An inverse probability of censoring weighted (IPCW) targeted maximum likelihood estimator (TMLE) for evaluating a marginal point treatment effect from data where some variables were collected on only a subset of participants using a two-stage design (or marginal mean outcome for a single arm study). A TMLE for conditional parameters defined by a marginal structural model (MSM) is also available.

Conditional logistic regression with longitudinal follow up and
    individual-level random coefficients: A stable and efficient
    two-step estimation method.

Estimate a sparse Gaussian state-space model with mixed frequency
    data via sparse principal components analysis and the Kalman filter
    and smoother. For more details see Franjic and Schweikert (2024) <doi:10.2139/ssrn.4733872>.

Analyse time to event data with two time scales by estimating a smooth hazard that varies over two time scales. If covariates are available, estimate a proportional hazards model with such a two-dimensional baseline hazard. 
  Functions are provided to prepare the raw data for estimation, to fit the model and to plot the two-dimensional smooth hazard. 
  Extension to a competing risks model are implemented. For details about the method please refer to Carollo et al. (2025) <doi:10.1002/sim.10297>.

Implements combined p-value functions for two trials along with compatible combined point and interval estimates as described in Pawel, Roos, and Held (2025) <doi:10.48550/arXiv.2503.10246>.

Carries out analyses of two-way tables with one observation per cell, together with graphical displays
    for an additive fit and
    a diagnostic plot for removable 'non-additivity' via a power transformation of the response.
    It implements methods from Tukey's Exploratory Data Analysis (1973) <ISBN: 978-0201076165>, including a
    1-degree-of-freedom test for row*column 'non-additivity', linear in the row and column effects.

Computes the implicit weights attached to two-way fixed effects regressions, as well as summary measures of these regressions' robustness to heterogeneous treatment effects. Based on de Chaisemartin and D'Haultfoeuille (2020) <DOI:10.1257/aer.20181169>.