Package: SensIAT 0.3.0.9000

Andrew Redd

SensIAT: Sensitivity Analysis for Irregular Assessment Times

Sensitivity analysis for trials with irregular and informative assessment times, based on a new influence function-based, augmented inverse intensity-weighted estimator.

Authors:Andrew Redd [aut, cre], Yujing Gao [aut], Shu Yang [aut], Bonnie Smith [aut], Ravi Varadhan [aut], Agatha Mallett [ctb, ctr], Daniel Scharfstein [pdr, aut], University of Utah [cph]

SensIAT_0.3.0.9000.tar.gz
SensIAT_0.3.0.9000.zip(r-4.7)SensIAT_0.3.0.9000.zip(r-4.6)SensIAT_0.3.0.9000.zip(r-4.5)
SensIAT_0.3.0.9000.tgz(r-4.6-x86_64)SensIAT_0.3.0.9000.tgz(r-4.6-arm64)SensIAT_0.3.0.9000.tgz(r-4.5-x86_64)SensIAT_0.3.0.9000.tgz(r-4.5-arm64)
SensIAT_0.3.0.9000.tar.gz(r-4.7-arm64)SensIAT_0.3.0.9000.tar.gz(r-4.7-x86_64)SensIAT_0.3.0.9000.tar.gz(r-4.6-arm64)SensIAT_0.3.0.9000.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
SensIAT/json (API)
NEWS

# Install 'SensIAT' in R:

install.packages('SensIAT', repos = c('https://uofuepibio.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/uofuepibio/sensiat/issues

Pkgdown/docs site:https://uofuepibio.github.io

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

SensIAT_example_data - SensIAT Example Data
SensIAT_example_fulldata - SensIAT Example Data

On CRAN:

cpp

5.62 score 1 stars 12 scripts 484 downloads 17 exports 44 dependencies

Last updated from:908f22c984. Checks:11 OK, 1 ERROR, 1 FAIL. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	475
linux-devel-x86_64	OK	449
source / vignettes	OK	379
linux-release-arm64	OK	430
linux-release-x86_64	OK	410
macos-release-arm64	OK	252
macos-release-x86_64	OK	1083
macos-oldrel-arm64	OK	378
macos-oldrel-x86_64	OK	840
windows-devel	ERROR	484
windows-release	OK	542
windows-oldrel	OK	536
wasm-release	FAIL	143

Exports:benchmark_term2_methods compute_influence_terms compute_SensIAT_expected_values extrapolate_from_last_observation fit_marginal_model fit_SensIAT_fulldata_model fit_SensIAT_marginal_mean_model fit_SensIAT_marginal_mean_model_generalized fit_SensIAT_single_index_fixed_bandwidth_model fit_SensIAT_single_index_fixed_coef_model fit_SensIAT_single_index_norm1coef_model fit_SensIAT_within_group_model jackknife make_term2_integrand_fast prepare_SensIAT_data simulate_SensIAT_data simulate_SensIAT_two_groups

Dependencies:assertthat BB class cli cpp11 dplyr farver generics ggplot2 glue gtable isoband KernSmooth labeling lattice lifecycle magrittr MASS Matrix MAVE mda orthogonalsplinebasis pillar pkgconfig pracma purrr quadprog R6 RColorBrewer Rcpp RcppArmadillo rlang S7 scales stringi stringr survival tibble tidyr tidyselect utf8 vctrs viridisLite withr

Fitting with Different Loss and Link Functions

Rendered fromloss_and_links.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2026-04-15
Started: 2025-10-17

Term2 Integration Methods: Performance Comparison

Rendered fromterm2-integration-methods.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2026-04-15
Started: 2026-04-15

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Plot for Estimated Treatment Effect for 'SensIAT_fulldata_jackknife_results' Objects	autoplot.SensIAT_fulldata_jackknife_results
Plot for Estimated Treatment Effect for 'SensIAT_fulldata_model' Objects	autoplot.SensIAT_fulldata_model
Plot a 'SensIAT_within_group_model' Object	autoplot.SensIAT_within_group_model
Plot Estimates at Given Times for 'SensIAT_withingroup_jackknife_results' Objects	autoplot.SensIAT_withingroup_jackknife_results
Benchmark Term2 Integration Methods	benchmark_term2_methods
Compute Influence Terms	compute_influence_terms compute_influence_terms.default compute_influence_terms.glm compute_influence_terms.SensIAT::Single-index-outcome-model
Compute Conditional Expected Values based on Outcome Model	compute_SensIAT_expected_values compute_SensIAT_expected_values.glm compute_SensIAT_expected_values.lm compute_SensIAT_expected_values.negbin
Fit SensIAT Marginal Mean Model (Unified)	fit_marginal_model
Produce fitted model for group (treatment or control)	fit_SensIAT_fulldata_model fit_SensIAT_within_group_model
Fit the Marginal Means Model	fit_SensIAT_marginal_mean_model
Fit the marginal mean model for generalize outcomes.	fit_SensIAT_marginal_mean_model_generalized
Outcome Modeler for 'SensIAT' Single Index Model.	fit_SensIAT_single_index_fixed_bandwidth_model fit_SensIAT_single_index_fixed_coef_model
Single Index Model using 'MAVE' and Optimizing Bandwidth.	fit_SensIAT_single_index_norm1coef_model
Perform Jackknife Resampling on an Object	jackknife jackknife.SensIAT_fulldata_model jackknife.SensIAT_within_group_model
Give the Marginal Mean Estimate and its Estimated Asymptotic Variance	predict.SensIAT_fulldata_model predict.SensIAT_within_group_model
Prepare Data for Sensitivity Analysis with Irregular Assessment Times	prepare_SensIAT_data
SensIAT Example Data	SensIAT_example_data SensIAT_example_fulldata
Simulate SensIAT Data	simulate_SensIAT_data
Simulate Treatment and Control Groups	simulate_SensIAT_two_groups