Randomized and balanced allocation of units to treatment groups using the Finite Selection Model (FSM). The FSM was originally proposed and developed at the RAND corporation by Carl Morris to enhance the experimental design for the now famous Health Insurance Experiment. See Morris (1979) <doi:10.1016/0304-4076(79)90053-8> for details on the original version of the FSM.

Estimation of Rosenthal's fail safe number including confidence intervals. The relevant papers are the following. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2014). "Publication Bias in Meta-Analysis: Confidence Intervals for Rosenthal's Fail-Safe Number". International Scholarly Research Notices, Volume 2014. <doi:10.1155/2014/825383>. Konstantinos C. Fragkos, Michail Tsagris and Christos C. Frangos (2017). "Exploring the distribution for the estimator of Rosenthal's fail-safe number of unpublished studies in meta-analysis". Communications in Statistics-Theory and Methods, 46(11):5672--5684. <doi:10.1080/03610926.2015.1109664>.

Fuzzy set ordination is a multivariate analysis used in ecology to
    relate the composition of samples to possible explanatory variables.  While
    differing in theory and method, in practice, the use is similar to 'constrained
    ordination.'  The package contains plotting and summary functions as well as
    the analyses.

Estimating the number of factors in Exploratory Factor Analysis (EFA) with out-of-sample prediction errors using a cross-validation scheme. Haslbeck & van Bork (Preprint) <https://psyarxiv.com/qktsd>.

Support for fuzzy spatial objects, their operations, and fuzzy spatial inference models based on Spatial Plateau Algebra. 
    It employs fuzzy set theory and fuzzy logic as foundation to deal with spatial fuzziness. 
    It mainly implements underlying concepts defined in the following research papers: 
    (i) "Spatial Plateau Algebra: An Executable Type System for Fuzzy Spatial Data Types" <doi:10.1109/FUZZ-IEEE.2018.8491565>; 
    (ii) "A Systematic Approach to Creating Fuzzy Region Objects from Real Spatial Data Sets" <doi:10.1109/FUZZ-IEEE.2019.8858878>; 
    (iii) "Spatial Data Types for Heterogeneously Structured Fuzzy Spatial Collections and Compositions" <doi:10.1109/FUZZ48607.2020.9177620>;
    (iv) "Fuzzy Inference on Fuzzy Spatial Objects (FIFUS) for Spatial Decision Support Systems" <doi:10.1109/FUZZ-IEEE.2017.8015707>;
    (v) "Evaluating Region Inference Methods by Using Fuzzy Spatial Inference Models" <doi:10.1109/FUZZ-IEEE55066.2022.9882658>.