Package: terminaldigits 0.1.0

Josh McCormick

terminaldigits: Tests of Uniformity and Independence for Terminal Digits

Implements simulated tests for the hypothesis that terminal digits are uniformly distributed (chi-squared goodness-of-fit) and the hypothesis that terminal digits are independent from preceding digits (several tests of independence for r x c contingency tables). Also, for a number of distributions, implements Monte Carlo simulations for type I errors and power for the test of independence.

Authors:Josh McCormick [aut, cre]

terminaldigits_0.1.0.tar.gz
terminaldigits_0.1.0.tar.gz(r-4.5-noble)terminaldigits_0.1.0.tar.gz(r-4.4-noble)
terminaldigits_0.1.0.tgz(r-4.4-emscripten)terminaldigits_0.1.0.tgz(r-4.3-emscripten)
terminaldigits.pdf |terminaldigits.html✨
terminaldigits/json (API)

# Install 'terminaldigits' in R:
install.packages('terminaldigits', repos = 'https://cloud.r-project.org')
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • decoy - 3,320 observations from a decoy experiment

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

2.70 score 122 downloads 4 exports 2 dependencies

Last updated 3 years agofrom:357c4bf131. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 27 2025
R-4.5-linux-x86_64OKMar 27 2025
R-4.4-linux-x86_64OKMar 27 2025

Exports:td_independencetd_simulatetd_teststd_uniformity

Dependencies:discretefitRcpp

Package Introduction

Josh McCormick

Rendered frompackages_introduction.Rmdusingknitr::rmarkdownon Mar 27 2025.

Last update: 2022-05-13
Started: 2022-05-13

Citation

To cite package ‘terminaldigits’ in publications use:

McCormick J (2022). terminaldigits: Tests of Uniformity and Independence for Terminal Digits. R package version 0.1.0, https://CRAN.R-project.org/package=terminaldigits.

Corresponding BibTeX entry: 

  @Manual{,
    title = {terminaldigits: Tests of Uniformity and Independence for
      Terminal Digits},
    author = {Josh McCormick},
    year = {2022},
    note = {R package version 0.1.0},
    url = {https://CRAN.R-project.org/package=terminaldigits},
  }

Readme and manuals

terminaldigits

The package terminaldigits implements simulated tests of uniformity and independence for terminal digits. For certain parameters, terminaldigits also implements Monte Carlo simulations for type I errors and power for the test of independence. Simulations are run in C++ utilizing Rcpp.

Installation

You can install the development version of terminaldigits from GitHub with:

# install.packages("devtools")
devtools::install_github("josh-mc/terminaldigits")

Usage

In many cases, terminal digits can be assumed to be uniformly distributed and independent of preceding digits. A violation of either of these assumptions may point to a data quality issue.

The following examples are based on a data set taken from the third round of a decoy experiment involving hand-washing purportedly carried out in a number of factories in China. For details, see decoy and Yu, Nelson, and Simonsohn (2018).

The td_uniformity function tests the assumption of uniformity using Pearson’s chi-squared statistic for goodness-of-fit.

library(terminaldigits)

td_uniformity(decoy$weight, decimals = 2, reps = 1000)
#> 
#>  Pearson's chi-squared GOF test for uniformity of terminal digits
#> 
#> data:  decoy$weight
#> Chi-squared = 539.67, p-value = 0.000999

The td_independence function tests the assumption of independence. The default statistic is again Pearson’s chi-squared statistic but the log-likelihood ratio statistic, the Freeman-Tukey statistic, and the root-mean-square statistic are also available.

td_independence(decoy$weight, decimals = 2, reps = 1000)
#> 
#>  Chisq test for independence of terminal digits
#> 
#> data:  decoy$weight
#> Chisq = 6422.4, p-value = 0.000999

The td_test function is a wrapper for the above two functions. For more details, including a discussion of the td_simulate function, see the package introduction vignette.

References

Yu, F., Nelson, L., & Simonsohn, U. (2018, December 5). “In Press at Psychological Science: A New ‘Nudge’ Supported by Implausible Data.” DataColoda 74. http://datacolada.org/74