Convolutional Neural Network for Face Detection

An open source library for face detection in images. Provides a pretrained convolutional neural network based on < https://github.com/ShiqiYu/libfacedetection> which can be used to detect faces which have size greater than 10x10 pixels.

Bindings to 'OpenCV' Computer Vision Library

Exposes some of the available 'OpenCV' < https://opencv.org/> algorithms, such as a QR code scanner, and edge, body or face detection. These can either be applied to analyze static images, or to filter live video footage from a camera device.

An Interface for Face Recognition

Provides an interface to the 'Kairos' Face Recognition API < https://kairos.com/face-recognition-api>. The API detects faces in images and returns estimates for demographics like gender, ethnicity and age.

Epistasis Test in Meta-Analysis

Traditional meta-regression based method has been developed for using meta-analysis data, but it faced the challenge of inconsistent estimates. This package purpose a new statistical method to detect epistasis using incomplete information summary, and have proven it not only successfully let consistency of evidence, but also increase the power compared with traditional method (Detailed tutorial is shown in website).

Tool for Annotating Images in Preparation for Analysis

A tool to help create shiny apps for selecting and annotating elements of images. Users must supply images, questions, and answer choices. The user interface is a dynamic shiny app, that displays the images and questions and answer choices. The data generated can be saved to a file that can be used for subsequent analysis. The original purpose was to annotate still images from tennis video for face recognition and emotion detection purposes.

Neyman-Pearson (NP) Classification Algorithms and NP Receiver Operating Characteristic (NP-ROC) Curves

In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (i.e., the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha. As a result, the NP paradigm has not been properly implemented for many classification scenarios in practice. In this work, we develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, including popular methods such as logistic regression, support vector machines and random forests. Powered by this umbrella algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands, motivated by the popular receiver operating characteristic (ROC) curves. NP-ROC bands will help choose in a data adaptive way and compare different NP classifiers.

Analyzing Wildlife Data with Detection Error

Models for analyzing site occupancy and count data models with detection error, including single-visit based models, conditional distance sampling and time-removal models. Package development was supported by the Alberta Biodiversity Monitoring Institute and the Boreal Avian Modelling Project.

Methods for Detection of Clusters in Hierarchical Clustering Dendrograms

Contains methods for detection of clusters in hierarchical clustering dendrograms.

Kernel-Based Machine Learning Lab

Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.

Chernoff Faces for 'ggplot2'

Provides a Chernoff face geom for 'ggplot2'. Maps multivariate data to human-like faces. Inspired by Chernoff (1973) .