Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.

Implementation of community detection algorithm SCORE in the paper J. Jin (2015) <arXiv:1211.5803>, and SCORE+ in J. Jin, Z. Ke and S. Luo (2018) <arXiv:1811.05927>. Membership estimation algorithm called Mixed-SCORE in J. Jin, Z. Ke and S. Luo (2017) <arXiv:1708.07852>.

Evaluating probabilistic forecasts via proper scoring rules.  scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families.  Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score.  For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.

Implements multiple consistent scoring functions
    (Gneiting T (2011) <doi:10.1198/jasa.2011.r10138>) for assessing point
    forecasts and point predictions. Detailed documentation of scoring
    functions' properties is included for facilitating interpretation of
    results.

Dictionary-like reference for computing scoring rules in a wide
    range of situations. Covers both parametric forecast distributions (such as
    mixtures of Gaussians) and distributions generated via simulation.

Provides a collection of metrics and proper scoring rules 
    (Tilmann Gneiting & Adrian E Raftery (2007) 
    <doi:10.1198/016214506000001437>, Jordan, A., Krüger, F., & Lerch, S. (2019)
    <doi:10.18637/jss.v090.i12>) within a consistent framework for 
    evaluation, comparison and visualisation of forecasts. 
    In addition to proper scoring rules, functions are provided to assess 
    bias, sharpness and calibration 
    (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind
    M. Eggo, W. John Edmunds (2019) <doi:10.1371/journal.pcbi.1006785>) of 
    forecasts. 
    Several types of predictions (e.g. binary, discrete, continuous) which may 
    come in different formats (e.g. forecasts represented by predictive samples 
    or by quantiles of the predictive distribution) can be evaluated. 
    Scoring metrics can be used either through a convenient data.frame format, 
    or can be applied as individual functions in a vector / matrix format. 
    All functionality has been implemented with a focus on performance and is 
    robustly tested. Find more information about the package in the 
    accompanying paper (<doi:10.48550/arXiv.2205.07090>).