Chernoff Faces for 'ggplot2'

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

Multidimensional Item Response Theory

Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) ). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models.

An Alternative Conflict Resolution Strategy

R's default conflict management system gives the most recently loaded package precedence. This can make it hard to detect conflicts, particularly when they arise because a package update creates ambiguity that did not previously exist. 'conflicted' takes a different approach, making every conflict an error and forcing you to choose which function to use.

'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'.

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) .

Moving Subset Analysis FACE

The new methodology "moving subset analysis" provides functions to investigate the effect of environmental conditions on the CO2 fertilization effect within longterm free air carbon enrichment (FACE) experiments. In general, the functionality is applicable to derive the influence of a third variable (forcing experiment-support variable) on the relation between a dependent and an independent variable.

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.

Detection of Outliers in Time Series

Detection of outliers in time series following the Chen and Liu (1993) procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered.

Calculations and Visualisations Related to Geometric Morphometrics

A toolset for Geometric Morphometrics and mesh processing. This includes (among other stuff) mesh deformations based on reference points, permutation tests, detection of outliers, processing of sliding semi-landmarks and semi-automated surface landmark placement.

Outlier Detection Using Invariant Coordinate Selection

Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components. ICS is a general multivariate technique with many applications in multivariate analysis. ICSOutlier offers a selection of functions for automated detection of outliers in the data based on a fitted ICS object or by specifying the dataset and the scatters of interest. The current implementation targets data sets with only a small percentage of outliers.