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- The misty package is a tool for performing cluster analysis on mixed data types. It accommodates categorical, ordinal, and continuous variables, making it a robust choice for a wide range of clustering problems.
rbasics.org/packages/misty-package-in-r/
misty: Miscellaneous Functions 'T. Yanagida'. Miscellaneous functions for (1) data management (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3 ...
The misty package is a tool for performing cluster analysis on mixed data types. It accommodates categorical, ordinal, and continuous variables, making it a robust choice for a wide range of clustering problems.
Write Results of a misty Object into an Excel file. Miscellaneous functions for (1) data management (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures ...
Oct 24, 2024 · Miscellaneous functions for (1) data management (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for ...
center: Centering Predictor Variables in Single-Level and Multilevel... check.collin: Collinearity Diagnostics check.outlier: Statistical Measures for Leverage, Distance, and Influence
This function is a wrapper function for evaluating configural, metric, scalar, and strict between-group or longitudinal (partial) measurement invariance using confirmatory factor analysis with continuous indicators by calling the cfa function in the R package lavaan.
This function centers predictor variables in single-level data, two-level data, and three-level data at the grand mean (CGM, i.e., grand mean centering) or within cluster (CWC, i.e., group mean centering).
