flexfitr

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

https://github.com/apariciojohan/flexfitr

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

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "",
  fig.path = "man/figures/README_",
  fig.dpi = 600,
  out.width = "100%",
  warning = FALSE,
  message = FALSE
)
```

# {flexFitR} 


[![CRAN status](https://www.r-pkg.org/badges/version/flexFitR)](https://CRAN.R-project.org/package=flexFitR)
[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
![Minimal R Version](https://img.shields.io/badge/R%3E%3D-4.1-blue.svg)


`{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.

## Installation

Install released version from CRAN:
``` r
install.packages("flexFitR")
```

You can also install the development version of flexFitR from [GitHub](https://github.com/AparicioJohan/flexFitR) with:

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

## Features

* 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.

## Example

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

```{r, fig.dpi=600, fig.height=3, fig.alt= "plot xy"}
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")
```

```{r, fig.alt= "plot fun"}
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)
  )
}
```

```{r}
# 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)
  )
```

```{r}
print(mod_1)
```


```{r, fig.alt= "plot fin", fig.height=3}
# Auto plot
plot(mod_1)
```

```{r}
# Coefficients
coef(mod_1)
```

```{r}
# Variance-Covariance Matrix
vcov(mod_1)
```

```{r}
# Making predictions
predict(mod_1, x = 45)
```

## Documentation

For detailed documentation and examples, visit [flexFitR](https://apariciojohan.github.io/flexFitR/reference/index.html) 

* Vignette 1: [How to start](https://apariciojohan.github.io/flexFitR/articles/how_to_start.html) 
* Vignette 2: [Modeling plant emergence and canopy growth using UAV data](https://apariciojohan.github.io/flexFitR/articles/canopy-model.html)
* Vignette 3: [Modeling with constraints](https://apariciojohan.github.io/flexFitR/articles/maturity-model.html) 
* Vignette 4: [Making predictions ](https://apariciojohan.github.io/flexFitR/articles/predicted_values.html) 
* Vignette 5: [Plotting options](https://apariciojohan.github.io/flexFitR/articles/plotting.html) 

## Contributing

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.

## Code of Conduct

Please note that the flexFitR project is released with a [Contributor Code of Conduct](https://apariciojohan.github.io/flexFitR/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

## License

flexFitR is licensed under the MIT License. See the [LICENSE](https://apariciojohan.github.io/flexFitR/LICENSE.html) file for more details.

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