Zoning Methods for Spatial Data (geozoning)
A zoning method and a numerical criterion for zoning quality are available in this package. The zoning method is based on a numerical criterion that evaluates the zoning quality. This criterion quantifies simultaneously how zones are heterogeneous on the whole map and how neighbouring zones are similar. This approach allows comparison between maps either with different zones or different labels, which is of importance for zone delineation algorithms aiming at maximizing inter-zone variability. An optimisation procedure provides the user with the best zonings thanks to contour delineation for a given map.

Differential Item Functioning in Generalized Partial Credit Models (GPCMlasso)
Provides a function to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.

Composite Likelihood Inference for Spatial Ordinal Data with Replications (clordr)
Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error. Parametric bootstrapping is used to estimate the asymptotic standard error and covariance matrix.