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>.

Provides computational tools for estimating inverse regions and 
  constructing the corresponding simultaneous outer and inner confidence regions. 
  Acceptable input includes both one-dimensional and two-dimensional data for linear, 
  logistic, functional, and spatial generalized least squares regression models. 
  Functions are also available for constructing simultaneous confidence bands (SCBs) 
  for these models. The definition of simultaneous confidence regions (SCRs) follows 
  Sommerfeld et al. (2018) <doi:10.1080/01621459.2017.1341838>. Methods for estimating 
  inverse regions, SCRs, and the nonparametric bootstrap are based on 
  Ren et al. (2024) <doi:10.1093/jrsssc/qlae027>. Methods for constructing SCBs 
  are described in Crainiceanu et al. (2024) <doi:10.1201/9781003278726> 
  and Telschow et al. (2022) <doi:10.1016/j.jspi.2021.05.008>.

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. Further 
    details can be found in the package vignettes <doi:10.18637/jss.v090.i12>, 
    <doi:10.18637/jss.v110.i08>.