Showing 26 of total 26 results (show query)
kkholst
mets:Analysis of Multivariate Event Times
Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.
Maintained by Klaus K. Holst. Last updated 3 days ago.
multivariate-time-to-eventsurvival-analysistime-to-eventfortranopenblascpp
6.3 match 14 stars 13.47 score 236 scripts 42 dependents
ellessenne
rsimsum:Analysis of Simulation Studies Including Monte Carlo Error
Summarise results from simulation studies and compute Monte Carlo standard errors of commonly used summary statistics. This package is modelled on the 'simsum' user-written command in 'Stata' (White I.R., 2010 <https://www.stata-journal.com/article.html?article=st0200>), further extending it with additional performance measures and functionality.
Maintained by Alessandro Gasparini. Last updated 10 months ago.
biostatisticsmonte-carlo-errorsimulationsimulation-studysimulationsstatistics
4.7 match 28 stars 7.70 score 148 scripts
boehringer-ingelheim
BPrinStratTTE:Causal Effects in Principal Strata Defined by Antidrug Antibodies
Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event setting.
Maintained by Christian Stock. Last updated 11 months ago.
bayesian-methodscausal-inferenceclinical-trialestimandmcmc-methodspharmaceutical-developmentprincipal-stratificationsimulationstantime-to-eventcpp
11.0 match 3.18 score
ngreifer
WeightIt:Weighting for Covariate Balance in Observational Studies
Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality of several R packages and providing in-house estimation methods. Available methods include those that rely on parametric modeling, optimization, and machine learning. Also allows for assessment of weights and checking of covariate balance by interfacing directly with the 'cobalt' package. Methods for estimating weighted regression models that take into account uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how to install any package 'WeightIt' uses, including those that may not be on CRAN.
Maintained by Noah Greifer. Last updated 5 days ago.
causal-inferenceinverse-probability-weightsobservational-studypropensity-scores
2.7 match 112 stars 11.58 score 508 scripts 3 dependents
openpharma
beeca:Binary Endpoint Estimation with Covariate Adjustment
Performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.
Maintained by Alex Przybylski. Last updated 4 months ago.
4.7 match 6 stars 5.48 score 8 scripts
kosukeimai
MatchIt:Nonparametric Preprocessing for Parametric Causal Inference
Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.)
Maintained by Noah Greifer. Last updated 2 days ago.
1.5 match 220 stars 15.03 score 2.4k scripts 21 dependents
hakeemwahab
cities:Clinical Trials with Intercurrent Events Simulator
Simulates clinical trials and summarizes causal effects and treatment policy estimands in the presence of intercurrent events in a transparent and intuitive manner.
Maintained by Ahmad Hakeem Abdul Wahab. Last updated 2 years ago.
8.2 match 1 stars 2.59 score 39 scripts
ilundberg
gapclosing:Estimate Gaps Under an Intervention
Provides functions to estimate the disparities across categories (e.g. Black and white) that persists if a treatment variable (e.g. college) is equalized. Makes estimates by treatment modeling, outcome modeling, and doubly-robust augmented inverse probability weighting estimation, with standard errors calculated by a nonparametric bootstrap. Cross-fitting is supported. Survey weights are supported for point estimation but not for standard error estimation; those applying this package with complex survey samples should consult the data distributor to select an appropriate approach for standard error construction, which may involve calling the functions repeatedly for many sets of replicate weights provided by the data distributor. The methods in this package are described in Lundberg (2021) <doi:10.31235/osf.io/gx4y3>.
Maintained by Ian Lundberg. Last updated 3 months ago.
4.7 match 6 stars 4.48 score 8 scripts
priism-center
plotBart:Diagnostic and Plotting Functions to Supplement 'bartCause'
Functions to assist in diagnostics and plotting during the causal inference modeling process. Supplements the 'bartCause' package.
Maintained by Joseph Marlo. Last updated 10 months ago.
3.5 match 2 stars 4.30 score 20 scripts
lmaowisc
rmt:Restricted Mean Time in Favor of Treatment
Contains inferential and graphical routines for comparing two treatment arms in terms of the restricted mean time in favor of treatment.
Maintained by Lu Mao. Last updated 3 months ago.
2.7 match 4.34 score 22 scripts
soodoku
guess:Adjust Estimates of Learning for Guessing
Adjust Estimates of Learning for Guessing. The package provides standard guessing correction, and a latent class model that leverages informative pre-post transitions. For details of the latent class model, see <http://gsood.com/research/papers/guess.pdf>.
Maintained by Gaurav Sood. Last updated 3 years ago.
2.2 match 3 stars 4.29 score 13 scripts
sachsmc
pseval:Methods for Evaluating Principal Surrogates of Treatment Response
Contains the core methods for the evaluation of principal surrogates in a single clinical trial. Provides a flexible interface for defining models for the risk given treatment and the surrogate, the models for integration over the missing counterfactual surrogate responses, and the estimation methods. Estimated maximum likelihood and pseudo-score can be used for estimation, and the bootstrap for inference. A variety of post-estimation summary methods are provided, including print, summary, plot, and testing.
Maintained by Michael C Sachs. Last updated 6 years ago.
2.0 match 1 stars 3.88 score 15 scriptslmaowisc
WA:While-Alive Loss Rate for Recurrent Event in the Presence of Death
Contains inferential and graphical routines for multi-group analysis of while-alive loss (or event) rate for possibly recurrent nonfatal event in the presence of death.
Maintained by Lu Mao. Last updated 3 years ago.
2.0 match 3.00 score 6 scripts
tkhdyanagi
latenetwork:Inference on LATEs under Network Interference of Unknown Form
Estimating causal parameters in the presence of treatment spillover is of great interest in statistics. This package provides tools for instrumental variables estimation of average causal effects under network interference of unknown form. The target parameters are the local average direct effect, the local average indirect effect, the local average overall effect, and the local average spillover effect. The methods are developed by Hoshino and Yanagi (2023) <doi:10.48550/arXiv.2108.07455>.
Maintained by Takahide Yanagi. Last updated 2 years ago.
1.3 match 4.00 score 5 scripts
cran
ivdesign:Hypothesis Testing in Cluster-Randomized Encouragement Designs
An implementation of randomization-based hypothesis testing for three different estimands in a cluster-randomized encouragement experiment. The three estimands include (1) testing a cluster-level constant proportional treatment effect (Fisher's sharp null hypothesis), (2) pooled effect ratio, and (3) average cluster effect ratio. To test the third estimand, user needs to install 'Gurobi' (>= 9.0.1) optimizer via its R API. Please refer to <https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html>.
Maintained by Bo Zhang. Last updated 5 years ago.
4.3 match 1.00 score
sachsmc
eventglm: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) <doi:10.1177/0962280209105020> or Sachs and Gabriel (2022) <doi:10.18637/jss.v102.i09>. The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and corrected variance estimation.
Maintained by Michael C Sachs. Last updated 16 days ago.
0.5 match 5 stars 6.33 score 24 scripts 1 dependentscran
CICI:Causal Inference with Continuous (Multiple Time Point) Interventions
Estimation of counterfactual outcomes for multiple values of continuous interventions at different time points, and plotting of causal dose-response curves. Details are given in Schomaker, McIlleron, Denti, Diaz (2024) <doi:10.48550/arXiv.2305.06645>.
Maintained by Michael Schomaker. Last updated 3 months ago.
1.9 match 1 stars 1.48 score
riccardo-df
causalQual:Causal Inference for Qualitative Outcomes
Implements the framework introduced in Di Francesco and Mellace (2025) <doi:10.48550/arXiv.2502.11691>, shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences designs.
Maintained by Riccardo Di Francesco. Last updated 20 days ago.
0.5 match 10 stars 5.30 score
ly129
CausalMetaR:Causally Interpretable Meta-Analysis
Provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset, including those of Dahabreh et al. (2019) <doi:10.1111/biom.13716>, Robertson et al. (2021) <doi:10.48550/arXiv.2104.05905>, and Wang et al. (2024) <doi:10.48550/arXiv.2402.02684>. The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects and subgroup treatment effects. See Wang et al. (2024) <doi:10.48550/arXiv.2402.04341> for a detailed guide on using the package.
Maintained by Sean McGrath. Last updated 2 months ago.
0.5 match 2 stars 3.60 score 3 scripts
billytian
H2x2Factorial:Sample Size Calculation in Hierarchical 2x2 Factorial Trials
Implements the sample size methods for hierarchical 2x2 factorial trials under two choices of effect estimands and a series of hypothesis tests proposed in "Sample size calculation in hierarchical 2x2 factorial trials with unequal cluster sizes" (under review), and provides the table and plot generators for the sample size estimations.
Maintained by Zizhong Tian. Last updated 3 years ago.
0.5 match 2.70 score 1 scripts
amitsasson
TheSFACE:The Subtype Free Average Causal Effect
Estimation of the SF-ACE, a Causal Inference estimand proposed in the paper "The Subtype-Free Average Causal Effect For Heterogeneous Disease Etiology" (soon on arXiv).
Maintained by Amit Sasson. Last updated 3 years ago.
0.5 match 1.70 scorecran
randomizationInference:Flexible Randomization-Based Inference
Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.
Maintained by Joseph J. Lee. Last updated 3 years ago.
0.5 match 1.15 score 14 scripts