bridge.linearmap()
Bridging Two Ideal Point Estimates with Linear Transformation Method
compute.nv()
Compute Normal Vector
genip.montecarlo()
Generate Simulated Responses of Respondents with Ideal Points
ipbridging()
Bridging Ideal Point Estimates from Two Data Sets
ipest()
Estimating Ideal Points
mahalanobis_miss()
Mahalanobis distance (MD) for data with missing values
nv.krls()
Estimating Normal Vector through Kernel Regularized Least Squares
nv.svm()
Estimating Normal Vector through Support Vector Machine
oocflex()
Ordered Optimal Classification (Flexible Version)
setanchors()
Setting Anchors to Combine Two Datasets
summary(<oocflexObject>)
Ordered Optimal Classification Summary