a nearly
    feature complete, object oriented  wrapper for the 'HDF5' API
    <https://support.hdfgroup.org/documentation/hdf5/latest/_r_m.html> using R6 classes.
    Additionally, functionality is added so that 'HDF5' objects behave very
    similar to their corresponding R counterparts.

Some methods to manipulate 'HDF5' files, extending the 'hdf5r' package. Reading and writing R objects to 'HDF5' formats follow the specification of 'AnnData' <https://anndata.readthedocs.io/en/latest/fileformat-prose.html>.

Provides a forecasting method that efficiently maps vast
    numbers of (scalar-valued) signals into an aggregate density forecast
    in a time-varying and computationally fast manner. The method proceeds
    in two steps: First, it transforms a predictive signal into a density
    forecast and, second, it combines the resulting candidate density
    forecasts into an ultimate aggregate density forecast. For a detailed
    explanation of the method, please refer to Adaemmer et al. (2025)
    <doi:10.1080/07350015.2025.2526424>.

Provides an interface to 'HDFql' <https://www.hdfql.com/> 
    and helper functions for reading data from and writing data to 'HDF5' files. 'HDFql' 
    provides a high-level language for managing 'HDF5' data that is platform independent.
    For more information, see the reference manual 
    <https://www.hdfql.com/resources/HDFqlReferenceManual.pdf>.

Offers methods for visualizing, modelling, and forecasting high-dimensional functional time series, also known as functional panel data. Documentation about 'hdftsa' is provided via the paper by Cristian F. Jimenez-Varon, Ying Sun and Han Lin Shang (2024, <doi:10.1080/10618600.2024.2319166>).

Test the significance of coefficients in high dimensional generalized linear models.

Implementation of multiple approaches to perform inference in high-dimensional models.

A correlation-based batch process for fast, accurate imputation for 
    high dimensional missing data problems via chained random forests.
    See Waggoner (2023) <doi:10.1007/s00180-023-01325-9> for more on 'hdImpute',
    Stekhoven and Bühlmann (2012) <doi:10.1093/bioinformatics/btr597> for more on 'missForest', 
    and Mayer (2022) <https://github.com/mayer79/missRanger> for more on 'missRanger'.