A collection of statistical tests for the detection of differential
    item functioning (DIF) in multistage tests. Methods entail logistic regression,
    an adaptation of the simultaneous item bias test (SIBTEST), and various score-based tests.
    The presented tests provide itemwise test for DIF along categorical, ordinal or metric covariates. Methods for uniform and non-uniform 
    DIF effects are available depending on which method is used.

A new approach to detect change points based on smoothing and multiple testing, which is for long data sequence modeled as piecewise constant functions plus stationary Gaussian noise, see Dan Cheng and Armin Schwartzman (2015) <arXiv:1504.06384>.

Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2022) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels.pdf> that can be used to identify the number of regimes in Markov switching models.

Software to aid in modeling and analyzing mass-spectrometry-based
    proteome melting data. Quantitative data is imported and normalized and
    thermal behavior is modeled at the protein level. Methods exist for
    normalization, modeling, visualization, and export of results. For a
    general introduction to MS-based thermal profiling, see Savitski et al.
    (2014) <doi:10.1126/science.1255784>.

Implements the MST-kNN clustering algorithm which was proposed by Inostroza-Ponta, M. (2008) <https://trove.nla.gov.au/work/28729389?selectedversion=NBD44634158>.

Generation of response patterns under dichotomous and polytomous computerized multistage testing (MST) framework. It holds various item response theory (IRT) and score-based methods to select the next module and estimate ability levels (Magis, Yan and von Davier (2017, ISBN:978-3-319-69218-0)).