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.

Perform HTTP Requests and Process the Responses

Tools for creating and modifying HTTP requests, then performing them and processing the results. 'httr2' is a modern re-imagining of 'httr' that uses a pipe-based interface and solves more of the problems that API wrapping packages face.

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.

R Interface to Stan

User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.

Colored Terminal Output

The crayon package is now superseded. Please use the 'cli' package for new projects. Colored terminal output on terminals that support 'ANSI' color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI' color support is automatically detected. Colors and highlighting can be combined and nested. New styles can also be created easily. This package was inspired by the 'chalk' 'JavaScript' project.

'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library

'Armadillo' is a templated C++ linear algebra library aiming towards a good balance between speed and ease of use. It provides high-level syntax and functionality deliberately similar to Matlab. It is useful for algorithm development directly in C++, or quick conversion of research code into production environments. It provides efficient classes for vectors, matrices and cubes where dense and sparse matrices are supported. Integer, floating point and complex numbers are supported. A sophisticated expression evaluator (based on template meta-programming) automatically combines several operations to increase speed and efficiency. Dynamic evaluation automatically chooses optimal code paths based on detected matrix structures. Matrix decompositions are provided through integration with LAPACK, or one of its high performance drop-in replacements (such as 'MKL' or 'OpenBLAS'). It can automatically use 'OpenMP' multi-threading (parallelisation) to speed up computationally expensive operations. The 'RcppArmadillo' package includes the header files from the 'Armadillo' library; users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. Starting from release 15.0.0, the minimum compilation standard is C++14 so 'Armadillo' version 14.6.3 is included as a fallback when an R package forces the C++11 standard. Package authors should set a '#define' to select the 'current' version, or select the 'legacy' version (also chosen as default) if they must. See 'GitHub issue #475' for details. Since release 7.800.0, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.

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.