A unified toolkit for out-of-sample forecast dominance testing.
    Covers unconditional and conditional equal and superior predictive
    ability, encompassing, and nested-model comparison. Implements the
    Diebold-Mariano test with the Harvey, Leybourne, and Newbold (1997)
    <doi:10.1016/S0169-2070(96)00719-4> small-sample correction; the
    Clark-West MSFE-adjusted statistic (Clark and West, 2007)
    <doi:10.1016/j.jeconom.2006.05.023>; the ENC-NEW encompassing test of
    Clark and McCracken (2001) <doi:10.1016/S0304-4076(01)00071-9>; the
    Giacomini-White conditional equal predictive ability test (Giacomini
    and White, 2006) <doi:10.1111/j.1468-0262.2006.00718.x>; Hansen's
    superior predictive ability test (Hansen, 2005)
    <doi:10.1198/073500105000000063>; the conditional superior predictive
    ability test of Li, Liao, and Quaedvlieg (2022)
    <doi:10.1093/restud/rdab039>; and the uniform and average multi-horizon
    SPA tests of Quaedvlieg (2021) <doi:10.1080/07350015.2019.1620074>.

A web application for displaying, analysing and forecasting univariate time series. Includes basic methods such as mean, naïve, seasonal naïve and drift, as well as more complex methods such as Holt-Winters Box,G and Jenkins, G (1976) <doi:10.1111/jtsa.12194> and ARIMA Brockwell, P.J. and R.A.Davis (1991) <doi:10.1007/978-1-4419-0320-4>.

Convenient functions for ensemble forecasts in R combining
    approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
    thetaf(), nnetar(), stlm(), tbats(), snaive() and arfima() can be combined with equal weights, weights
    based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>),
    or cross-validated weights. Cross validation for time series data with user-supplied models
    and forecasting functions is also supported to evaluate model accuracy.

Runs multiple individual time series models, and combines them into an ensembles of time series models. This is mainly used to predict the results of the monthly labor market report from the 
    United States Bureau of Labor Statistics for virtually any part of the economy reported by the Bureau of Labor Statistics, but it can be easily modified to work with other types of time series data.
    For example, the package was used to predict the winning men's and women's time for the 2024 London Marathon.