# Stan Models for the Paired Comparison Factor Model

## Documentation for package ‘pcFactorStan’ version 1.5.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

pcFactorStan-package | Stan Models for the Pairwise Comparison Factor Model
calibrateItems | Determine the optimal scale constant for a set of items
cmp_probs | Item response function for pairwise comparisons
filterGraph | Filter graph to remove vertices that are not well connected
findModel | Given a model name, return stanmodel object
generateCovItems | Generate paired comparison data with random correlations between items
generateFactorItems | Generate paired comparison data for a factor model
generateItem | Generate paired comparison data for one or more items given absolute latent scores
generateSingleFactorItems | Generate paired comparison data with a common factor that accounts for some proportion of the variance
itemModelExplorer | A Shiny app to experiment with the item response model
normalizeData | Normalize data according to a canonical order
outlierTable | List observations with Pareto values larger than a given threshold
parDistributionCustom | Produce data suitable for plotting parameter distributions
parDistributionFor | Produce data suitable for plotting parameter distributions
parInterval | Produce data suitable for plotting parameter estimates
pcFactorStan | Stan Models for the Pairwise Comparison Factor Model
pcStan | Fit a paired comparison Stan model
phyActFlowPropensity | Physical activity flow propensity
prepCleanData | Transforms data into a form tailored for efficient evaluation by Stan
prepData | Transforms data into a form tailored for efficient evaluation by Stan
prepFactorModel | Specify a factor model
prepSingleFactorModel | Specify a single factor model
responseCurve | Produce data suitable for plotting item response curves
roundRobinGraph | Create an edge list with round-robin connectivity
toLoo | Compute approximate leave-one-out (LOO) cross-validation for Bayesian models using Pareto smoothed importance sampling (PSIS)
twoLevelGraph | Create an edge list with a random two level connectivity
unfactor | Turn a factor back into a vector of integers
withoutIndex | Remove the array indexing from a parameter name