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ggdag - Analyze and Create Elegant Directed Acyclic Graphs

Tidy, analyze, and plot directed acyclic graphs (DAGs). 'ggdag' is built on top of 'dagitty', an R package that uses the 'DAGitty' web tool (<https://dagitty.net/>) for creating and analyzing DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using 'ggplot2' and 'ggraph', as well as common analytic and graphical functions, such as determining adjustment sets and node relationships.

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causal-inferencedagggplot-extension

13.08 score 466 stars 10 dependents 3.5k scripts 8.1k downloads

marginaleffects - Predictions, Comparisons, Slopes, Marginal Means, and Hypothesis Tests

Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference. Details can be found in Arel-Bundock, Greifer, and Heiss (2024) <doi:10.18637/jss.v111.i09>.

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12.27 score 57 dependents 4.9k scripts 38k downloads

ggarrow - Arrows for 'ggplot2'

A 'ggplot2' extension that adds specialised arrow geometry layers. It offers more arrow options than the standard 'grid' arrows that are built-in many line-based geom layers.

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ggplot-extensionggplot2ggplot2-geom

9.17 score 59 stars 5 dependents 139 scripts 4.0k downloads

propensity - A Toolkit for Calculating and Working with Propensity Scores

Calculates propensity score weights for multiple causal 'estimands' across binary, continuous, and categorical exposures. Provides methods for handling extreme propensity scores through trimming, truncation, and calibration. Includes inverse probability weighted estimators that correctly account for propensity score estimation uncertainty.

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8.33 score 26 stars 2 dependents 126 scripts 355 downloads

quartets - Datasets to Help Teach Statistics

In the spirit of Anscombe's quartet, this package includes datasets that demonstrate the importance of visualizing your data, the importance of not relying on statistical summary measures alone, and why additional assumptions about the data generating mechanism are needed when estimating causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>, D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.1080/26939169.2023.2276446>, "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.

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6.86 score 47 stars 1 dependents 51 scripts 295 downloads

halfmoon - Techniques to Build Better Balance

Build better balance in causal inference models. 'halfmoon' helps you assess propensity score models for balance between groups using metrics like standardized mean differences and visualization techniques like mirrored histograms. 'halfmoon' supports both weighting and matching techniques.

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6.80 score 24 stars 1 dependents 124 scripts 548 downloads

deli - M-Estimation and Empirical Sandwich Variance Estimation

Solves estimating equations to obtain M-estimators together with empirical sandwich variance estimates, providing a general interface for both custom and built-in estimating equations. Built-in equations cover basic statistics, regression, causal inference, survival analysis, measurement error, and more. An 'R' port of the 'Python' library 'delicatessen' described in 'Zivich et al. (2022)' <doi:10.48550/arXiv.2203.11300>.

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6.77 score 2 stars 4 dependents 31 scripts 367 downloads

tipr - Tipping Point Analyses

The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al. (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983), Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli & Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele (2016), we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance.

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6.04 score 42 stars 1 dependents 124 scripts 1.4k downloads

balancing - Optimization-Based Balancing Weights for Causal Inference

Calculates optimization-based balancing weights for causal inference, including entropy balancing, inverse probability tilting, the covariate balancing propensity score, energy balancing, characteristic function distance balancing, and stable balancing weights. Weights target covariate balance directly by solving a convex optimization problem, with support for binary, categorical, and continuous exposures across a range of causal estimands. A Rust core provides the numerical solvers.

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rustcargo

4.91 score 3 stars 20 scripts

positively - Diagnose Positivity Violations and Extrapolation in Causal Inference

Provides diagnostics for positivity violations and extrapolation in causal inference. Includes methods based on effective data points, positivity regression trees, hat values, highest density regions, density ratios, and parametric bootstrap bias estimates, covering binary, categorical, and continuous exposures as well as time-varying treatments. Each diagnostic returns a tidy result object and an optional plot.

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4.91 score 1 stars 1 dependents 12 scripts

tidysmd - Tidy Standardized Mean Differences

Tidy standardized mean differences ('SMDs'). 'tidysmd' uses the 'smd' package to calculate standardized mean differences for variables in a data frame, returning the results in a tidy format.

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4.73 score 9 stars 2 dependents 486 downloads

causalgenerics - Shared Generics for the 'r-causal' Ecosystem

A home for the S3 generics shared across the 'r-causal' ecosystem, including 'propensity', 'halfmoon', 'positively', and 'balancing'. Owning the generic definitions in one place lets those packages register methods without masking one another when several are attached at once. The generics cover inverse probability weighted estimation, effective sample size, and the metadata carried by causal weight vectors, and the package supplies the abstract causal weight class and the result class those estimates are returned in, together with the methods both classes carry, so that the ecosystem packages inherit them rather than writing their own. The design follows that of the 'generics' package, which provides commonly used S3 generics for the same purpose: letting packages share a single definition instead of each defining its own.

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4.65 score 1 stars 5 dependents 3 scripts 245 downloads

causalworkshop - Install Workshop Materials for Causal Inference in R

Install workshop materials for the Causal Inference in R.

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4.26 score 24 stars 50 scripts

touringplans - Walt Disney World Park Data

The touringplans data team has collected Walt Disney World Park data from 2015-2021, include wait time data for specific attractions as well as park-specific metadata. This package provides access to these datasets.

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3.80 score 4 stars 1 dependents 105 scripts

causalpie - Causal Pies for Sufficient Causes

Causal pies are a way of visualizing the sufficient causes framework. Sufficient causes are what must occur before some event happens. The components of what causes the event may be in any or all sufficient causes, and those that are in all are called necessary causes. The sufficient cause framework was developed by Kenneth 'Rothman' as a way of thinking about causes of disease in epidemiology ('Rothman' (1976) <doi:10.1093/oxfordjournals.aje.a112335>).

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2.90 score 8 stars 3 scripts