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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.
Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the 'UDPipe' 'NLP' Toolkit
This natural language processing toolkit provides language-agnostic
'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency
parsing' of raw text. Next to text parsing, the package also allows you to train
annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided
at < https://universaldependencies.org/format.html>. The techniques are explained
in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0
with UDPipe', available at
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>.
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.
Online Multivariate Changepoint Detection
Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay.
Angle-Based Outlier Detection
Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.
Detecting Correlated Genomic Regions
Performs correlation matrix segmentation and applies a test procedure to detect highly correlated regions in gene expression.
Detection of domains in HiC data
This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue.