Functionality to add, delete, read and update table
    records from your 'AppSheet' apps, using the official API <https://api.appsheet.com/>.

An unofficial companion to the textbook "Applied Regression
    Analysis" by N.R. Draper and H. Smith (3rd Ed., 1998) including all the
    accompanying datasets.

Assists the evaluation of whether and
    where to focus code optimization, using Amdahl's law and visual aids
    based on line profiling. Amdahl's profiler organizes profiling output
    files (including memory profiling) in a visually appealing way.
    It is meant to help to balance development
    vs. execution time by helping to identify the most promising sections
    of code to optimize and projecting potential gains. The package is
    an addition to R's standard profiling tools and is not a wrapper for them.

Implements the scenario analysis proposed by Antolin-Diaz, 
    Petrella and Rubio-Ramirez (2021) 
    "Structural scenario analysis with SVARs" <doi:10.1016/j.jmoneco.2020.06.001>.

Provides methods to analyse the stability of non-deterministic prediction models. 
    Prediction stability is quantified either as data-based prediction stability (phi) 
    or as model-based prediction stability (psi). The package implements measures for 
    categorical, ordinal, and metric predictions based on repeated model fitting 
    and corresponding predictions. Methods are based on Lange et al. (2025) <doi:10.1186/s12859-025-06097-1>.

The functions in this package inspect, read, edit and run files for 'APSIM' "Next Generation" ('JSON')
             and 'APSIM' "Classic" ('XML'). The files with an 'apsim' extension correspond to
	     'APSIM' Classic (7.x) - Windows only - and the ones with an 'apsimx' extension correspond to 'APSIM' "Next Generation".
	     For more information about 'APSIM' see (<https://www.apsim.info/>) and for 'APSIM'
	     next generation (<https://apsimnextgeneration.netlify.app/>).

The transmission between two time-series prices is assessed. It contains several functions for linear and nonlinear threshold co-integration, and furthermore, symmetric and asymmetric error correction models.