An Interface to Specify Causal Graphs and Compute Bounds on Causal Effects

When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) .

Calculate Outbreak Probabilities for a Branching Process Model

Quantify outbreak risk posed by individual importers of a transmissible pathogen. Input parameters of negative binomial offspring distributions for the number of transmissions from each infected individual and initial number of infected. Calculate probabilities of final outbreak size and generations of transmission, as described in Toth et al. (2015) and Toth et al. (2016) .

Regression Models for Event History Outcomes

A user friendly, easy to understand way of doing event history regression for marginal estimands of interest, including the cumulative incidence and the restricted mean survival, using the pseudo observation framework for estimation. For a review of the methodology, see Andersen and Pohar Perme (2010) or Sachs and Gabriel (2022) . The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and corrected variance estimation.

Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples

Methods for obtaining improved estimates of non-linear cross-validated risks are obtained using targeted minimum loss-based estimation, estimating equations, and one-step estimation (Benkeser, Petersen, van der Laan (2019), ). Cross-validated area under the receiver operating characteristics curve (LeDell, Petersen, van der Laan (2015), ) and other metrics are included.

Prepare and Explore Data for Palaeobiological Analyses

Provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of 'palaeoverse' is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales < https://stratigraphy.org/chart/>) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) .

Visualizing and Quantifying Decision Uncertainty

A suite of tools to help modelers and decision-makers effectively interpret and communicate decision risk when evaluating multiple policy options. It uses model outputs from uncertainty analysis for baseline scenarios and policy alternatives to generate visual representations of uncertainty and quantitative measures for assessing associated risks. For more details see Wiggins and colleagues (2025) and < https://dut.ihe.ca/>.