Delicatessen

- Checked compatibility with NumPy 2.0
- Added estimating equation for regression calibration to measurement error corrections
- Change to pharmacokinetic model underlying structure
- Added E-max model and its ED function as estimating equations
- Replaced parameter log-logistic models with the more general `ee_loglogistic` estimating equation. The `ee_loglogistic` models will be removed in v3.0

- Added extended Rogan-Gladen to correct for differential measurement error
- Added separate functionality to compute the sandwich matrix. This avoids needing to call `MEstimator` to compute the sandwich matrix.
- Updated all docs

v2.1 addition of new estimating equations: Rogan-Gladen measurement error correction, multinomial logistic regression, efficient g-estimation, log-linear SMM g-estimation.

Added support for Python 3.12

Added option to rescale spline terms when generated

Re-organized test structure for easier maintenance (does not impact actual package)

Bug fixes: fixed issue in call to ee_lasso_regression

v2.0 adds an automatic differentiation functionality to compute the bread matrix. So, now both numerical approximation and automatic differentiation are supported.

Additions of v1.4 release

- Added Generalized Linear Models (GLM) as a built-in estimating equation
- Added Z-scores, P-values, and S-values
- Added Marginal Structural Models with Inverse Probability Weighting as a built-in estimating equation
- Added support for missingness weights with `ee_ipw` and `ee_gestimation_snmm`

- Adds G-estimation of linear structural nested mean models
- Fixes bug with Powell hybrid method for root-finding specification