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specleanr
View on CRAN: Click
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Download and install specleanr package within the R console
Install from CRAN:
install.packages("specleanr")
Install from Github:
library("remotes")
install_github("cran/specleanr") Install by package version:
library("remotes")
install_version("specleanr", "1.0.0") Attach the package and use:
library("specleanr")
Maintained by
Anthony Basooma
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-12-10
Latest Update: 2025-12-10
Description:
A framework used to detect and handle outliers during data analysis workflows. Outlier detection is a statistical concept with applications in data analysis workflows, highlighting records that are suspiciously high or low. Outlier detection in distribution models was initiated by Chapman (1991) (available at <https://www.researchgate.net/publication/332537800_Quality_control_and_validation_of_point-sourced_environmental_resource_data>), who developed the reverse jackknifing method. The concept was further developed and incorporated into different R packages, including 'flexsdm' (Velazco et al., 2022, <doi:10.1111/2041-210X.13874>) and 'biogeo' (Robertson et al., 2016 <doi:10.1111/ecog.02118>). We compiled various outlier detection methods obtained from the literature, including those elaborated in Dastjerdy et al. (2023) <doi:10.3390/geotechnics3020022> and Liu et al. (2008) <doi:10.1109/ICDM.2008.17>. In this package, we introduced the ensembling aspect, where multiple outlier detection methods are used to flag the record as either an absolute outlier. The concept can also be applied in general data analysis, as well as during the development of species distribution models.
How to cite:
Anthony Basooma (2025). specleanr: Detecting Environmental Outliers in Data Analysis Pipelines. R package version 1.0.0, https://cran.r-project.org/web/packages/specleanr. Accessed 05 Jun. 2026.
Previous versions and publish date:
1.0.0 (2025-11-25 21:20)
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