rLPJGUESS
An R interface to the LPJ-GUESS vegetation model, with parallel runs on workstations and HPC clusters.
Cited 19 times on Google Scholar (as of 29 October 2026)
rLPJGUESS runs LPJ-GUESS, a dynamic global vegetation model, from within R instead of through its own configuration files. Calibration and sensitivity analysis need hundreds to thousands of model runs, so the package runs them in parallel: on a workstation via SOCK clusters, on HPC systems via MPI. Combined with a sampler such as BayesianTools, this makes Bayesian calibration of LPJ-GUESS feasible.
The package targets LPJ-GUESS 3.1. It is not on CRAN and can be installed from GitHub.
Authors: Maurizio Bagnara (maintainer), Florian Hartig, Ramiro Silveyra Gonzalez.
Paper describing rLPJGUESS
Citations
rLPJGUESS has been cited 19 times on Google Scholar as of 29 October 2026. These are the citations of the paper describing it. The chart shows how they are distributed over the years, as of 21 October 2026; the 2026 figure covers the year up to that date.
Citations per year
Widely cited work citing rLPJGUESS
The most-cited articles that cite rLPJGUESS or its paper, ranked by their own citation count in OpenAlex as of 29 October 2026; the group's own papers are left out.
- A fundamental theorem for eco-environmental surface modelling and its applications
- Advancing simulations of water fluxes, soil moisture and drought stress by using the LWF-Brook90 hydrological model in R
- Regional carbon stock assessment and the potential effects of land cover change
- Parameter Estimation in Land Surface Models: Challenges and Opportunities With Data Assimilation and Machine Learning
- Heterogeneous impacts of human activities and climate change on transformed vegetation dynamics on the Qinghai-Tibet Plateau