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>.

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)) .

Fast Covariance Estimation for Multivariate Sparse Functional Data

Multivariate functional principal component analysis via fast covariance estimation for multivariate sparse functional data or longitudinal data proposed by Li, Xiao, and Luo (2020) .

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.

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.

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) .

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.

Isolate-Detect Methodology for Multiple Change-Point Detection

Provides efficient implementation of the Isolate-Detect methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the "deterministic + noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and Fryzlewicz (2018) < https://docs.wixstatic.com/ugd/24cdcc_6a0866c574654163b8255e272bc0001b.pdf>. Currently implemented scenarios are: piecewise-constant signal with Gaussian noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.

Change Point Detection with Missing Values

A four step change point detection method that can detect break points with the presence of missing values proposed by Liu and Safikhani (2023) < https://drive.google.com/file/d/1a8sV3RJ8VofLWikTDTQ7W4XJ76cEj4Fg/view?usp=drive_link>.

Detect Sensitive Points in the Tail

The goal of 'TailID' is to detect sensitive points in the tail of a dataset using techniques from Extreme Value Theory (EVT). It utilizes the Generalized Pareto Distribution (GPD) for assessing tail behavior and detecting inconsistent points with the Identical Distribution hypothesis of the tail. For more details see Manau (2025).