tda-statistical-modeling-toolkit

To fit a logistic regression model in TDL, use this toolkit to estimate the model and then apply its built-in Bayesian robustness checks and simulation-based sensitivity analyses to validate your results.

Yes, you can perform power analysis in TDL. The toolkit assists with power analysis specifically for statistical model validation, helping you assess the reliability of your fitted GLMs and survival models.

You can fit non-topological statistical models in TDL, including Generalized Linear Models, OLS, logit, probit, and survival models, using the toolkit's model fitting and validation functionalities.

Yes, you need prior knowledge of statistical modeling and familiarity with TDL's framework to effectively fit, review, and validate models like GLMs and survival models using this toolkit.

The best way to validate models in TDL is using the toolkit's Bayesian robustness checks and simulation-based sensitivity analyses, which allow you to thoroughly review and validate fitted statistical models.

Simulation-based sensitivity analysis is required in TDL to conduct robustness checks, ensuring that your fitted statistical models remain valid and reliable under varying data conditions and assumptions.