A simple interface for multivariate correlation analysis that unifies various classical
  statistical procedures including t-tests, tests in univariate and multivariate linear models,
  parametric and nonparametric tests for correlation, Kruskal-Wallis tests, common approximate
  versions of Wilcoxon rank-sum and signed rank tests, chi-squared tests of independence, score
  tests of particular hypotheses in generalized linear models, canonical correlation analysis and
  linear discriminant analysis.

Implements the uniform scaled beta distribution and
  the continuous convolution kernel density estimator.

Consensus analysis, model-based clustering, and cultural consensus theory applications to response data (e.g. questionnaires). The models are applied using hierarchical Bayesian inference. The current package version supports binary, ordinal, and continuous data formats.

Automated assessment and selection of weighting factors for accurate quantification using linear calibration curve. 
 In addition, a 'shiny' App is provided, allowing users to analyze their data using an interactive graphical user interface, without any programming requirements.

Duplicated music data (pre-processed and formatted) for entity resolution. The total size of the data set is 9763. There are respective gold standard records that are labeled and can be considered as a unique identifier.

Supplies higher-order coordinatized data specification and fluid transform operators that include pivot and anti-pivot as special cases. 
    The methodology is describe in 'Zumel', 2018, "Fluid data reshaping with 'cdata'", <https://winvector.github.io/FluidData/FluidDataReshapingWithCdata.html> , <DOI:10.5281/zenodo.1173299> .
    This package introduces the idea of explicit control table specification of data transforms.
    Works on in-memory data or on remote data using 'rquery' and 'SQL' database interfaces.

Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan, 2024; <doi:10.2139/ssrn.3721250>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).