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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.
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.
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.
Transformer Emotion Analysis for Text, Image, Video, and Speech
Zero-shot emotion classification for text, images, video and
speech, and tools to find faces and code them by hand. Text is scored
with 'Hugging Face' transformer pipelines (default
'cross-encoder/nli-distilroberta-base'). Images and video are scored
with 'OpenAI CLIP'; faces can be limited to those found by 'OpenCV
YuNet'. Speech segments are scored with 'LAION CLAP'. Frame scores can
be calibrated and combined into posteriors, and detected faces or other
items can be turned into a local coding task served from R. Any other
zero-shot model can be selected by its name on
< https://huggingface.co/models>. For details see Tomašević, Golino, and
Christensen (2026)
Statistical Inference on Lineup Fairness
Computes statistical measures for laboratory and police lineups in forensic psychology, including lineup bias, functional and effective size, diagnosticity, confidence-accuracy curves, signal detection models, and simulation tools. Optional face-similarity functions use numeric face representations computed by Python's 'deepface' library; optional lineup-memory model fitting uses a separately installed, pinned 'pyWitness' engine.
Fast 3D Alpha Hull with Label Propagation
Fast 3D alpha shape (alpha hull) computation with label propagation
from input points to hull vertices and faces. Uses 'CGAL' for robust geometric
computations. Optimized for Light Detection and Ranging ('LiDAR') processing
and tree segmentation workflows. The implementation follows the alpha shape
algorithm described in Edelsbrunner et al. (1983)
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.
Discrete Morse Theory for 3D Meshes Derived from Point Clouds
Ultra-fast computation of discrete Morse (Marston Morse) gradient vector fields
and critical simplices (0-simplices: vertices, 1-simplices: edges,
2-simplices: faces) on 3D triangular meshes from point clouds.
Provides C++ backend with parallel processing for large-scale
topological analysis, including connected component analysis and
visualization tools. Perfect for Light Detection and Ranging ('LiDAR') data,
computational topology, and geometric analysis applications.
The implementation follows Forman (1998)