Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models. Details about the methods used can be found in these articles. Zhang and Wang (2003) <doi:10.1007/s11336-012-9301-5>. Zhang (2014) <doi:10.3758/s13428-013-0424-0>.

Provides an accessible and robust implementation of core BME 
    methodologies for spatial prediction. It enables the systematic integration 
    of heterogeneous data sources including both hard data (precise 
    measurements) and soft interval data (bounded or uncertain observations) 
    while incorporating prior knowledge and supporting variogram-based spatial 
    modeling. The BME methodology is described in Christakos (1990) 
    <doi:10.1007/BF00890661> and Serre and Christakos (1999) 
    <doi:10.1007/s004770050029>.

Calculates the necessary quantities to perform Bayesian multigroup equivalence testing. 
             Currently the package includes the Bayesian models and equivalence criteria outlined in Pourmohamad and Lee (2023) 
             <doi:10.1002/sta4.645>, but more models and equivalence testing features may be added over time.

Fit Bayesian multivariate GARCH models using 'Stan' for full Bayesian inference. Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) <doi:10.1198/073500102288618487> and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) <doi:10.1017/S0266466600009063> while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) <doi:10.31234/osf.io/j57pk>. The fitted models contain 'rstan' objects and can be examined with 'rstan' functions.