Tidyverse-Compatible Fragility Index Calculations

Provides optimized, Tidyverse-compatible functions for calculating the Fragility Index and Reverse Fragility Index for 2x2 contingency tables from clinical trials. Uses customized hypergeometric and algebraic calculations along with binary search algorithms to achieve substantial speedups over standard implementations, with seamless integration into 'dplyr' pipelines.

Cross-Framework Causal Fragility Index

Provides a unified workflow for running, classifying, visualizing, and interpreting sensitivity analyses for unmeasured confounding across multiple causal frameworks. Introduces the Causal Fragility Index (CFI), a single 0-100 composite score that integrates evidence from the partial R-squared robustness value approach (Cinelli and Hazlett, 2020, ), E-value metrics (VanderWeele and Ding, 2017, ), and the Impact Threshold for a Confounding Variable (Frank, 2000, ) into one interpretable measure of robustness. The package also provides template-based plain-language narrative interpretation and publication-ready reporting, with optional integration with the 'confoundvis' package for sensitivity plots.

Assessing and Visualizing Fragility of Clinical Results with Binary Outcomes

A collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 ; Lin, 2021 ), conventional pairwise meta-analyses (Atal et al., 2019 ), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 ). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 ) and detailed in Lin and Chu (2022 ).

Adversarial Robustness Attacks for Statistical Claims

Attacks fitted R model claims by searching for small plausible perturbations that make a target result disappear. The package focuses on claim-level fragility, smallest-kill reporting, and reproducible caveated robustness checks for ordinary fitted model objects. The methods draw on the fragility-index concept of Walsh et al. (2014) , multiverse analysis of Steegen et al. (2016) , specification-curve analysis of Simonsohn et al. (2020) , and robust covariance estimation of Zeileis (2004) .

Robustness and Drift Auditing for Longitudinal Decision Systems

Provides tools for constructing longitudinal decision paths, quantifying temporal drift, tracking subgroup disparity trajectories, and stress-testing longitudinal conclusions under hidden bias. Implements three signature metrics: the Drift Intensity Index (DII), which measures structural instability in transition dynamics using the Frobenius norm of consecutive transition matrix differences; the Bias Amplification Index (BAI), which quantifies whether group disparities widen or converge over time; and the Temporal Fragility Index (TFI), which estimates the minimum hidden-bias perturbation required to nullify a longitudinal trend conclusion. An interactive 'shiny' application supports exploratory analysis, visualization, and reproducible reporting. Methods are motivated by applications in educational and social science research, including the Early Childhood Longitudinal Study (ECLS). The DII is based on the Frobenius norm as described in Golub and Van Loan (2013, ISBN:9781421407944). The TFI extends the hidden-bias sensitivity framework of Rosenbaum (2002, ISBN:9781441912633). The BAI draws on disparity-trajectory methods discussed in Duncan and Murnane (2011, ISBN:9780871542731).

Sliding Window Functions

Provides type-stable rolling window functions over any R data type. Cumulative and expanding windows are also supported. For more advanced usage, an index can be used as a secondary vector that defines how sliding windows are to be created.

Sparse Lightweight Arrays and Matrices

Data structures and algorithms for sparse arrays and matrices, based on index arrays and simple triplet representations, respectively.

Indexed Data Frames

Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure.

S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)

An S3 class with methods for working with regular and irregular time series. The class stores data as numeric vectors/matrices (or factors) along with a time index of arbitrary class (including numeric, Date, POSIXct, chron, yearmon, yearqtr, etc.). Functions and methods are consistent with the ts class and base R and also extend standard generics. Tools include: Data import/export, coercion, visualization (with base R, 'ggplot2', 'lattice', 'tinyplot'), alignment and merging, aggregation, lags and subsets, rolling analytics, and time-based interpolation/filling. The design is introduced in Zeileis and Grothendieck (2005) .

Time Indexes and Time Indexed Series

Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.