Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.

Provides utilities for 'Kokudo Suuchi', the GIS data service of the Japanese government.
    See <https://nlftp.mlit.go.jp/index.html> for more information.

Assists researchers in choosing Key Opinion Leaders (KOLs) in a network to help disseminate or encourage adoption of an innovation by other network members. Potential KOL teams are evaluated using the ABCDE framework (Neal et al., 2025 <doi:10.31219/osf.io/3vxy9_v1>). This framework which considers: (1) the team members' Availability, (2) the Breadth of the team's network coverage, (3) the Cost of recruiting a team of a given size, and (4) the Diversity of the team's members, (5) which are pooled into a single Evaluation score.

Functions for analysing eye tracking data, including event detection, visualizations and area of interest (AOI) based analyses. 
  The package includes implementations of the IV-T, I-DT, adaptive velocity threshold, and Identification by two means clustering (I2MC) algorithms.
  See separate documentation for each function. The principles underlying I-VT and I-DT algorithms are described in Salvucci & Goldberg (2000) <doi:10.1145/355017.355028>. 
  Two-means clustering is described in Hessels et al. (2017), <doi: 10.3758/s13428-016-0822-1>. 
  The adaptive velocity threshold algorithm is described in Nyström & Holmqvist (2010),<doi:10.3758/BRM.42.1.188>. 
  A documentation of the 'kollaR' can be found in Kleberg et al (2026) <doi:10.3758/s13428-025-02903-z>. Cite this paper when using 'kollaR'
  See a demonstration in the URL.

Estimate and forecast Bayesian simultaneous equation models for macroeconomic time series. Provides tools to specify systems of behavioral equations and accounting identities, transform and manage time series, simulate from the posterior using a Metropolis-within-Gibbs sampler, and generate unconditional and conditional forecasts with user-defined priors and restrictions. Methods are described in Rathke A. and Sarferaz S. (forthcoming) "Bayesian Estimation of Simultaneous Equations Model".