Easy-to-Use Tools for Common Forms of Random Assignment and Sampling

Generates random assignments for common experimental designs and random samples for common sampling designs.

Automatic Construction of Forced-Choice Tests

Forced-choice (FC) response has gained increasing popularity and interest for its resistance to faking when well-designed (Cao & Drasgow, 2019 ). To established well-designed FC scales, typically each item within a block should measure different trait and have similar level of social desirability (Zhang et al., 2020 ). Recent study also suggests the importance of high inter-item agreement of social desirability between items within a block (Pavlov et al., 2021 ). In addition to this, FC developers may also need to maximize factor loading differences (Brown & Maydeu-Olivares, 2011 ) or minimize item location differences (Cao & Drasgow, 2019 ) depending on scoring models. Decision of which items should be assigned to the same block, termed item pairing, is thus critical to the quality of an FC test. This pairing process is essentially an optimization process which is currently carried out manually. However, given that we often need to simultaneously meet multiple objectives, manual pairing becomes impractical or even not feasible once the number of latent traits and/or number of items per trait are relatively large. To address these problems, autoFC is developed as a practical tool for facilitating the automatic construction of FC tests (Li et al., 2022 ), essentially exempting users from the burden of manual item pairing and reducing the computational costs and biases induced by simple ranking methods. Given characteristics of each item (and item responses), FC measures can be constructed either automatically based on user-defined pairing criteria and weights, or based on exact specifications of each block (i.e., blueprint; see Li et al., 2024 ). Users can also generate simulated responses based on the Thurstonian Item Response Theory model (Brown & Maydeu-Olivares, 2011 ) and predict trait scores of simulated/actual respondents based on an estimated model.

Manage Environment Specific Configuration Values

Manage configuration values across multiple environments (e.g. development, test, production). Read values using a function that determines the current environment and returns the appropriate value.

Multivariate Exploratory Data Analysis and Data Mining

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Extract and Visualize the Results of Multivariate Data Analyses

Provides some easy-to-use functions to extract and visualize the output of multivariate data analyses, including 'PCA' (Principal Component Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple Factor Analysis) functions from different R packages. It contains also functions for simplifying some clustering analysis steps and provides 'ggplot2' - based elegant data visualization.

Interface to 'Lp_solve' v. 5.5 to Solve Linear/Integer Programs

Lp_solve is freely available (under LGPL 2) software for solving linear, integer and mixed integer programs. In this implementation we supply a "wrapper" function in C and some R functions that solve general linear/integer problems, assignment problems, and transportation problems. This version calls lp_solve version 5.5.

Multiple Precision Arithmetic

Multiple Precision Arithmetic (big integers and rationals, prime number tests, matrix computation), "arithmetic without limitations" using the C library GMP (GNU Multiple Precision Arithmetic).

R Interface to RNG with Multiple Streams

Provides an interface to the C implementation of the random number generator with multiple independent streams developed by L'Ecuyer et al (2002). The main purpose of this package is to enable the use of this random number generator in parallel R applications.

Generic Reproducible Parallel Backend for 'foreach' Loops

Provides functions to perform reproducible parallel foreach loops, using independent random streams as generated by L'Ecuyer's combined multiple-recursive generator [L'Ecuyer (1999), ]. It enables to easily convert standard '%dopar%' loops into fully reproducible loops, independently of the number of workers, the task scheduling strategy, or the chosen parallel environment and associated foreach backend.

Some Additional Multiple Imputation Functions, Especially for 'mice'

Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, ) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, ), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, ; van Buuren, 2018, Ch.7, ), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, ), substantive model compatible imputation (Bartlett et al., 2015, ), and features for the generation of synthetic datasets (Reiter, 2005, ; Nowok, Raab, & Dibben, 2016, ).