Depends: R (>= 3.0.0), methods, quantreg, survival, Formula

Title: Outlier Detection using quantile regression for Censored Data
Authors@R: c(person(given = "Soo-Heang", family = "Eo", role = c("aut","cre"),
person(given = "Huixia Judy", family = "Wang", role = "ctb"),
person(given = "Lan", family = "Wang", role = "ctb"),
person(given = "HyungJun", family = "Cho", role = "aut",
Description: This package provides three algorithms to detect outlying observations for censored survival data.
Depends: R (>= 3.0.0), methods, survival, quantreg, Formula

Title: Outlier detection for replicated high-throughput data
Description: Detecting outlying observations for replicated high-throughput high-dimensional data based on a MA plot with quantile regression approach.
Depends: R (>= 3.0.0), quantreg, MatrixModels, outliers, pcaPP

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

Description: 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.
Imports: ggplot2, DDoutlier, depth, depthTools,ldbod

Description: 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.
Imports: ggplot2, DDoutlier, depth, depthTools,ldbod, spatstat, plotly