Outlier detection for replicated high-throughput data
c(person(given = "Soo-Heang", family = "Eo", role = c("aut","cre"),
email = "eo.sooheang@gmail.com"),
person(given = "HyungJun", family = "Cho", role = "aut",
email = "hj4cho@korea.ac.kr"))
Detecting outlying observations for replicated high-throughput high-dimensional data based on a MA plot with quantile regression approach.
R (>= 3.0.0), quantreg, MatrixModels, outliers, pcaPP
Soo-Heang Eo [aut, cre],
HyungJun Cho [aut]

Outlier Detection for Multi-replicated High-throughput Data
Detecting outlying values such as genes, peptides or samples for multi-replicated high-throughput high-dimensional data
quantreg, MatrixModels, outliers, pcaPP, methods, graphics

To detect outliers using different methods namely model based outlier detection (Barnett, V. 1978 <https://www.jstor.org/stable/2347159>), distance based outlier detection (Hautamaki, V., Karkkainen, I., and Franti, P. 2004 <http://cs.uef.fi/~franti/papers.html>), dispersion based outlier detection (Jin, W., Tung, A., and Han, J. 2001 <https://link.springer.com/chapter/10.1007/0-387-25465-X_7>), depth based outlier detection (Johnson, T., Kwok, I., and Ng, R.T. 1998 <http://www.aaai.org/Library/KDD/1998/kdd98-038.php>) and density based outlier detection (Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. 1996 <https://dl.acm.org/citation.cfm?id=3001507>). This package provides labelling of observations as outliers and outlierliness of each outlier. For univariate and bivariate data, visualization is also provided.

To detect outliers using different methods namely model based outlier detection (Barnett, V. 1978 <https://www.jstor.org/stable/2347159>), distance based outlier detection (Hautamaki, V., Karkkainen, I., and Franti, P. 2004 <http://cs.uef.fi/~franti/papers.html>), dispersion based outlier detection (Jin, W., Tung, A., and Han, J. 2001 <https://link.springer.com/chapter/10.1007/0-387-25465-X_7>), depth based outlier detection (Johnson, T., Kwok, I., and Ng, R.T. 1998 <http://www.aaai.org/Library/KDD/1998/kdd98-038.php>) and density based outlier detection (Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. 1996 <https://dl.acm.org/citation.cfm?id=3001507>). This package provides labelling of observations as outliers and outlierliness of each outlier. For univariate, bivariate and trivariate data, visualization is also provided.
ggplot2, DDoutlier, depth, depthTools,ldbod, spatstat, plotly