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Fast Change Point Detection via Sequential Gradient Descent
Implements fast change point detection algorithm based on the
paper "Sequential Gradient Descent and Quasi-Newton's Method for
Change-Point Analysis" by Xianyang Zhang, Trisha Dawn
< https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is
based on dynamic programming with pruning and sequential gradient
descent. See Li and Zhang (2026)
Flexible Tool for Bias Detection, Visualization, and Mitigation
Measure fairness metrics in one place for many models. Check how big is model's bias towards different races, sex, nationalities etc. Use measures such as Statistical Parity, Equal odds to detect the discrimination against unprivileged groups. Visualize the bias using heatmap, radar plot, biplot, bar chart (and more!). There are various pre-processing and post-processing bias mitigation algorithms implemented. Package also supports calculating fairness metrics for regression models. Find more details in (Wiśniewski, Biecek (2021))
Outliers Detection
Provides functions for detecting outliers in datasets using statistical methods. The package supports identification of anomalous observations in numerical data and is intended for use in data cleaning, exploratory data analysis, and preprocessing workflows.
Detect and Treat Outliers in Data Mining
Implements a suite of tools for outlier detection and treatment in data mining. It includes univariate methods (Z-score, Interquartile Range), multivariate detection using Mahalanobis distance, and density-based detection (Local Outlier Factor) via the 'dbscan' package. It also provides functions for visualization using 'ggplot2' and data cleaning via Winsorization.
Multivariate Outlier Detection Methods
Provides methods for detecting multivariate outliers in numeric datasets. The package implements classical Mahalanobis distance, robust Minimum Covariance Determinant (MCD), and Principal Component Analysis (PCA)-based approaches for outlier detection. The methodology is informed by Aggarwal (2017)
Analyzing Data with Cellwise Outliers
Tools for detecting cellwise outliers and robust methods to analyze
data which may contain them. Contains the implementation of the algorithms described in
Rousseeuw and Van den Bossche (2018)
QTL Hotspot Detection
This function produces both the numerical and graphical summaries of the QTL hotspot detection in the genomes that are available on the worldwide web including the flanking markers of QTLs.
Easily Download 'EconDataverse' Datasets
The 'EconDataverse' is a universe of open-source packages to work seamlessly with economic data. This package is designed to make it easy to download selected datasets that are preprocessed by 'EconDataverse' packages and publicly hosted on 'Hugging Face'. Learn more about the 'EconDataverse' at < https://www.econdataverse.org>.
Detecting Outliers in Network Meta-Analysis
A set of functions providing several outlier (i.e., studies with extreme findings) and influential detection measures and methodologies in network meta-analysis : - simple outlier and influential detection measures - outlier and influential detection measures by considering study deletion (shift the mean) - plots for outlier and influential detection measures - Q-Q plot for network meta-analysis - Forward Search algorithm in network meta-analysis. - forward plots to monitor statistics in each step of the forward search algorithm - forward plots for summary estimates and their confidence intervals in each step of forward search algorithm.
Geometrically Inspired Multivariate Changepoint Detection
Implements the high-dimensional changepoint detection method GeomCP and the related mappings used for changepoint detection. These methods view the changepoint problem from a geometrical viewpoint and aim to extract relevant geometrical features in order to detect changepoints. The geomcp() function should be your first point of call. References: Grundy et al. (2020)