Toolkit to support and perform discrete event simulations with and without
    resource constraints in the context of health technology assessments (HTA).
    The package focuses on cost-effectiveness modelling and aims to be submission-ready
    to relevant HTA bodies in alignment with 'NICE TSD 15'
    <https://sheffield.ac.uk/nice-dsu/tsds/patient-level-simulation>.
    More details an examples can be found in the package website <https://jsanchezalv.github.io/WARDEN/>.

Heuristic methods to solve the routing problems in a warehouse management. Package includes several heuristics such as the Midpoint, Return, S-Shape and Semi-Optimal Heuristics for designation of the picker’s route in order picking. The heuristics aim to provide the acceptable travel distances while considering warehouse layout constraints such as aisles and shelves. 
    It also includes implementation of the COPRAS (COmplex PRoportional ASsessment) method for supporting selection of locations to be visited by the picker in shared storage systems. The package is designed to facilitate more efficient warehouse routing and logistics operations.     
    see:
    Bartholdi, J. J.,  Hackman, S. T. (2019). "WAREHOUSE & DISTRIBUTION SCIENCE. Release 0.98.1."
    The Supply Chain & Logistics Institute. H. Milton Stewart School of Industrial and Systems Engineering. 
    Georgia Institute of Technology. 
    <https://www.warehouse-science.com/book/editions/wh-sci-0.98.1.pdf>.

This estimates precise weaning ages
	for a given skeletal population
	by analyzing the stable nitrogen isotope ratios of them.
	Bone collagen turnover rates estimated anew and
	the approximate Bayesian computation (ABC)
	were adopted in this package.

Infectious disease surveillance requires early outbreak detection. This
    package provides statistical tools for analyzing time-series monitoring 
    data through three core methods: 
    a) EWMA (Exponentially Weighted Moving Average)
    b) Modified-CUSUM (Modified Cumulative Sum)
    c) Adjusted-Serfling models
    Methodologies are based on:
    - Wang et al. (2010) <doi:10.1016/j.jbi.2009.08.003>
    - Wang et al. (2015) <doi:10.1371/journal.pone.0119923>
    Designed for epidemiologists and public health researchers working with
    disease surveillance systems.