The wavelet-based variance transformation method is used for system modelling and prediction. It refines predictor spectral representation using Wavelet Theory, which leads to improved model specifications and prediction accuracy. Details of methodologies used in the package can be found in Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962>, Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>, and Jiang, Z., Sharma, A., & Johnson, F. (2021) <doi:10.1016/J.JHYDROL.2021.126816>.
zoo, FNN, readr, knitr, cowplot, gridGraphics, bookdown,
rmarkdown, SPEI, NPRED, synthesis, kableExtra, fitdistrplus,
testthat

c(person(given = "Ze",
family = "Jiang",
role = c("aut", "cre"),
email = "ze.jiang@unsw.edu.au",
comment = c(ORCID = "0000-0002-3472-0829")),
person(given = "Md. Mamunur",
family = "Rashid",
role = c("aut"),
comment = c(ORCID = "0000-0002-0315-9055")),
person(given = "Ashish",
family = "Sharma",
role = c("aut"),
comment = c(ORCID = "0000-0002-6758-0519")),
person(given = "Fiona",
family = "Johnson",
role = c("aut"),
comment = c(ORCID = "0000-0001-5708-1807")))

waveslim, stats, tidyr, ggplot2, sp, rlang (>= 1.0.0)
zoo, FNN, readr, knitr, cowplot, gridGraphics, bookdown,
rmarkdown, SPEI, NPRED, synthesis, kableExtra, fitdistrplus,
devtools, testthat

Marie-Pierre Sylvestre, Marie-Eve Beauchamp, Ryan Patrick Kyle, Michal Abrahamowicz
A flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.

Marie-Pierre Sylvestre <marie-pierre.sylvestre@umontreal.ca>, Marie-Eve Beauchamp <marie-eve.beauchamp@clinepi.mcgill.ca>, Ryan Patrick Kyle <ryan.kyle@mail.mcgill.ca>, Michal Abrahamowicz <michal.abrahamowicz@mcgill.ca>
WCE implements a flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.