Rigorous - DICOM Input / Output

Data input/output functions for data that conform to the Digital Imaging and Communications in Medicine (DICOM) standard, part of the Rigorous Analytics bundle.

'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. 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'.

Hierarchical Model of Species Communities

Hierarchical Modelling of Species Communities (HMSC) is a model-based approach for analyzing community ecological data. This package implements it in the Bayesian framework with Gibbs Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020) ).

Ordination and Multivariate Analysis for Ecology

A variety of ordination and community analyses useful in analysis of data sets in community ecology. Includes many of the common ordination methods, with graphical routines to facilitate their interpretation, as well as several novel analyses.

Project MOSAIC Statistics and Mathematics Teaching Utilities

Data sets and utilities from Project MOSAIC (< http://www.mosaic-web.org>) used to teach mathematics, statistics, computation and modeling. Funded by the NSF, Project MOSAIC is a community of educators working to tie together aspects of quantitative work that students in science, technology, engineering and mathematics will need in their professional lives, but which are usually taught in isolation, if at all.

Collection of Methods to Detect Dichotomous, Polytomous, and Continuous Differential Item Functioning (DIF)

Methods to detect differential item functioning (DIF) in dichotomous, polytomous, and continuous items, using both classical and modern approaches. These include Mantel-Haenszel procedures, logistic regression (including ordinal models), and regularization-based methods such as LASSO. Uniform and non-uniform DIF effects can be detected, and some methods support multiple focal groups. The package also provides tools for anchor purification, rest score matching, effect size estimation, and DIF simulation. See Magis, Beland, Tuerlinckx, and De Boeck (2010, Behavior Research Methods, 42, 847–862, ) for a general overview.

Scalable Robust Estimators with High Breakdown Point

Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point: principal component analysis (Filzmoser and Todorov (2013), ), linear and quadratic discriminant analysis (Todorov and Pires (2007)), multivariate tests (Todorov and Filzmoser (2010) ), outlier detection (Todorov et al. (2010) ). See also Todorov and Filzmoser (2009) , Todorov and Filzmoser (2010) and Boudt et al. (2019) .

Analyzing Wildlife Data with Detection Error

Models for analyzing site occupancy and count data models with detection error, including single-visit based models (Lele et al. 2012 , Moreno et al. 2010 , Solymos et al. 2012 , Denes et al. 2016 ), conditional distance sampling and time-removal models (QPAD) (Solymos et al. 2013 , Solymos et al. 2018 ), and single bin QPAD (SQPAD) models (Lele & Solymos 2025 ). Package development was supported by the Alberta Biodiversity Monitoring Institute and the Boreal Avian Modelling Project.

Shiny Apps to Support Capacity Building on Harvest Control Rules

Three Shiny apps are provided that introduce Harvest Control Rules (HCR) for fisheries management. 'Introduction to HCRs' provides a simple overview to how HCRs work. Users are able to select their own HCR and step through its performance, year by year. Biological variability and estimation uncertainty are introduced. 'Measuring performance' builds on the previous app and introduces the idea of using performance indicators to measure HCR performance. 'Comparing performance' allows multiple HCRs to be created and tested, and their performance compared so that the preferred HCR can be selected.

Distance Sampling Detection Function and Abundance Estimation

A simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) for more information on methods and < https://distancesampling.org/resources/vignettes.html> for example analyses.