Tool to assessing whether the results of a study could be influenced by collinearity. Simulations under a given hypothesized truth regarding effects of an exposure on the outcome are used and the resulting curves of lagged effects are visualized. A user's manual is provided, which includes detailed examples (e.g. a cohort study looking for windows of vulnerability to air pollution, a time series study examining the linear association of air pollution with hospital admissions, and a time series study examining the non-linear association between temperature and mortality). The methods are described in Basagana and Barrera-Gomez (2021) <doi:10.1093/ije/dyab179>.
Version: 0.0.4 Depends: R (≥ 4.1) Imports: dlnm, graphics, grDevices, MASS, mgcv, nlme, stats, utils, VGAM Suggests: knitr, rmarkdown, splines, xtable Published: 2023-09-19 DOI: 10.32614/CRAN.package.collin Author: Jose Barrera-Gomez [aut, cre], Xavier Basagana [aut] Maintainer: Jose Barrera-Gomez <jose.barrera at isglobal.org> License: GPL-3 NeedsCompilation: no Citation: collin citation info Materials: README NEWS CRAN checks: collin results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=collin to link to this page.
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