Implements a unified predictive framework in which individual
    parameters are decomposed as theta_i equal to theta_ref plus
    Delta(x_i, theta_ref), with theta_ref a population reference and
    Delta an explicit deviation function. The decomposition follows the
    Additive-Multiplicative-Modulated canonical form and is estimated
    through three complementary paths: hierarchical Bayesian inference
    via 'Stan', varying-coefficient models via penalized splines, and
    amortized inference via hypernetworks in 'torch'. The package
    provides identifiability diagnostics, validity tests for the
    population reference, and benchmarks against canonical zero-inflated
    count datasets and avian abundance data from the eBird Status and
    Trends project. The framework and its estimation paths are described
    in Gomez Julian (2026) <doi:10.5281/zenodo.21046269>.

Functions to compute the Generalized Dynamic Principal Components
    introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>. The implementation
    includes an automatic procedure proposed in Peña, Smucler and Yohai (2020) <DOI:10.18637/jss.v092.c02>
    for the identification of both the number of lags to be used
    in the generalized dynamic principal components as well as the number of components required
    for a given reconstruction accuracy.

Convert GDP time series data from one unit to
    another. All common GDP units are included, i.e. current and constant
    local currency units, US$ via market exchange rates and international
    dollars via purchasing power parities.

Contains a function called gds() which accepts three input
    parameters like lower limits, upper limits and the frequencies of the
    corresponding classes. The gds() function calculate and return the values
    of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard
    deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', 'gq2', 'gq3'),
    inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate
    effective data analysis. For skewness and kurtosis calculations we use moments.