knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  fig.cap=""
)


crestr An R package to perform probabilistic palaeoclimate reconstructions from palaeoecological datasets

crestr produces probabilistic reconstructions of past climate change from fossil assemblage data (Chevalier, 2022). crestr works by analysing how certain biological indicators (like plant or animal remains) respond to climate factors, using statistical methods to estimate these relationships. These relationships are mdelled as 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.

Why choose crestr? Unlike traditional methods, crestr uses probabilistic techniques to provide more accurate and flexible climate reconstructions. Its focus on accessibility means you don’t need to be an expert coder to get meaningful results.


NOTE: While active development of crestr has concluded, its robust features will continue to provide valuable insights for palaeoclimate research. The available documentation and resources will remain accessible for independent use. In addition, I am committed to maintaining this bug-free. As such, please reach out at paleo@manuelchevalier.com if you encounter technical issues.

Installation

Ready to explore the climate history hidden in your data? Install crestr now and leverage its robust tools for your research.

The package is available from GitHub and can be installed as follow:

if(!require(devtools)) install.packages("devtools")
devtools::install_github("mchevalier2/crestr")

crestr in a nutshell

Key advantages of crestr:

To get started, refer to the crestr cheat sheet, which provides step-by-step guidance on using the package. This document summarises the main functionalities of the package and illustrates the main key step you will have to follow to reconstruct environmental parameters from your data. [download here]

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Example applications


knitr::include_graphics("https://raw.githubusercontent.com/mchevalier2/crestr/master/webpage/example-app.png")

Real-world examples of how crestr has been used. (A) density of presence records available in the gbif4crest_02 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) Lake Van, Turkey: Reconstructed annual rainfall patterns (Chevalier, 2019), (C) Laguna Fùquene, Colombia: Estimated average yearly temperatures. (unpublished) and (D) Marine Core MD96-2048 (off southeastern Africa): 800,000 year-long temperature changes in marine environments (Chevalier et al., 2021).

CREST in the scientific literature

knitr::include_graphics("https://raw.githubusercontent.com/mchevalier2/crestr/master/webpage/crest-use-02.png")

Location of real_world crest-based climate reconstructions. All these examples showcase CREST’s ability to contribute to global climate research and advance our understanding of regional environmental changes.


knitr::include_graphics("https://raw.githubusercontent.com/mchevalier2/crestr/master/webpage/crest-use-01.png")
knitr::include_graphics("https://raw.githubusercontent.com/mchevalier2/crestr/master/webpage/crest-use-03.png")

Last update: 03/01/2025

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.


References



mchevalier2/crestr documentation built on Feb. 14, 2025, 6:31 p.m.