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.

Distance & Density-Based Outlier Detection

Outlier detection in multidimensional domains. Implementation of notable distance and density-based outlier algorithms. Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) , Angiulli, F., & Pizzuti, C. (2002) , Hautamaki, V., & Ismo, K. (2004) and Zhang, K., Hutter, M. & Jin, H. (2009) . For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) , Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) , Schubert, E., Zimek, A. & Kriegel, H-P. (2014) , Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) , Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) , Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) , Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009) , Zhu, Q., Feng, Ji. & Huang, J. (2016) , Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015) , Tang, B. & Haibo, He. (2017) and Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011) .

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) .

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.

Mark-Recapture Distance Sampling

Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.

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.

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>.