This package purposes to deal with public survey data of
        Japanese government via their Application Programming Interface
        (http://statdb.nstac.go.jp/)

Compute Gower's distance (or similarity) coefficient between records. Compute 
    the top-n matches between records. Core algorithms are executed in parallel on systems
    supporting OpenMP.

Provides tools for applying the Bayesian Gower agreement methodology (presented in the package vignette) to nominal or ordinal data. The framework can accommodate any number of units, any number of coders, and missingness; and can handle both one-way and two-way random study designs. Influential units and/or coders can be identified easily using leave-one-out statistics.

Implements a variant of the Self-Organizing Map (SOM) algorithm
    designed for mixed-attribute datasets. Similarity between observations is
    computed using the Gower distance, and categorical prototypes are updated
    via heuristic strategies (weighted mode and multinomial sampling).
    Provides functions for model fitting, mapping, visualization
    (U-Matrix and component planes), and evaluation, making SOM applicable
    to heterogeneous real-world data. For methodological details see
    Sáez and Salas (2026) <doi:10.1007/s41060-025-00941-6>.

A collection of tools which extract a model documentation from 'GAMS' code and comments. 
             In order to use the package you need to install 'pandoc' and 'pandoc-citeproc' 
             first (<https://pandoc.org/>).

Functions to estimate the parameters of the generalized Poisson distribution with or without covariates using maximum likelihood. The references include Nikoloulopoulos A.K. & Karlis D. (2008). "On modeling count data: a comparison of some well-known discrete distributions". Journal of Statistical Computation and Simulation, 78(3): 437--457, <doi:10.1080/10629360601010760> and Consul P.C. & Famoye F. (1992). "Generalized Poisson regression model". Communications in Statistics - Theory and Methods, 21(1): 89--109, <doi:10.1080/03610929208830766>.