Release history of causal.decomp

The following changes have been made since the initial release of causal.decomp 0.0.1.

Changes in `causal.decomp` 0.1.0.

The function sensitivity is added, which implements the sensitivity analysis for the causal decomposition analysis. As of version 0.1.0, the argument boot.res of sensitivity must be an object generated by smi with a single mediator. The object generated by sensitivity can be visualized in contour plots with robustness values using the plot() method.
If non-NULL weights are used in fitting fit.m and fit.y, the weights are incorporated in the estimation by the smi, mmi, or pocr function.
New data sMIDUS is added, which is synthetic data containing variables from actual Midlife Development in the U.S. (MIDUS) data used in Park et al. (2023). As the actual data is not publicly available due to confidentiality concerns, sMIDUS is not directly derived from the actual data but artificially generated to mimic the actual MIDUS data.

Park, S., Qin, X., & Lee, C. (2020). Estimation and sensitivity analysis for causal decomposition in health disparity research. Sociological Methods & Research, 00491241211067516.
Park, S., Kang, S., & Lee, C. (2021+). Choosing an optimal method for causal decomposition analysis: A better practice for identifying contributing factors to health disparities. arXiv preprint arXiv:2109.06940.
Park, S., Kang, S., Lee, C., & Ma, S. (2023). Sensitivity analysis for causal decomposition analysis: Assessing robustness toward omitted variable bias, Journal of Causal Inference. Forthcoming.