Analysis of Check-All-that-Apply (CATA) Data
Adjusted Rand index
Inspect/summarize many b-cluster analysis runs
MAD distances between objects
Convert 3d array of CATA data to 4d array of CATA differences
b-cluster analysis by hierarchical agglomerative strategy
b-cluster analysis by non-hierarchical iterative ascent clustering str...
Wrapper function for b-cluster analysis
Cochran's Q test
Apply top-k box coding to scale data
Evaluate Quality of Cluster Analysis Solution
Calculate the b-measure
Calculate within-cluster homogeneity
Permutation tests for CATA data
McNemar's test
Penalty-Lift Analysis
Calculate Coefficient
Salton's cosine measure
Plot variation in retained sensory differentiation
Converts 3d array of CATA data to a tall 2d matrix format
Apply top-c choices coding to a vector of scale data from a respondent
Converts 3d array of CATA data to a wide 2d matrix format
Package contains functions for analyzing check-all-that-apply (CATA) data from consumer and sensory tests. Cochran's Q test, McNemar's test, and Penalty-Lift analysis are provided; for details, see Meyners, Castura & Carr (2013) <doi:10.1016/j.foodqual.2013.06.010>. Cluster analysis can be performed using b-cluster analysis, then evaluated using various measures; for details, see Castura, Meyners, Varela & Næs (2022) <doi:10.1016/j.foodqual.2022.104564>. Consumers can also be clustered on their product-related hedonic responses; see Castura, Meyners, Pohjanheimo, Varela & Næs (2023) <doi:10.1111/joss.12860>. Permutation tests based on the L1-norm methods are provided; for details, see Chaya, Castura & Greenacre (2025) <doi:10.1016/j.foodqual.2025.105639>.