Actuarial reports are prepared for the last day of a specific 
    period, such as a month, a quarter or a year. Actuarial models assume that 
    certain events happen at the beginning or end of periods. The package 
    contains functions to easily refer to the first or last (working) day 
    within a specific period relative to a base date to facilitate actuarial 
    reporting and to compare results.

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>.