IRT Separate Calibration Linking Methods (plink)
Item response theory based methods are used to compute linking constants and conduct chain linking of unidimensional or multidimensional tests for multiple groups under a common item design. The unidimensional methods include the Mean/Mean, Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous (1PL, 2PL and 3PL) and/or polytomous (graded response, partial credit/generalized partial credit, nominal, and multiple-choice model) items. The multidimensional methods include the least squares method and extensions of the Haebara and Stocking-Lord method using single or multiple dilation parameters for multidimensional extensions of all the unidimensional dichotomous and polytomous item response models. The package also includes functions for importing item and/or ability parameters from common IRT software, conducting IRT true score and observed score equating, and plotting item response curves/surfaces, vector plots, information plots, and comparison plots for examining parameter drift.

One-Sided Cross-Validation (OSCV)
Functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, <doi:10.1007/s00180-017-0713-7> and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, <arXiv:1703.05157>.

A Self-Describing Dataset Format and Interface (fold)
Defines a compact data format that includes metadata. The function fold() creates the format by converting from data.frame, and unfold() converts back. The predictability of the folded format supports reusability of data processing tools, while the presence of embedded metadata improves portability, interpretability, and efficiency.

Fuzzy String Matching (fuzzywuzzyR)
Fuzzy string matching implementation of the ‘fuzzywuzzy’ <https://…/fuzzywuzzy> ‘python’ package. It uses the Levenshtein Distance <https://…/Levenshtein_distance> to calculate the differences between sequences.

Significance Bars for ‘ggplot2’ (ggsignif)
Enrich your ggplots with group-wise comparisons. This package provides an easy way to indicate if two groups are significantly different. Commonly this is shown by a bar on top connecting the groups of interest which itself is annotated with the level of significance (NS, *, **, ***). The package provides a single layer (geom_signif) that takes the groups for comparison and the test (t.test(), wilcox.text() etc.) as arguments and adds the annotation to the plot.