No packages match

gfoRmula - Parametric G-Formula

Implements the non-iterative conditional expectation (NICE) algorithm of the g-formula algorithm (Robins (1986) <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). The g-formula can estimate an outcome's counterfactual mean or risk under hypothetical treatment strategies (interventions) when there is sufficient information on time-varying treatments and confounders. This package can be used for discrete or continuous time-varying treatments and for failure time outcomes or continuous/binary end of follow-up outcomes. The package can handle a random measurement/visit process and a priori knowledge of the data structure, as well as censoring (e.g., by loss to follow-up) and two options for handling competing events for failure time outcomes. Interventions can be flexibly specified, both as interventions on a single treatment or as joint interventions on multiple treatments. See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on how to use the package.

Last updated

7.57 score 186 stars 201 scripts 568 downloads

metamedian - Meta-Analysis of Medians

Implements several methods to meta-analyze studies that report the sample median of the outcome. The methods described by McGrath et al. (2019) <doi:10.1002/sim.8013>, Ozturk and Balakrishnan (2020) <doi:10.1002/sim.8738>, and McGrath et al. (2020a) <doi:10.1002/bimj.201900036> can be applied to directly meta-analyze the median or difference of medians between groups. Additionally, a number of methods (e.g., McGrath et al. (2020b) <doi:10.1177/0962280219889080>, Cai et al. (2021) <doi:10.1177/09622802211047348>, and McGrath et al. (2023) <doi:10.1177/09622802221139233>) are implemented to estimate study-specific (difference of) means and their standard errors in order to estimate the pooled (difference of) means. Methods for meta-analyzing median survival times (McGrath et al. (2026) <doi:10.1002/sim.70533>) are also implemented. See McGrath et al. (2024) <doi:10.1002/jrsm.1686> for a detailed guide on using the package.

Last updated

4.65 score 10 stars 18 scripts 584 downloads

estmeansd - Estimating the Sample Mean and Standard Deviation from Commonly Reported Quantiles in Meta-Analysis

Implements the methods of McGrath et al. (2020) <doi:10.1177/0962280219889080> and Cai et al. (2021) <doi:10.1177/09622802211047348> for estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis. These methods can be applied to studies that report the sample median, sample size, and one or both of (i) the sample minimum and maximum values and (ii) the first and third quartiles. The corresponding standard error estimators described by McGrath et al. (2023) <doi:10.1177/09622802221139233> are also included.

Last updated

4.47 score 3 stars 2 dependents 3.3k downloads

smoothedIPW - Time-Smoothed Inverse Probability Weighting for Repeatedly Measured Outcomes

Implements several methods to estimate effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interest. Specifically, the package implements the time-smoothed inverse probability weighted estimators described in McGrath et al. (2025) <doi:10.48550/arXiv.2509.13971>. Outcomes may be repeatedly, non-monotonically, informatively, and sparsely measured in the data source. The package also supports settings where outcomes are truncated by death, i.e. some individuals die during follow-up which renders the outcome of interest undefined at the follow-up time of interest.

Last updated

3.54 score 198 downloads

miapack - Marginalization over Incomplete Auxiliaries

Implements methods to estimate conditional outcome means in settings with missingness-not-at-random and incomplete auxiliary variables. Specifically, this package implements the marginalization over incomplete auxiliaries (MIA) method proposed by Mathur et al. (2026) <doi:10.13140/RG.2.2.30750.19524>. The package supports the iterative conditional expectation estimator and the noniterative conditional expectation estimator.

Last updated

3.48 score 2 scripts 232 downloads

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. (2025) <doi:10.1017/rsm.2025.5> for a detailed guide on using the package.

Last updated

3.48 score 3 stars 3 scripts 423 downloads

learner - Latent Space-Based Transfer Learning

Implements transfer learning methods for low-rank matrix estimation. These methods leverage similarity in the latent row and column spaces between one or more source populations and a target population to improve estimation in the target population. The methods include the LatEnt spAce-based tRaNsfer lEaRning (LEARNER) method and the direct projection LEARNER (D-LEARNER) method described by McGrath et al. (2024) <doi:10.48550/arXiv.2412.20605>. Supports weighted combinations of source spaces, separate row- and column-space penalties, and cross-validation for penalty selection.

Last updated

cppopenmp

3.30 score 2 stars 4 scripts 314 downloads