Package: CroptimizR 1.0.0

Samuel Buis

CroptimizR: A Package to Estimate Parameters of Crop Models

The purpose of CroptimizR is to provide functions for estimating crop model parameters from observations of their simulated variables. This process is often referred to as calibration. For that, it offers a generic framework for linking crop models with up-to-date and ad-hoc algorithms, as well as a choice of goodness-of-fit criteria and additional features adapted to the problem of crop model calibration including AgMIP calibration protocol and user-defined sequential multi-step workflow. It facilitates the comparison of different types of methods on different models.

Authors:Samuel Buis [aut, cre], Patrice Lecharpentier [aut], Remi Vezy [aut], Michel Giner [aut], Drew Holzworth [ctb], Henrike Mielenz [ctb], Taru Palosuo [ctb], Thomas Robine [ctb], Sabine Seidel [ctb], Peter Thorburn [ctb], Daniel Wallach [ctb], Theo Vailhere [ctb], Julie Constantin [rev], Benjamin Dumont [rev]

CroptimizR_1.0.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
CroptimizR/json (API)

# Install 'CroptimizR' in R:
install.packages('CroptimizR', repos = c('https://sticsrpacks.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sticsrpacks/croptimizr/issues

On CRAN:

Conda:

crop-modelparameter-estimationsensitivity-analysisuncertainty-analysis

7.43 score 32 stars 1 packages 14 scripts 20 exports 125 dependencies

Last updated from:3a10fb358d. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE505
source / vignettesOK256
linux-release-x86_64NOTE482
macos-release-arm64NOTE328
macos-oldrel-arm64NOTE373
windows-develNOTE468
windows-releaseNOTE477
windows-oldrelNOTE541
wasm-releaseOK222

Exports:AICAICcBICcrit_log_cwsscrit_log_cwss_corrcrit_olscrit_wlsestim_paramfilter_obsget_agmip_protocol_exampleget_agmip_protocol_templateget_obs_varlikelihood_log_ciidnlikelihood_log_ciidn_corrload_protocol_agmipplot_estimVSinitplot_valuesVSitplot_valuesVSit_2Drun_protocol_agmiptest_wrapper

Dependencies:apeaskpassbase64encBayesianToolsbootbridgesamplingBrobdingnagbslibcachemcellrangerclicodacodetoolscommonmarkcpp11crayonCroPlotRcrosstalkcurldata.tableDHARMadigestdoParalleldplyrellipseemulatorevaluateexpmfarverfastmapfontawesomeforeachfsgapgap.datasetsgenericsggh4xggplot2ggrepelgluegmmgridExtragtablehighrhmshtmltoolshtmlwidgetshttpuvhttrIDPmiscisobanditeratorsjquerylibjsonliteknitrlabelinglaterlatticelazyevallhslifecyclelme4lmtestlubridatemagrittrMASSMatrixmemoisemgcvmimeminqamsmmvtnormnlmenloptrnumDerivopensslotelpillarpkgconfigplotlyplyrprettyunitsprogresspromisespurrrqgamR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreadxlreformulasrematchreshape2rlangrmarkdownS7sandwichsassscalesshinysourcetoolsstringistringrsurvivalsystibbletictoctidyrtidyselecttimechangetinytextmvtnormutf8vctrsviridisLitewithrxfunxtableyamlzoo

Calibrating a complex crop model using the AgMIP protocol
Introduction | Scientific and technical context | The AgMIP calibration protocol | The STICS model and CroptimizR wrappers | The dataset | Observations and data formatting | Observed variables | The cropr format | Describing the calibration protocol | Principle | Excel-based specification | Converting the Excel file to R objects | Structure of the protocol for this example | Example: the phenology group | Parameter information | Running the protocol | Exploring the results | Global structure of the returned object | Exploring parameter selection results (Step 6) | Built-in diagnostics plots | Evaluating the results on an independent dataset | Conclusion
Rendered fromAgMIP_Calibration_Protocol.Rmdusingknitr::rmarkdown

Last update: 2026-02-09
Started: 2026-01-31

CroptimizR
Introduction | Concepts | Parameter estimation | Specific features and interest of CroptimizR | Principle of use | Model wrappers | Observations | Examples | Model evaluation | References
Rendered fromCroptimizR.Rmdusingknitr::rmarkdown

Last update: 2026-01-31
Started: 2022-08-23

Guidelines for implementing a crop model R wrapper for CroptimizR
Introduction | How to write a basic version of a model wrapper for CroptimizR | Model wrapper interface | Minimum required functionalities | Implementation | Test your wrapper | Optional issues | Examples | Example of a basic wrapper for an LAI toy model | Another version taking into account the different shapes param_values can take | Other examples
Rendered fromDesigning_a_model_wrapper.Rmdusingknitr::rmarkdown

Last update: 2026-01-31
Started: 2019-11-15

Parameter estimation with CroptimizR: a case with specific and varietal parameters
Introduction | Study Case | Initialisation step | Read and select the corresponding observations | Set information on the parameters to estimate | Set options for the model | Set options for the parameter estimation method | Run the optimization | Compare simulations and observations before and after optimization
Rendered fromParameter_estimation_Specific_and_Varietal.Rmdusingknitr::rmarkdown

Last update: 2026-01-31
Started: 2019-11-15

Parameter estimation with CroptimizR: a simple case using the ApsimX crop Model
Introduction | Study Case | Initialisation step | Set the list of situations and variables to consider in this example | Run the model before optimization for a prior evaluation | Read and select the corresponding observations | Set information on the parameters to estimate | Set options for the parameter estimation method | Run the optimization | Run the model after optimization | Plot the results
Rendered fromApsimX_parameter_estimation_simple_case.Rmdusingknitr::rmarkdown

Last update: 2026-01-31
Started: 2019-11-15

Parameter estimation with CroptimizR: a simple case using the STICS crop Model
Introduction | Study Case | Initialisation step | Generate Stics input files from JavaStics input files | Run the model before optimization for a prior evaluation | Read and select the observations for the parameter estimation | Set information on the parameters to estimate | Set options for the parameter estimation method | Run the optimization | Run the model after optimization | Plot the results
Rendered fromParameter_estimation_simple_case.Rmdusingknitr::rmarkdown

Last update: 2026-01-31
Started: 2019-10-17

Parameter estimation with the DREAM-zs algorithm
Introduction | Study Case | Initialisation step | Read and select the corresponding observations | Set information on the parameters to estimate | Set options for the model | Set options for the DREAM method | Run the parameter estimation | Launch a new estimation starting from previous results
Rendered fromParameter_estimation_DREAM.Rmdusingknitr::rmarkdown

Last update: 2026-01-31
Started: 2020-02-02

Parameter selection with CroptimizR
Introduction | Study Case | Plotting the observations | Setting information on the parameters to estimate | Choosing the default parameters values | Setting optimization options | Running the optimization | Generating diagnostics using CroPlotR
Rendered fromParameter_selection.Rmdusingknitr::rmarkdown

Last update: 2026-01-31
Started: 2026-01-31

Available parameter estimation algorithms in CroptimizR
Frequentist algorithms | Bayesian algorithms | Criteria and likelihoods | References
Rendered fromAvailable_parameter_estimation_algorithms.Rmdusingknitr::rmarkdown

Last update: 2022-10-07
Started: 2020-02-02