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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)
Spatial Modeling on Stream Networks
Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010)
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)
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),
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)