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Description

Complete Stochastic Modelling Solution.

Makes univariate, multivariate, or random fields simulations precise and simple. Just select the desired time series or random fields’ properties and it will do the rest. CoSMoS is based on the framework described in Papalexiou (2018, <doi:10.1016/j.advwatres.2018.02.013>), extended for random fields in Papalexiou and Serinaldi (2020, <doi:10.1029/2019WR026331>), and further advanced in Papalexiou et al. (2021, <doi:10.1029/2020WR029466>) to allow fine-scale space-time simulation of storms (or even cyclone-mimicking fields).

CoSMoS

Travis build status AppVeyor build status CRAN status CRAN_Download_Badge license

Complete Stochastic Modelling Solution

CoSMoS is an R package that makes time series generation with desired properties easy. Just choose the characteristics of the time series you want to generate, and it will do the rest. The generated time series preserve any probability distribution and any linear autocorrelation structure. Users can generate as many and as long time series from processes such as precipitation, wind, temperature, relative humidity etc. It is based on a framework that unified, extended, and improved a modelling strategy that generates time series by transforming “parent” Gaussian time series having specific characteristics (Papalexiou, 2018).

Install

To install the latest version of the package run:

## copy-paste to get the latest version of CoSMoS

if (!require('devtools')) {install.packages('devtools'); library(devtools)} 

install_github('TycheLab/CoSMoS', upgrade = 'never')

library(CoSMoS)

?`CoSMoS-package`

Funding

The package was partly funded by the Global institute for Water Security (GIWS; https://www.usask.ca/water/) and the Global Water Futures (GWF; https://gwf.usask.ca/) program.

Authors

Coded by: Filip Strnad and Francesco Serinaldi
Conceptual design by: Simon Michael Papalexiou
Tested and documented by: Yannis Markonis
Maintained by: Kevin Shook

References

Papalexiou, S.M., 2018. Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency. Advances in Water Resources 115, 234-252. https://doi.org/10.1016/j.advwatres.2018.02.013

Metadata

Version

2.1.0

License

Unknown

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