Experience studies are used by actuaries to explore historical
    experience across blocks of business and to inform assumption setting
    activities. This package provides functions for preparing data, creating 
    studies, visualizing results, and beginning assumption development. 
    Experience study methods, including exposure calculations, are described in:
    Atkinson & McGarry (2016) "Experience Study Calculations" 
    <https://www.soa.org/49378a/globalassets/assets/files/research/experience-study-calculations.pdf>.
    The limited fluctuation credibility method used by the 'exp_stats()'
    function is described in: Herzog (1999, ISBN:1-56698-374-6) 
    "Introduction to Credibility Theory".

This code provides a simple method for representing a visual scene as it may be seen by an animal with less acute vision. When using (or for more information), please cite the original publication.

Create, upload and run 'Acumos' R models.
  'Acumos' (<https://www.acumos.org>) is a platform and open source framework intended to make it easy to build,
  share, and deploy AI apps. 'Acumos' is part of the 'LF AI Foundation', an umbrella
  organization within 'The Linux Foundation'. With this package, user can create a
  component, and push it to an 'Acumos' platform.

Package 'ACV' (short for Affine Cross-Validation) offers an improved time-series cross-validation loss estimator which utilizes both in-sample and out-of-sample forecasting performance via a carefully constructed affine weighting scheme. Under the assumption of stationarity, the estimator is the best linear unbiased estimator of the out-of-sample loss. Besides that, the package also offers improved versions of Diebold-Mariano and Ibragimov-Muller tests of equal predictive ability which deliver more power relative to their conventional counterparts. For more information, see the accompanying article Stanek (2021) <doi:10.2139/ssrn.3996166>.

Functions for calculating the acute chronic workload ratio using three 
            different methods: exponentially weighted moving average (EWMA), rolling 
            average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this 
            methods can be found in Williams et al. (2017) <doi:10.1136/bjsports-2016-096589>
            for EWMA and Windt & Gabbet (2018) for RAC and RAU <doi: 10.1136/bjsports-2017-098925>.