These functions take a character vector as input, identify and 
  cluster similar values, and then merge clusters together so their values 
  become identical. The functions are an implementation of the key collision 
  and ngram fingerprint algorithms from the open source tool Open Refine 
  <https://openrefine.org/>. More info on key collision and ngram fingerprint 
  can be found here <https://openrefine.org/docs/technical-reference/clustering-in-depth>.

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the 'RMarkdown' vignette: "'refitME' R-package tutorial".

A tool for researchers and psychologists to automatically code open-ended responses to the Cognitive Reflection Test (CRT), a widely used class of tests in cognitive science and psychology for assessing an individual's propensity to override an incorrect gut response and engage in further reflection to find a correct answer. This package facilitates the standardization of Cognitive Reflection Test responses analysis across large datasets in cognitive psychology, decision-making, and related fields. By automating the coding process, it not only reduces manual effort but also aims to reduce the variability introduced by subjective interpretation of open-ended responses, contributing to a more consistent and reliable analysis. 'reflectR' supports automatic coding and machine scoring for the original English-language version of CRT (Frederick, 2005) <doi:10.1257/089533005775196732>, as well as for CRT4 and CRT7, 4- and 7-item versions, respectively (Toplak et al., 2014) <doi:10.1080/13546783.2013.844729>, for the CRT-long version built via Item Response Theory by Primi and colleagues (2016) <doi:10.1002/bdm.1883>, and for CRT-2 by Thomson & Oppenheimer (2016) <doi:10.1017/s1930297500007622>.
    Note: While 'reflectR' draws inspiration from the principles and scientific literature underlying the different versions of the Cognitive Reflection Test, it has been independently developed and does not hold any affiliation with any of the original authors.
    The development of this package benefited significantly from the kind insight and suggestion provided by Dr. Keela Thomson, whose contribution is gratefully acknowledged.
    Additional gratitude is extended to Dr. Paolo Giovanni Cicirelli, Prof. Marinella Paciello, Dr. Carmela Sportelli, and Prof. Francesca D'Errico, whose contributions highlighted the significant importance and practical relevance of this construct in personality, social, and cognitive research.