Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data.

Implements covariance-stabilised sufficient dimension reduction
 for continuous responses with information-theoretic structural dimension
 selection. Supported methods include sliced inverse regression, sliced
 average variance estimation, directional regression, and principal Hessian
 directions. Sample, ridge, Oracle Approximating Shrinkage, Ledoit-Wolf, and
 Maximum Entropy Covariance (MEC) estimators are provided alongside
 prediction, resampling, simulation, and diagnostic utilities. The
 sufficient dimension reduction
 methods build on Li (1991) <doi:10.1080/01621459.1991.10475035>, Li (1992)
 <doi:10.1080/01621459.1992.10476258>, and Li and Wang (2007)
 <doi:10.1198/016214507000000536>. Covariance shrinkage follows Olorede and
 Yahya (2019) <doi:10.48550/arXiv.1909.13017>, Ledoit and
 Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4> and Chen et al. (2010)
 <doi:10.1109/TSP.2010.2053029>.

Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) 
    <doi:10.19173/irrodl.v18i2.2952>.	Automates the process of identifying learning materials that are not effectively supporting student learning in 
    technology-mediated courses by synthesizing information about access to course content and performance on assessments.