aberrance

Detect Aberrant Behavior in Test Data

https://github.com/kyliegorney/aberrance

Science Score: 26.0%

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  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
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  • Scientific vocabulary similarity
    Low similarity (11.7%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Detect Aberrant Behavior in Test Data

Basic Info
  • Host: GitHub
  • Owner: kyliegorney
  • License: gpl-3.0
  • Language: R
  • Default Branch: main
  • Homepage:
  • Size: 68.4 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created 10 months ago · Last pushed 10 months ago
Metadata Files
Readme Changelog License

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# aberrance


[![](https://www.r-pkg.org/badges/version/aberrance)](https://CRAN.R-project.org/package=aberrance)
[![](https://cranlogs.r-pkg.org/badges/grand-total/aberrance)](https://CRAN.R-project.org/package=aberrance)
[![](https://cranlogs.r-pkg.org/badges/aberrance)](https://CRAN.R-project.org/package=aberrance)


The *aberrance* package contains a collection of functions for detecting several types of aberrant behavior, including:

- **Answer copying**, using statistics such as the $\omega$ statistic (Wollack, 1997).
- **Answer similarity**, using statistics such as the $GBT$ statistic (van der Linden & Sotaridona, 2006) and the $M4$ statistic (Maynes, 2014).
- **Change point**, using statistics such as the likelihood ratio test-based statistic, the score test-based statistic, and the Wald test-based statistic (Shao et al., 2016; Sinharay, 2016; Tu et al., 2023).
- **Nonparametric misfit**, using statistics such as the $ZU3$ statistic (van der Flier, 1982) and the $H^T$ statistic (Sijtsma, 1986).
- **Parametric misfit**, using statistics such as the standardized log-likelihood statistic (Drasgow et al., 1985) and its various corrections (Bedrick, 1997; Gorney et al., 2024; Molenaar & Hoijtink, 1990; Snijders, 2001).
- **Preknowledge**, using statistics such as the signed likelihood ratio test statistic (Sinharay, 2017).
- **Rapid guessing**, using methods such as the custom threshold method (Wise et al., 2004; Wise & Kong, 2005), the normative threshold method (Wise & Ma, 2012), and the cumulative proportion correct method (Guo et al., 2016).
- **Test tampering**, using statistics such as the erasure detection index (Wollack et al., 2015; Wollack & Eckerly, 2017) and its corrected versions (Sinharay, 2018).

## Installation

Install the released version from CRAN:
```{r CRAN, eval = FALSE}
install.packages("aberrance")
```

Alternatively, install the development version from GitHub:

```{r GitHub, eval = FALSE}
# install.packages("devtools")
devtools::install_github("kyliegorney/aberrance")
```

Owner

  • Name: Kylie Gorney
  • Login: kyliegorney
  • Kind: user

Assistant Professor at Michigan State University

GitHub Events

Total
  • Release event: 3
  • Watch event: 1
  • Push event: 16
  • Create event: 6
Last Year
  • Release event: 3
  • Watch event: 1
  • Push event: 16
  • Create event: 6

Packages

  • Total packages: 1
  • Total downloads:
    • cran 290 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
cran.r-project.org: aberrance

Detect Aberrant Behavior in Test Data

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 290 Last month
Rankings
Dependent packages count: 28.2%
Dependent repos count: 36.1%
Average: 49.7%
Downloads: 84.7%
Maintainers (1)
Last synced: 10 months ago

Dependencies

DESCRIPTION cran
  • MASS * imports
  • Rcpp >= 0.11.0 imports
  • grDevices * imports
  • graphics * imports
  • stats * imports
  • utils * imports