A procedure that fits derivative curves based on a sequence of quotient differences.  In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves.  In a non-hierarchical setting the package produces a single derivative curve.

Hard drive data: Class of data allowing the easy importation/manipulation of out of memory data sets. The data sets are located on disk but look like in-memory, the syntax for manipulation is similar to 'data.table'. Operations are performed "chunk-wise" behind the scene.

'HDF5' (Hierarchical Data Format 5) is a high-performance 
    library and file format for storing and managing large, complex 
    data. This package provides the static libraries and headers for 
    the 'HDF5' 'C' library (release 2.0.0). It is intended for R 
    package developers to use in the 'LinkingTo' field, which 
    eliminates the need for users to install system-level 'HDF5' 
    dependencies. This build is compiled with thread-safety enabled 
    and supports dynamic loading of external compression filters. 
    'HDF5' is developed by 'The HDF Group' <https://www.hdfgroup.org/>.

'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>.