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Showing content from https://github.com/AparicioJohan/flexFitR below:

AparicioJohan/flexFitR: flexFitR: Flexible Non-Linear Least Square Model Fitting in R.

{flexFitR} is an R package designed for efficient modeling and analysis of large and complex datasets. It offers powerful tools for parameter estimation, model fitting, and visualization, leveraging the {optimx} package for optimization and the {future} package for parallel processing.

Install released version from CRAN:

install.packages("flexFitR")

You can also install the development version of flexFitR from GitHub with:

# install.packages("devtools")
devtools::install_github("AparicioJohan/flexFitR")

• Parameter Estimation: Utilizes {optimx} algorithms to solve and estimate parameters for a given function.
• Parallelization: Implements parallel processing using the {future} package, enabling efficient fitting of hundreds of curves simultaneously.
• Visualization Tools: Provides a variety of plots to visualize model fits, correlations, predictions, derivatives, and more.
• Statistical Rigor: Offers standard errors and p-values for coefficients, as well as for predictions, supporting robust conclusions and interpretations.
• Prediction: Supports diverse prediction types, including point predictions, area under the curve (AUC), first and second derivatives, and custom expressions based on model parameters.
• Flexibility: Allows users to fix certain parameters in the model and specify different initial values per grouping factor, accepting both numerical inputs and expressions.
• Custom Modeling Functions: Equipped with built-in modeling functions for common analysis tasks, while also permitting users to supply their own custom functions.

Here’s a simple example to get you started with {flexFitR}. This example demonstrates fitting a piecewise regression model:

library(flexFitR)

dt <- data.frame(
  time = c(0, 29, 36, 42, 56, 76, 92, 100, 108),
  variable = c(0, 0, 0.67, 15.11, 77.38, 99.81, 99.81, 99.81, 99.81)
)
plot(explorer(dt, time, variable), type = "xy")

fn_lin_plat <- function(t, t1 = 45, t2 = 80, k = 0.9) {
  ifelse(
    test = t < t1,
    yes = 0,
    no = ifelse(t >= t1 & t <= t2, k / (t2 - t1) * (t - t1), k)
  )
}

# Fitting a linear plateau function
mod_1 <- dt |>
  modeler(
    x = time,
    y = variable,
    fn = "fn_lin_plat",
    parameters = c(t1 = 45, t2 = 80, k = 90)
  )

print(mod_1)

Call:
variable ~ fn_lin_plat(time, t1, t2, k) 

Residuals (`Standardized`):
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.2722  0.0000  2.4495 

Optimization Results `head()`:
 uid   t1 t2    k   sse
   1 38.6 61 99.8 0.449

Metrics:
 Groups      Timing Convergence Iterations
      1 0.1081 secs        100%   511 (id)

# Coefficients
coef(mod_1)
# A tibble: 3 × 7
    uid fn_name     coefficient solution std.error `t value` `Pr(>|t|)`
  <dbl> <chr>       <chr>          <dbl>     <dbl>     <dbl>      <dbl>
1     1 fn_lin_plat t1              38.6    0.0779      496.   4.54e-15
2     1 fn_lin_plat t2              61.0    0.0918      665.   7.82e-16
3     1 fn_lin_plat k               99.8    0.137       730.   4.47e-16

# Variance-Covariance Matrix
vcov(mod_1)
$`1`
              t1           t2            k
t1  6.061705e-03 -0.002940001 1.877072e-07
t2 -2.940001e-03  0.008431400 4.204939e-03
k   1.877072e-07  0.004204939 1.870426e-02
attr(,"fn_name")
[1] "fn_lin_plat"

# Making predictions
predict(mod_1, x = 45)
# A tibble: 1 × 5
    uid fn_name     x_new predicted.value std.error
  <dbl> <chr>       <dbl>           <dbl>     <dbl>
1     1 fn_lin_plat    45            28.5     0.223

For detailed documentation and examples, visit flexFitR

• Vignette 1: How to start
• Vignette 2: Modeling plant emergence and canopy growth using UAV data
• Vignette 3: Modeling with constraints
• Vignette 4: Making predictions
• Vignette 5: Plotting options

Contributions to flexFitR are welcome! If you’d like to contribute, please fork the repository and submit a pull request. For significant changes, please open an issue first to discuss your ideas.

Please note that the flexFitR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

flexFitR is licensed under the MIT License. See the LICENSE file for more details.

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