Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.

Fast and memory-less computation of the energy statistics related quantities for vectors and matrices. References include: Szekely G. J. and Rizzo M. L. (2014), <doi:10.1214/14-AOS1255>. Szekely G. J. and Rizzo M. L. (2023), <ISBN:9781482242744>. Tsagris M. and Papadakis M. (2025). <doi:10.48550/arXiv.2501.02849>.

Estimates item and person parameters for the Continuous Response Model (CRM; Samejima, 1973, <doi:10.1007/BF02291114>), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.

Implements the Polynomial Maximization Method ('PMM') for parameter estimation
    in linear and time series models when error distributions deviate from normality.
    The 'PMM2' variant achieves lower variance parameter estimates compared to ordinary
    least squares ('OLS') when errors exhibit significant skewness. Includes methods
    for linear regression, 'AR'/'MA'/'ARMA'/'ARIMA' models, and bootstrap inference.
    Methodology described in Zabolotnii, Warsza, and Tkachenko (2018) <doi:10.1007/978-3-319-77179-3_75>,
    Zabolotnii, Tkachenko, and Warsza (2022) <doi:10.1007/978-3-031-03502-9_37>, and
    Zabolotnii, Tkachenko, and Warsza (2023) <doi:10.1007/978-3-031-25844-2_21>.

An implementation of sequential testing that uses evidence ratios
    computed from the weights of a set of models. These weights correspond either
    to Akaike weights computed from the Akaike Information Criterion (AIC) or the
    Bayesian Information Criterion (BIC) and following Burnham & Anderson
    (2004, <doi:10.1177/0049124104268644>) recommendations, or to pseudo-BMA
    weights computed from the WAIC or the LOO-IC of models fitted
    with 'brms' and following Yao et al. (2017, <arXiv:1704.02030v3>).

Provides tools for determining estimability of linear functions 
  of regression coefficients, and 'epredict' methods that handle 
  non-estimable cases correctly. Estimability theory is discussed in
  many linear-models textbooks including Chapter 3 of Monahan, JF (2008), 
  "A Primer on Linear Models", Chapman and Hall (ISBN 978-1-4200-6201-4).