Open Source OCR Engine

Bindings to 'Tesseract': a powerful optical character recognition (OCR) engine that supports over 100 languages. The engine is highly configurable in order to tune the detection algorithms and obtain the best possible results.

Multivariate Outlier Detection and Imputation for Incomplete Survey Data

Algorithms for multivariate outlier detection when missing values occur. Algorithms are based on Mahalanobis distance or data depth. Imputation is based on the multivariate normal model or uses nearest neighbour donors. The algorithms take sample designs, in particular weighting, into account. The methods are described in Bill and Hulliger (2016) .

Fast Principal Component Analysis for Outlier Detection

Methods to detect genetic markers involved in biological adaptation. 'pcadapt' provides statistical tools for outlier detection based on Principal Component Analysis. Implements the method described in (Luu, 2016) and later revised in (Privé, 2020) .

Error Detection in Science

Test published summary statistics for consistency (Brown and Heathers, 2017, ; Allard, 2018, < https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/>; Heathers and Brown, 2019, < https://osf.io/5vb3u/>). The package also provides infrastructure for implementing new error detection techniques.

Outlier Detection via Pruning Mutual Reachability Minimum Spanning Trees

Implements an anomaly detection algorithm based on a dataset's mutual reachability minimum spanning tree: 'deadwood' prunes protruding tree segments and marks small debris as outliers; see Gagolewski (2026) < https://deadwood.gagolewski.com/>. More precisely, tree edges with weights greater than the detected elbow point are removed. All the resulting connected components whose sizes do not exceed a prespecified threshold are deemed anomalous. The use of a mutual reachability distance pulls peripheral observations farther away from one another. If the dataset is comprised of well-separated clusters of heterogeneous densities, an attempt to split the dataset and refine the outlierness markers will be made. The 'Python' version of 'deadwood' is available via 'PyPI'.

Bayesian Online Changepoint Detection

Implements the Bayesian online changepoint detection method by Adams and MacKay (2007) for univariate or multivariate data. Gaussian and Poisson probability models are implemented. Provides post-processing functions with alternative ways to extract changepoints.

Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

Implements the Exploratory Graph Analysis (EGA) framework for dimensionality and psychometric assessment. EGA estimates the number of dimensions in psychological data using network estimation methods and community detection algorithms. A bootstrap method is provided to assess the stability of dimensions and items. Fit is evaluated using the Entropy Fit family of indices. Unique Variable Analysis evaluates the extent to which items are locally dependent (or redundant). Network loadings provide similar information to factor loadings and can be used to compute network scores. A bootstrap and permutation approach are available to assess configural and metric invariance. Hierarchical structures can be detected using Hierarchical EGA. Time series and intensive longitudinal data can be analyzed using Dynamic EGA, supporting individual, group, and population level assessments.

Interactive Grammar of Graphics

An implementation of an interactive grammar of graphics, taking the best parts of 'ggplot2', combining them with the reactive framework of 'shiny' and drawing web graphics using 'vega'.

Google's Compact Language Detector 2

Bindings to Google's C++ library Compact Language Detector 2 (see < https://github.com/cld2owners/cld2#readme> for more information). Probabilistically detects over 80 languages in plain text or HTML. For mixed-language input it returns the top three detected languages and their approximate proportion of the total classified text bytes (e.g. 80% English and 20% French out of 1000 bytes). There is also a 'cld3' package on CRAN which uses a neural network model instead.

Statistical Power, Sample Size, and Detectable Effect Calculations

Flexible and comprehensive functions for statistical power, minimum required sample size, and minimum detectable effect calculations across a wide range of commonly used hypothesis tests in psychological, biomedical, and social sciences.