Developer

multiverseRCT

An R package for conducting multiverse analyses on randomized controlled trial (RCT) data. Instead of making a single set of analytical choices, multiverse analysis explores how different preprocessing decisions and model specifications affect research conclusions.

• Apply multiple preprocessing methods (e.g., different imputation strategies, outlier handling)
• Fit multiple model specifications (adjusted/unadjusted, different model families)
• Visualize the variation in treatment effects across the multiverse
• Quantify the sensitivity of conclusions to analytic decisions

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BriDGE

A comprehensive R package for causal analysis of randomized controlled trial (RCT) data. It seamlessly integrates causal discovery through Directed Acyclic Graphs (DAGs) with flexible mediation analysis using Generalized Additive Models (GAMs).

• Causal Discovery: multiple algorithms (MMHC, HC, PC-stable) to learn causal structures from data
• Flexible Mediation Analysis: handles nonlinear relationships using GAMs with automatic smoothing
• Bootstrap Inference: robust uncertainty quantification through parallel bootstrapping
• Sensitivity Analysis: assess robustness of findings to data perturbations
• Comprehensive Visualization: publication-ready plots for all analysis components

Companion methods paper — "BriDGE the gap: improving Behavioural research by integrating DAGs and GAMs in Experiments" — in press at Behavior Research Methods.

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sequenceRCT

An R package for analyzing behavioral trajectories in randomized controlled trials. It shifts focus from endpoint-only comparisons to tracking how participants transition across discrete states over time — particularly suited for behavioral public policy research where timing, persistence, and relapse patterns are critical to intervention design.

• State Encoding: converts raw state labels into coded sequence matrices
• Transition Analysis: estimates empirical transition probabilities between adjacent time points
• Complexity Profiling: entropy, turbulence, and volatility metrics for participant sequences
• Trajectory Clustering: groups similar behavioral patterns using TraMineR distances and hierarchical clustering
• Statistical Testing: Wilcoxon, Cliff's delta, and Chi-square tests for between-group differences

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ArticleOrbit

Inspired by behavioural science principles and built by an academic who couldn't find the right tracker, ArticleOrbit is the must-have macOS app for managing every manuscript milestone — effortlessly and intuitively.

• Behaviour-Driven Design: interfaces and reminders shaped by proven behavioural science
• Unified Submission Tracker: log titles, venues, authors, and all key dates
• Live Dashboard & Analytics: status breakdown, journal stats, waiting times
• Timeline View: stacked bars map your submission cadence over time
• Quick Sharing & Export: share summaries via Mail or Notes; export to CSV

Perfect for principal investigators, research groups, and individual scholars who value clarity, data, and deadlines.

Randomista

Built by a social scientist who wanted the rigorous path to be the easy one, Randomista walks each study through a defensible randomised controlled trial on your iPad or iPhone — design, power, sealed reproducible randomisation, AsPredicted-style preregistration, and concealed allocation in the field.

• Design: arms, allocation weights, stratification factors, and outcomes — defined once, locked automatically when a sealed sequence exists
• Power: t-test, proportions, and ANOVA calculators validated against R's pwr package, with attrition inflation and power curves
• Randomise: simple, permuted-block, or stratified sequences from a reproducible seed, sealed with a SHA-256 hash re-verified on every open
• Allocate: concealed allocation table; one-handed reveal in the field, stamped once, never editable
• Preregister: nine AsPredicted-style questions filled from your design; OSF-ready Markdown or PDF export

No account, no analytics, no network calls — your trial never leaves your device. US$14.99, one-time purchase, iPadOS & iOS 26+.