| Testing conditional independence in psychometric networks: An analysis of three bayesian methods N Sekulovski, S Keetelaar, K Huth, EJ Wagenmakers, R van Bork, ... PsyArXiv, 2023 | 8 | 2023 |
| bgms: Bayesian variable selection for networks of binary and/or ordinal variables [Computer software manual] M Marsman, D van den Bergh, N Sekulovski R package version 0.1. 0, 2023 | 6 | 2023 |
| Sensitivity analysis of prior distributions in bayesian graphical modeling: Guiding informed prior choices for conditional independence testing N Sekulovski, S Keetelaar, J Haslbeck, M Marsman PsyArXiv, 2023 | 4 | 2023 |
| A default Bayes factor for testing null hypotheses about the fixed effects of linear two-level models. N Sekulovski, H Hoijtink Psychological Methods, 2023 | 2 | 2023 |
| Prevalence, Patterns, and Predictors of Paranormal Beliefs in the Netherlands: A Several-Analysts Approach S Hoogeveen, D Borsboom, Š Kucharský, M Marsman, D Molenaar, ... PsyArXiv. https://doi. org/10.31234/osf. io/ajush, 2024 | 1 | 2024 |
| Comparing maximum likelihood and pseudo-maximum likelihood estimators for the Ising model S Keetelaar, N Sekulovski, D Borsboom, M Marsman PsyArXiv, 2023 | 1 | 2023 |
| Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package K Huth, S Keetelaar, N Sekulovski, D van den Bergh, M Marsman PsyArXiv, 2023 | 1 | 2023 |
| A Good Check on the Bayes Factor N Sekulovski, M Marsman, EJ Wagenmakers OSF, 2024 | | 2024 |
| Comparing Maximum Likelihood and Maximum Pseudolikelihood Estimators for the Ising Model S Keetelaar, N Sekulovski, D Borsboom, M Marsman | | |
