The main functions carry out Gibbs' sampler routines for nonparametric and semiparametric Bayesian models for random effects meta-analysis.

Fit Bayesian models with a focus on the spatial econometric models.

Efficient simulation of Brownian semistationary (BSS) processes using the hybrid simulation scheme, as described in 
    Bennedsen, Lunde, Pakkannen (2017) <arXiv:1507.03004v4>, as well as functions to fit BSS processes
    to data, and functions to estimate the stochastic volatility process of a BSS process.

Functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.

Computation of the minimum sample size using the Average
    Coverage Criterion or the Average Length Criterion for estimating
    binomial proportions using beta prior distributions. For more details
    see Costa (2025) <DOI:10.1007/978-3-031-72215-8_14>.

Efficient methods for Bayesian inference of state space models 
    via Markov chain Monte Carlo (MCMC) based on parallel 
    importance sampling type weighted estimators 
    (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), 
    particle MCMC, and its delayed acceptance version. 
    Gaussian, Poisson, binomial, negative binomial, and Gamma
    observation densities and basic stochastic volatility models 
    with linear-Gaussian state dynamics, as well as general non-linear Gaussian 
    models and discretised diffusion models are supported. 
    See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.

Provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. 'BSSoverSpace' is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<arXiv:2201.02023>.