Package: flexFitR 1.2.4

flexFitR: Flexible Non-Linear Least Square Model Fitting

Provides tools for flexible non-linear least squares model fitting using general-purpose optimization techniques. The package supports a variety of optimization algorithms, including those provided by the 'optimx' package, making it suitable for handling complex non-linear models. Features include parallel processing support via the 'future' and 'foreach' packages, comprehensive model diagnostics, and visualization capabilities. Implements methods described in Nash and Varadhan (2011, <doi:10.18637/jss.v043.i09>).

Authors:Johan Aparicio [cre, aut], Jeffrey Endelman [aut], University of Wisconsin Madison [cph]

flexFitR_1.2.4.tar.gz
flexFitR_1.2.4.zip(r-4.7-any)flexFitR_1.2.4.zip(r-4.6-any)flexFitR_1.2.4.zip(r-4.5-any)
flexFitR_1.2.4.tgz(r-4.6-any)flexFitR_1.2.4.tgz(r-4.5-any)
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flexFitR_1.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
flexFitR/json (API)

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

Bug tracker:https://github.com/apariciojohan/flexfitr/issues

Pkgdown/docs site:https://apariciojohan.github.io

Datasets:
  • dt_potato - Drone-derived data from a potato breeding trial
  • dt_potato_22 - Drone-derived data from a potato breeding trial
  • dt_soybean_22 - Soybean canopy cover from HTP platform

On CRAN:

Conda:

nlsoptimization

7.77 score 9 stars 186 scripts 426 downloads 35 exports 113 dependencies

Last updated from:eb7c700ac5. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK227
source / vignettesOK309
linux-releaseOK258
macos-releaseOK187
macos-oldrelOK163
windows-develOK163
windows-releaseOK154
windows-oldrelOK111
wasm-releaseOK164

Exports:.delta_method.delta_method_auc.delta_method_deriv.delta_method_gen.fitter_curveaugmentcompute_tangentexplorerffff_aucff_derivfn_exp_expfn_exp_linfn_exp2_expfn_exp2_linfn_linfn_lin_logisfn_lin_pl_linfn_lin_platfn_lllfn_logisticfn_lplfn_qplfn_quadfn_quad_pl_smfn_quad_platinverse_predictlist_funslist_methodsmetricsmodel_selectionmodelerperformanceplot_fnseries_mutate

Dependencies:abindagriutilitiesbackportsbootbroomcarcarDataclicodetoolscolorspacecorrplotcowplotcpp11data.tableDerivdigestdoBydoFuturedotCall64dplyremmeansestimabilityevaluatefarverfieldsforeachforecastFormulafracdifffuturefuture.applygenericsggplot2ggpubrggrepelggsciggsignifglobalsglueGPArotationgridExtragtablehighrisobanditeratorsknitrlabelinglatticelifecyclelistenvlme4lmerTestlmtestmagrittrmapprojmapsMASSMatrixMatrixModelsmgcvminqamnormtmodelrmvtnormnlmenloptrnnetnumDerivoptimxparallellypbkrtestpillarpkgconfigpolynompracmaprogressrpsychpurrrqtlquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangrstatixS7scalesspamSparseMSpATSstatgenSTAstringistringrsubplexsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsviridisLitewithrxfunxtableyamlzoo

Model selection
Choosing a different model for every plot | 1. Exploring data | 2. Candidate models | 3. Fitting the candidates | 4. Comparing by hand with performance() | 5. Automatic selection | 6. The selected object behaves like any other fit | 7. Which criterion should you rank on? | 8. When a plot cannot be compared | Summary
Rendered frommodel_selection.Rmdusingknitr::rmarkdown

Last update: 2026-09-09
Started: 2026-09-09

How to start
Getting started | 1. Simple linear regression | Comparison with lm | 2. Piece-wise regression | Comparison with nls | Providing Initial values | Fixing parameters
Rendered fromhow_to_start.Rmdusingknitr::rmarkdown

Last update: 2026-09-08
Started: 2024-08-20

Modeling soybean canopy cover
Estimating soybean canopy development phases | 1. Exploring data | 2. Regression function | 3. Fitting models | 3.1. Extracting model coefficients and uncertainty measures | 4. Plotting options | 5. Deriving canopy development traits | 6. Modeling all plots using parallel processing | 7. Conclusion | References
Rendered frommodeling_soybean.Rmdusingknitr::rmarkdown

Last update: 2026-08-05
Started: 2026-08-05

Modeling with constraints
Introduction to Modeling with Constraints | Example Case | Loading libraries | 1. Exploring data | 2. Regression function | 3. Fitting Models with Constraints | 3.1. Extracting model coefficients and uncertainty measures | 4. Plotting options | 5. Conclusion
Rendered frommaturity-model.Rmdusingknitr::rmarkdown

Last update: 2026-01-27
Started: 2024-07-01

Plotting options
Loading dataset and libraries | Modeling | Plotting predictions and derivatives
Rendered fromplotting.Rmdusingknitr::rmarkdown

Last update: 2025-03-26
Started: 2024-09-23

Predicted values
0. Model fitting | 1. Point prediction | 2. Integral of the function (area under the curve) | 3. Function of the parameters | 4. Derivatives | 4.1. First derivative | 4.2. Second derivative | 5. Conclusions
Rendered frompredicted_values.Rmdusingknitr::rmarkdown

Last update: 2025-03-26
Started: 2024-11-22

Modeling
Modeling plant emergence and canopy growth using UAV data | Loading libraries | 1. Exploring data | 2. Regression Function | 3. Fitting Models | 3.1. Extracting model coefficients and uncertainty measures | 4. Providing different initial values | 5. Fixing some parameters of the model | 6. Comparing estimations | 7. Making predictions | 8. Modelling all plots using parallel processing
Rendered fromcanopy-model.Rmdusingknitr::rmarkdown

Last update: 2025-03-26
Started: 2024-06-30

Readme and manuals

Help Manual

Help pageTopics
Extra Sum-of-Squares F-Test for 'modeler' objectsanova.modeler
Augment a 'modeler' object with influence diagnosticsaugment
Combine objects of class 'modeler'c.modeler
Coefficients for an object of class 'modeler'coef.modeler
Compute tangent line(s) from a 'modeler' objectcompute_tangent
Confidence intervals for a modeler objectconfint.modeler
Drone-derived data from a potato breeding trial (2020)dt_potato
Drone-derived data from a potato breeding trial (2022)dt_potato_22
Soybean canopy cover from HTP platform (Keller et all., 2026)dt_soybean_22
Explore dataexplorer
Extract fitted values from a 'modeler' objectfitted.modeler
Double-exponential functionfn_exp_exp
Exponential-linear functionfn_exp_lin
Super-exponential exponential functionfn_exp2_exp
Super-exponential linear functionfn_exp2_lin
Linear functionfn_lin
Linear-logistic functionfn_lin_logis
Linear plateau linear functionfn_lin_pl_lin
Linear plateau functionfn_lin_plat
Linear–logistic–linear functionfn_lll
Logistic functionfn_logistic
Linear plateau linear with constrainsfn_lpl
Quadratic–plateau–linear functionfn_qpl
Quadratic functionfn_quad
Smooth Quadratic-plateau functionfn_quad_pl_sm
Quadratic-plateau functionfn_quad_plat
Akaike's An Information Criterion for an object of class 'modeler'AIC.modeler BIC.modeler goodness_of_fit
Inverse prediction from a 'modeler' objectinverse_predict.modeler
Print available functions in flexFitRlist_funs
Print available methods in flexFitRlist_methods
Extract Log-Likelihood for an object of class 'modeler'logLik.modeler
Metrics for an object of class 'modeler'metrics
Select the best model for each groupmodel_selection
Modeler: Non-linear regression for curve fittingmodeler
Compare performance of different modelsperformance
Plot user-defined functionplot_fn
Plot an object of class 'explorer'plot.explorer
Plot an object of class 'modeler'plot.modeler
Plot an object of class 'performance'plot.performance
Predict an object of class 'modeler'predict.modeler
Print an object of class 'modeler'print.modeler
Extract residuals from a 'modeler' objectresiduals.modeler
Transform variables in a data frameseries_mutate
Subset an object of class 'modeler'subset.modeler
Update a 'modeler' objectupdate.modeler
Variance-Covariance matrix for an object of class 'modeler'vcov.modeler