| pgmpy: Probabilistic Graphical Models using Python AP Ankur Ankan Scipy US 2015, 6-11, 2015 | 196* | 2015 |
| Testing graphical causal models using the R package “dagitty” A Ankan, IMN Wortel, J Textor Current Protocols 1 (2), e45, 2021 | 46 | 2021 |
| Mastering probabilistic graphical models using python A Ankan, A Panda Packt Publishing Ltd, 2015 | 17 | 2015 |
| In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome JHA Creemers, A Ankan, KCB Roes, G Schröder, N Mehra, CG Figdor, ... Nature Communications 14 (1), 2348, 2023 | 5 | 2023 |
| A simple unified approach to testing high-dimensional conditional independences for categorical and ordinal data A Ankan, J Textor Proceedings of the AAAI Conference on Artificial Intelligence 37 (10), 12180 …, 2023 | 1 | 2023 |
| Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models A Ankan, I Wortel, K Bollen, J Textor International Conference on Artificial Intelligence and Statistics, 7252-7264, 2023 | 1 | 2023 |
| pgmpy: A Python Toolkit for Bayesian Networks A Ankan, J Textor arXiv preprint arXiv:2304.08639, 2023 | | 2023 |
| Group Correction Statement (Data Availability Statements) A Ankan, IMN Wortel, J Textor Current Protocols 1, e58, 2022 | | 2022 |
| Group Correction Statement (Conflict of Interest Statements) A Ankan, IMN Wortel, J Textor Current Protocols 1, e75, 2022 | | 2022 |
| Identifying Model-Implied Instrumental Variables in Structural Equation Models using Graphical Criteria A Ankan | | 2019 |
