This package is under active development. APIs may change.

statim

Higher Level Interface for Statistical Inference

Package Overview

What does statim mean?

statim is a Latin word for “immediately, at once”. The name carries a double meaning:

• stat: as in statistics, the domain this package lives in
• im (statim): as in immediate, signalling that inference should be expressible as a direct declaration, not somewhat a sequence of mechanical steps

This simply means: you declare what statistical inference you want to perform, then statim immediately delivers how.

Installation

The package is yet to be submitted into CRAN.

# Stable version (not yet released)
install.packages("statim")

For the time being, you can install the current implementation on GitHub:

# Development version from GitHub
# install.packages("pak")
pak::pak("joshuamarie/statim")

Usages

statim provides a human-readable, easy-to-write syntax. statim is a much higher-level package, inherits most of strongest features in R that makes the code easy to write and understand, such as dplyr / tidyr’s use of <tidyselect-helpers>. This re-imagines the grammar of statistical inference implementation in R. It embodies almost the same paradigm as ggplot2, except it enforces the use of actual pipes, not +.

library(statim)

Here’s an example of a quick H-test pipeline:

1. tidyverse-like grammar semantics

   sleep |>
       define_model(x_by(extra, group)) |>
       prepare_test(TTEST) |>
       conclude()

   #> -- Summary ---------------------------------------------------------------------
   #> 
   #> ─────────────────────────────────
   #>   groups   diff   t-stat  pval   
   #> ─────────────────────────────────
   #>   group   -1.580  -1.861  0.079  
   #> ─────────────────────────────────
   #> 
   #> 
   #> -- Confidence Interval ---------------------------------------------------------
   #> 
   #> ──────────────────────────────
   #>   groups  lower_95  upper_95  
   #> ──────────────────────────────
   #>   group    -3.365    0.205    
   #> ──────────────────────────────

   sleep |>
       define_model(x_by(extra, group)) |>
       prepare_test(TTEST) |>
       via("permute", n = 500L) |> 
       conclude()

   #> ============================== T-test Permutation ==============================
   #> 
   #> 
   #> -- Summary ---------------------------------------------------------------------
   #> 
   #> ───────────────────────────────
   #>   Statistic  p-value  n_perms  
   #> ───────────────────────────────
   #>    -1.580     0.082     500    
   #> ───────────────────────────────

   The bottom part of this code is the part where the “pipeline” is re-calibrated from the classical t-test procedure, into permutation t-test.

2. 1-liner syntax

   TTEST(x_by(extra, group), sleep)

   #> -- Summary ---------------------------------------------------------------------
   #> 
   #> ─────────────────────────────────
   #>   groups   diff   t-stat  pval   
   #> ─────────────────────────────────
   #>   group   -1.580  -1.861  0.079  
   #> ─────────────────────────────────
   #> 
   #> 
   #> -- Confidence Interval ---------------------------------------------------------
   #> 
   #> ──────────────────────────────
   #>   groups  lower_95  upper_95  
   #> ──────────────────────────────
   #>   group    -3.365    0.205    
   #> ──────────────────────────────

1. is better since you can re-calibrate the declaration of the H-test you want to perform. Otherwise, (2) if you want to eagerly run the actual H-test.

Core Ideas

The package is designed around three ideas:

1. Declarative models: describe the structure of your data with define_model() and model IDs like x_by(), rel(), and pairwise()
2. Composable pipeline: build up a test specification lazily, then execute with conclude()
3. Extensible implementations: every test is a test_define() object; bring your own engine, your own method, your own implementation

License

MIT © Joshua Marie

Contributing

We are sincerely grateful for contributions; they are beneficial for the project and for us as maintainers. Please read CONTRIBUTING.md for development setup, pull request guidelines, and workflow notes.

Code of Conduct

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