Computing transitive (and non-transitive) index numbers (Coelli et al., 2005 <doi:10.1007/b136381>) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 <doi:10.1007/b136381>; Rao et al., 2002 <doi:10.1007/978-1-4615-0851-9_4>) and Minimum spanning tree (Hill, 2004 <doi:10.1257/0002828043052178>) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas.

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).

Tools used by organizational researchers for the analysis of multilevel data. Includes four broad sets of tools. First, functions for estimating within-group agreement and reliability indices. Second, functions for manipulating multilevel and longitudinal (panel) data. Third, simulations for estimating power and generating multilevel data. Fourth, miscellaneous functions for estimating reliability and performing simple calculations and data transformations.

Implement Bayesian Multilevel Modelling for compositional data 
             in a multilevel framework. Compute multilevel compositional data and 
             Isometric log ratio (ILR) at between and within-person levels, 
             fit Bayesian multilevel models for compositional predictors and outcomes, 
             and run post-hoc analyses such as isotemporal substitution models.

The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for
 multilevel mediation, the option of a moderating variable for either the a, b,
 or both paths, and covariates. Currently the 1-1-1 model is supported
 and several options of random effects; the initial code for bootstrapping was
 evaluated in simulations by Falk, Vogel, Hammami, and Miočević (2024) <doi:10.3758/s13428-023-02079-4>.
 Support for Bayesian estimation using 'brms' comprises ongoing work. Currently
 only continuous mediators and outcomes are supported. Factors for any
 predictors must be numerically represented.