Hyperlink Automatic Detection

Automatic detection of hyperlinks for packages and calls in the text of 'rmarkdown' or 'quarto' documents.

PharmacoVigilance Signal Detection

A collection of several pharmacovigilance signal detection methods extended to the multiple comparison setting.

Backward Procedure for Change-Point Detection

Implements a backward procedure for single and multiple change point detection proposed by Shin et al. . The backward approach is particularly useful to detect short and sparse signals which is common in copy number variation (CNV) detection.

R Interface to X-13-ARIMA-SEATS

Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the 'seasonalview' package. Uses the X-13-binaries from the 'x13binary' package.

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.