The goal of crestr
is to produce probabilistic
reconstructions of past climate change from fossil assemblage data (Chevalier,
2022). The approach is based on the estimation of responses of
studies bio-proxy studied to climate parameters using probability
density functions (PDFs; see Chevalier et
al. (2014) and Chevalier
(2019)). The theory underpinning this package is explained in
section A
bit of theory and is illustrated with an application based on
pseudo-data in section Get
Started. The different vignettes present different aspects of
the structure of the package and the data it contains, along with
applications based on real data.
NOTE: If you notice any bug, or if you would like to see some specific functions implemented, you can contact me at chevalier.manuel@gmail.com.
You can install the package from CRAN.
install.packages("crestr")
You can also install the development version from GitHub with:
if(!require(devtools)) install.packages("devtools")
devtools::install_github("mchevalier2/crestr")
(A) density of presence records available in my calibration dataset upscaled at a 1° resolution. The diamonds represent the location of the pollen records used to generate the reconstructions presented in B-D, and the coloured boxes represent the extent of their respective calibration zones. (B) Annual precipitation reconstructions from Lake Van, Turkey (Chevalier, 2019), (C) Mean annual temperature reconstruction from Laguna de Fùquene, Colombia (unpublished) and (D) Mean Annual temperature reconstruction from marine core MD96-2048 (Chevalier et al., 2021).
Last update: 24/08/2021
N.B.: This list is as exhaustive as possible, but some studies may be missing. Contact me if you want your study to be added.