Global hypothesis tests combine information across multiple endpoints to test a single hypothesis. The prediction test is a recently proposed global hypothesis test with good performance for small sample sizes and many endpoints of interest. The test is also flexible in the types and combinations of expected results across the individual endpoints. This package provides functions for data processing and calculation of the prediction test.

Provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.

Makes the time series prediction easier by automatizing this process
  using four main functions: prep(), modl(), pred() and postp(). Features different
  preprocessing methods to homogenize variance and to remove trend and seasonality.
  Also has the potential to bring together different predictive models to make comparatives.
  Features ARIMA and Data Mining Regression models (using caret).

Implements the Single Transferable Vote (STV) electoral system, with
    clear explanatory graphics. The core function stv() uses Meek's method, the purest
    expression of the simple principles of STV, but which requires electronic
    counting. It can handle votes expressing equal preferences for subsets of
    the candidates. A function stv.wig() implementing the Weighted Inclusive
    Gregory method, as used in Scottish council elections, is also provided,
    and with the same options, as described in the manual. The required vote
    data format is as an R list: a function pref.data() is provided to transform
    some commonly used data formats into this format.
    References for methodology:
    Hill, Wichmann and Woodall (1987) <doi:10.1093/comjnl/30.3.277>,
    Hill, David (2006) <https://www.votingmatters.org.uk/ISSUE22/I22P2.pdf>,
    Mollison, Denis (2023) <arXiv:2303.15310>,
    (see also the package manual pref_pkg_manual.pdf).