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dilp

The goal of dilp is to help with analysis of quantitative fossil leaf traits. Key functions included are:

  • Digital Leaf Physiognomy - dilp()

    • Estimate mean annual temperature and mean annual precipitation using multiple linear regressions.
  • Fossil Leaf Mass per Area - lma()

    • Reconstruct leaf mass per area using leaf area and petiole width
  • Leaf Margin Analysis - temp_slr()

    • Estimate mean annual temperature using leaf margin analysis
  • Leaf Area Analysis - precip_slr()

    • Estimate mean annual precipitation using leaf area analysis

Installation

You can install the stable version of dilp from your R session with:

install.packages("dilp")

You can install the development version of dilp from GitHub with:

# install.packages("devtools")
devtools::install_github("mjbutrim/dilp")

Example

Find a basic example of running a DiLP and LMA analysis in this vignette

For ease of use, a template spreadsheet for data collection can be found here: DiLP Data Collection Template

If you encounter any problems, or would like to request a feature, please create an issue here

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Version

Install

install.packages('dilp')

Monthly Downloads

189

Version

1.1.0

License

GPL (>= 3)

Issues

Pull Requests

Stars

Forks

Maintainer

Matthew J. Butrim

Last Published

April 5th, 2024

Functions in dilp (1.1.0)

dilp_errors

Check for common errors in DiLP measurements
dilp-package

dilp: Reconstruct Paleoclimate and Paleoecology with Leaf Physiognomy
lma

Generate a suite of leaf mass per area results
calc_lma

Generate leaf mass per area results
dilp_processing

Process raw leaf physiognomic data
dilp_outliers

Identify outlier specimens
dilp

Generate DiLP results
dilp_cca

Test if site leaf physiognomy falls within the physiognomic space of the DiLP calibration dataset
temp_slr

Estimate temperature with simple linear regression
precip_slr

Estimate precipitation with simple linear regression
climate_calibration_data

Climate Calibration Data
McAbeeExample

McAbee Example Data
%>%

Pipe operator
physiognomy_calibration_data

Physiognomy Calibration Data
view_regressions

View preloaded regressions