Nonparametric kernel density estimation, bandwidth selection,
    and other utilities for analyzing directional data. Implements the estimator
    in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the
    cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987)
    <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in
    García-Portugués (2013) <doi:10.1214/13-ejs821>.

Contains the support functions for the Time Series Analysis book.
	     We present a function to calculate MSE and MAE for inputs
	     of actual and forecast values.  We also have the code for
	     disaggregation as found in Wei and Stram
	     (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>),
	     and Hodgess and Wei (1996, "Temporal Disaggregation of Time
	     Series").

The twoStepsBenchmark() and threeRuleSmooth() functions allow you to 
    disaggregate a low-frequency time series with higher frequency time series, 
    using the French National Accounts methodology. The aggregated sum of the 
    resulting time series is strictly equal to the low-frequency time series within the 
    benchmarking window. Typically, the low-frequency time series is an annual one, 
    unknown for the last year, and the high frequency one is either quarterly or 
    monthly. See "Methodology of quarterly national accounts", Insee Méthodes 
    N°126, by Insee (2012, ISBN:978-2-11-068613-8, <https://www.insee.fr/en/information/2579410>).

Provides tools for temporal disaggregation, including:
    (1) High-dimensional and low-dimensional series generation for simulation studies;
    (2) A toolkit for temporal disaggregation and benchmarking using low-dimensional indicator series 
        as proposed by Dagum and Cholette (2006, ISBN:978-0-387-35439-2);
    (3) Novel techniques by Mosley, Gibberd, and Eckley (2022, <doi:10.1111/rssa.12952>)
        for disaggregating low-frequency series in the presence of high-dimensional indicator matrices.

Fits disaggregation regression models using 'TMB' ('Template Model
    Builder'). When the response data are aggregated to polygon level but
    the predictor variables are at a higher resolution, these models can be
    useful. Regression models with spatial random fields. The package is 
    described in detail in Nandi et al. (2023) <doi:10.18637/jss.v106.i11>.