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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r, warning=FALSE, message=FALSE, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
dpi = 300,
fig.path = "man/figures/",
comment = "#>",
warning = FALSE,
message = FALSE,
out.width = "100%"
)
set.seed(333)
library(parameters)
```
# parameters <img src="man/figures/logo.png" align="right" height="139" />
[](https://doi.org/10.21105/joss.02445)
[](https://cran.r-project.org/package=parameters)
[](https://cranlogs.r-pkg.org/)
[](https://CRAN.R-project.org/package=parameters)
***Describe and understand your model's parameters!***
**parameters**' primary goal is to provide utilities for processing the parameters of various statistical models (see [here](https://easystats.github.io/insight/) for a list of supported models). Beyond computing *p-values*, *CIs*, *Bayesian indices* and other measures for a wide variety of models, this package implements features like *bootstrapping* of parameters and models, *feature reduction* (feature extraction and variable selection), or tools for data reduction like functions to perform cluster, factor or principal component analysis.
Another important goal of the **parameters** package is to facilitate and streamline the process of reporting results of statistical models, which includes the easy and intuitive calculation of standardized estimates or robust standard errors and p-values. **parameters** therefor offers a simple and unified syntax to process a large variety of (model) objects from many different packages.
## Installation
[](https://cran.r-project.org/package=parameters) [](https://easystats.r-universe.dev) [](https://github.com/easystats/parameters/actions)
Type | Source | Command
---|---|---
Release | CRAN | `install.packages("parameters")`
Development | r - universe | `install.packages("parameters", repos = "https://easystats.r-universe.dev")`
Development | GitHub | `remotes::install_github("easystats/parameters")`
> **Tip**
>
> Instead of `library(parameters)`, use `library(easystats)`. This will make all features of the easystats-ecosystem available.
>
> To stay updated, use `easystats::install_latest()`.
## Documentation
[](https://easystats.github.io/parameters/)
[](https://easystats.github.io/blog/posts/)
[](https://easystats.github.io/parameters/reference/index.html)
Click on the buttons above to access the package [documentation](https://easystats.github.io/parameters/) and the [easystats blog](https://easystats.github.io/blog/posts/), and check-out these vignettes:
- [Summary of Model Parameters](https://easystats.github.io/parameters/articles/model_parameters.html)
- [Parameter and Model Standardization](https://easystats.github.io/parameters/articles/standardize_parameters_effsize.html)
- [Robust Estimation of Standard Errors, Confidence Intervals and p-values](https://easystats.github.io/parameters/articles/model_parameters_robust.html)
- [Model Parameters and Missing Data](https://easystats.github.io/parameters/articles/model_parameters_mice.html)
- [Feature reduction (PCA, cMDS, ICA...)](https://easystats.github.io/parameters/articles/parameters_reduction.html)
- [Structural models (EFA, CFA, SEM...)](https://easystats.github.io/parameters/articles/efa_cfa.html)
- [Parameters selection](https://easystats.github.io/parameters/articles/parameters_selection.html)
- [A Practical Guide for Panel Data Analysis](https://easystats.github.io/parameters/articles/demean.html)
- [Plotting functions](https://easystats.github.io/see/articles/parameters.html)
## Contributing and Support
In case you want to file an issue or contribute in another way to the package, please follow [this guide](https://github.com/easystats/parameters/blob/main/.github/CONTRIBUTING.md). For questions about the functionality, you may either contact us via email or also file an issue.
# Features
## Model's parameters description
```{r echo=FALSE, fig.align='center', dpi=96}
knitr::include_graphics("man/figures/figure1.png")
```
The [`model_parameters()`](https://easystats.github.io/parameters/articles/model_parameters.html) function (that can be accessed via the `parameters()` shortcut) allows you to extract the parameters and their characteristics from various models in a consistent way. It can be considered as a lightweight alternative to [`broom::tidy()`](https://github.com/tidymodels/broom), with some notable differences:
- The column names of the returned data frame are *specific* to their content. For instance, the column containing the statistic is named following the statistic name, i.e., *t*, *z*, etc., instead of a generic name such as *statistic* (however, you can get standardized (generic) column names using [`standardize_names()`](https://easystats.github.io/insight/reference/standardize_names.html)).
- It is able to compute or extract indices not available by default, such as *p-values*, *CIs*, etc.
- It includes *feature engineering* capabilities, including parameters [bootstrapping](https://easystats.github.io/parameters/reference/bootstrap_parameters.html).
### Classical Regression Models
```{r}
model <- lm(Sepal.Width ~ Petal.Length * Species + Petal.Width, data = iris)
# regular model parameters
model_parameters(model)
# standardized parameters
model_parameters(model, standardize = "refit")
# heteroscedasticity-consitent SE and CI
model_parameters(model, vcov = "HC3")
```
### Mixed Models
```{r}
library(lme4)
model <- lmer(Sepal.Width ~ Petal.Length + (1 | Species), data = iris)
# model parameters with CI, df and p-values based on Wald approximation
model_parameters(model)
# model parameters with CI, df and p-values based on Kenward-Roger approximation
model_parameters(model, ci_method = "kenward", effects = "fixed")
```
### Structural Models
Besides many types of regression models and packages, it also works for other types of models, such as [**structural models**](https://easystats.github.io/parameters/articles/efa_cfa.html) (EFA, CFA, SEM...).
```{r, warning=FALSE, message=FALSE}
library(psych)
model <- psych::fa(attitude, nfactors = 3)
model_parameters(model)
```
## Variable and parameters selection
```{r echo=FALSE, fig.align='center', dpi=96}
knitr::include_graphics("man/figures/figure2.png")
```
[`select_parameters()`](https://easystats.github.io/parameters/articles/parameters_selection.html) can help you quickly select and retain the most relevant predictors using methods tailored for the model type.
```{r, warning=FALSE, message=FALSE}
lm(disp ~ ., data = mtcars) |>
select_parameters() |>
model_parameters()
```
## Citation
In order to cite this package, please use the following command:
```{r, comment=""}
citation("parameters")
```
## Code of Conduct
Please note that the parameters project is released with a [Contributor Code of Conduct](https://www.contributor-covenant.org/version/2/1/code_of_conduct/). By contributing to this project, you agree to abide by its terms.