Showing 169 of total 169 results (show query)
teppeiyamamoto
mediation:Causal Mediation Analysis
We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040> and Yamamoto (2013) <http://web.mit.edu/teppei/www/research/IVmediate.pdf>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.
Maintained by Teppei Yamamoto. Last updated 6 years ago.
126.5 match 10.48 score 896 scripts 11 dependentssfcheung
manymome:Mediation, Moderation and Moderated-Mediation After Model Fitting
Computes indirect effects, conditional effects, and conditional indirect effects in a structural equation model or path model after model fitting, with no need to define any user parameters or label any paths in the model syntax, using the approach presented in Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z>. Can also form bootstrap confidence intervals by doing bootstrapping only once and reusing the bootstrap estimates in all subsequent computations. Supports bootstrap confidence intervals for standardized (partially or completely) indirect effects, conditional effects, and conditional indirect effects as described in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Model fitting can be done by structural equation modeling using lavaan() or regression using lm().
Maintained by Shu Fai Cheung. Last updated 23 days ago.
bootstrappingconfidence-intervallavaanmanymomemediationmoderated-mediationmoderationregressionsemstandardized-effect-sizestructural-equation-modeling
133.7 match 1 stars 8.06 score 172 scripts 4 dependentscedricbatailler
JSmediation:Mediation Analysis Using Joint Significance
A set of helper functions to conduct joint-significance tests for mediation analysis, as recommended by Yzerbyt, Muller, Batailler, & Judd. (2018) <doi:10.1037/pspa0000132>.
Maintained by Cรฉdric Batailler. Last updated 2 months ago.
mediationmediation-analysispsychology
62.6 match 8 stars 6.06 score 36 scriptskrisrs1128
multimedia:Multimodal Mediation Analysis
Multimodal mediation analysis is an emerging problem in microbiome data analysis. Multimedia make advanced mediation analysis techniques easy to use, ensuring that all statistical components are transparent and adaptable to specific problem contexts. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis. More details are available in Jiang et al. (2024) "multimedia: Multimodal Mediation Analysis of Microbiome Data" <doi:10.1101/2024.03.27.587024>.
Maintained by Kris Sankaran. Last updated 30 days ago.
coveragemicrobiomeregressionsequencingsoftwarestatisticalmethodstructuralequationmodelscausal-inferencedata-integrationmediation-analysis
63.0 match 1 stars 5.56 score 13 scriptskaz-yos
regmedint:Regression-Based Causal Mediation Analysis with Interaction and Effect Modification Terms
This is an extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>). It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) <doi:10.1097/EDE.0000000000001643>. It also accommodates the original 'SAS' macro and 'PROC CAUSALMED' procedure in 'SAS' when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.
Maintained by Yi Li. Last updated 1 years ago.
causal-inferencemediation-analysis
37.9 match 29 stars 6.84 score 40 scriptsyinanzheng
HIMA:High-Dimensional Mediation Analysis
Allows to estimate and test high-dimensional mediation effects based on advanced mediator screening and penalized regression techniques. Methods used in the package refer to Zhang H, Zheng Y, Zhang Z, Gao T, Joyce B, Yoon G, Zhang W, Schwartz J, Just A, Colicino E, Vokonas P, Zhao L, Lv J, Baccarelli A, Hou L, Liu L. Estimating and Testing High-dimensional Mediation Effects in Epigenetic Studies. Bioinformatics. (2016) <doi:10.1093/bioinformatics/btw351>. PMID: 27357171.
Maintained by Yinan Zheng. Last updated 1 months ago.
32.4 match 24 stars 7.22 score 23 scriptstysonstanley
MarginalMediation:Marginal Mediation
Provides the ability to perform "Marginal Mediation"--mediation wherein the indirect and direct effects are in terms of the average marginal effects (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package. This framework allows the use of categorical mediators and outcomes with little change in interpretation from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details on the method.
Maintained by Tyson S Barrett. Last updated 3 years ago.
average-marginal-effectsmarginsmediationmediation-analysismediatorpartial-effectsrstudio
42.0 match 3 stars 4.29 score 13 scriptsaalfons
robmed:(Robust) Mediation Analysis
Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) <doi:10.18637/jss.v103.i13>. Further discussion on robust mediation analysis can be found in Alfons & Schley (2024) <doi:10.31234/osf.io/2hqdy>.
Maintained by Andreas Alfons. Last updated 16 days ago.
27.8 match 6 stars 6.35 score 31 scripts 1 dependentskkholst
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
11.2 match 14 stars 13.47 score 236 scripts 42 dependentsjmpsteen
medflex:Flexible Mediation Analysis Using Natural Effect Models
Run flexible mediation analyses using natural effect models as described in Lange, Vansteelandt and Bekaert (2012) <DOI:10.1093/aje/kwr525>, Vansteelandt, Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> and Loeys, Moerkerke, De Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.
Maintained by Johan Steen. Last updated 2 years ago.
causal-inferenceflexible-modelingmediation-analysis
18.8 match 23 stars 7.09 score 54 scriptsmvuorre
bmlm:Bayesian Multilevel Mediation
Easy estimation of Bayesian multilevel mediation models with Stan.
Maintained by Matti Vuorre. Last updated 4 months ago.
bayesian-data-analysismultilevel-mediation-modelsstatisticscpp
21.1 match 42 stars 5.81 score 34 scriptsdcoffman
tvmediation:Time Varying Mediation Analysis
Provides functions for estimating mediation effects that vary over time as described in Cai X, Coffman DL, Piper ME, Li R. Estimation and inference for the mediation effect in a time-varying mediation model. BMC Med Res Methodol. 2022;22(1):1-12.
Maintained by Donna Coffman. Last updated 3 years ago.
23.9 match 3 stars 4.95 score 4 scriptswjzhong
SMUT:Multi-SNP Mediation Intersection-Union Test
Testing the mediation effect of multiple SNPs on an outcome through a mediator.
Maintained by Wujuan Zhong. Last updated 4 years ago.
24.1 match 1 stars 4.91 score 27 scripts 2 dependentskimberlywebb
COMMA:Correcting Misclassified Mediation Analysis
Use three methods to estimate parameters from a mediation analysis with a binary misclassified mediator. These methods correct for the problem of "label switching" using Youden's J criteria. A detailed description of the analysis methods is available in Webb and Wells (2024), "Effect estimation in the presence of a misclassified binary mediator" <doi:10.48550/arXiv.2407.06970>.
Maintained by Kimberly Webb. Last updated 3 months ago.
21.9 match 5.18 score 7 scriptsweiliang
powerMediation:Power/Sample Size Calculation for Mediation Analysis
Functions to calculate power and sample size for testing (1) mediation effects; (2) the slope in a simple linear regression; (3) odds ratio in a simple logistic regression; (4) mean change for longitudinal study with 2 time points; (5) interaction effect in 2-way ANOVA; and (6) the slope in a simple Poisson regression.
Maintained by Weiliang Qiu. Last updated 4 years ago.
28.0 match 3 stars 3.97 score 65 scripts 2 dependentsams329
mzipmed:Mediation using MZIP Model
We implement functions allowing for mediation analysis to be performed in cases where the mediator is a count variable with excess zeroes. First a function is provided allowing users to perform analysis for zero-inflated count variables using the marginalized zero-inflated Poisson (MZIP) model (Long et al. 2014 <DOI:10.1002/sim.6293>). Using the counterfactual approach to mediation and MZIP we can obtain natural direct and indirect effects for the overall population. Using delta method processes variance estimation can be performed instantaneously. Alternatively, bootstrap standard errors can be used. We also provide functions for cases with exposure-mediator interactions with four-way decomposition of total effect.
Maintained by Andrew Sims. Last updated 2 years ago.
36.7 match 1 stars 3.00 score 2 scriptscaubm
ExactMed:Exact Mediation Analysis for Binary Outcomes
A tool for conducting exact parametric regression-based causal mediation analysis of binary outcomes as described in Samoilenko, Blais and Lefebvre (2018) <doi:10.1353/obs.2018.0013>; Samoilenko, Lefebvre (2021) <doi:10.1093/aje/kwab055>; and Samoilenko, Lefebvre (2023) <doi:10.1002/sim.9621>.
Maintained by Miguel Caubet. Last updated 1 years ago.
28.3 match 3.70 score 5 scriptssfcheung
semlbci:Likelihood-Based Confidence Interval in Structural Equation Models
Forms likelihood-based confidence intervals (LBCIs) for parameters in structural equation modeling, introduced in Cheung and Pesigan (2023) <doi:10.1080/10705511.2023.2183860>. Currently implements the algorithm illustrated by Pek and Wu (2018) <doi:10.1037/met0000163>, and supports the robust LBCI proposed by Falk (2018) <doi:10.1080/10705511.2017.1367254>.
Maintained by Shu Fai Cheung. Last updated 2 months ago.
confidence-intervalslavaanlikelihood-basedprofile-likelihoodstructural-equation-modeling
16.6 match 1 stars 5.93 score 188 scriptsdata-wise
RMediation:Mediation Analysis Confidence Intervals
We provide functions to compute confidence intervals for a well-defined nonlinear function of the model parameters (e.g., product of k coefficients) in single--level and multilevel structural equation models. It also computes a chi-square test statistic for a function of indirect effects. 'Tofighi', D. and 'MacKinnon', D. P. (2011). 'RMediation' An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692--700. <doi:10.3758/s13428-011-0076-x>. 'Tofighi', D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. <doi:10.3389/fpsyg.2019.02989>.
Maintained by Davood Tofighi. Last updated 1 years ago.
causal-inferenceconfidence-intervalslikelihood-ratio-testmediationmediation-analysis
23.0 match 1 stars 4.10 score 25 scriptsmeilinjiang
MAZE:Mediation Analysis for Zero-Inflated Mediators
A novel mediation analysis approach to address zero-inflated mediators containing true zeros and false zeros. See Jiang et al (2023) "A Novel Causal Mediation Analysis Approach for Zero-Inflated Mediators" <arXiv:2301.10064> for more details.
Maintained by Meilin Jiang. Last updated 1 years ago.
23.7 match 3.70 score 2 scriptseasystats
bayestestR:Understand and Describe Bayesian Models and Posterior Distributions
Provides utilities to describe posterior distributions and Bayesian models. It includes point-estimates such as Maximum A Posteriori (MAP), measures of dispersion (Highest Density Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and indices used for null-hypothesis testing (such as ROPE percentage, pd and Bayes factors). References: Makowski et al. (2021) <doi:10.21105/joss.01541>.
Maintained by Dominique Makowski. Last updated 10 hours ago.
bayes-factorsbayesfactorbayesianbayesian-frameworkcredible-intervaleasystatshacktoberfesthdimapposterior-distributionsrope
4.8 match 579 stars 16.84 score 2.2k scripts 82 dependentsnhejazi
medoutcon:Efficient Natural and Interventional Causal Mediation Analysis
Efficient estimators of interventional (in)direct effects in the presence of mediator-outcome confounding affected by exposure. The effects estimated allow for the impact of the exposure on the outcome through a direct path to be disentangled from that through mediators, even in the presence of intermediate confounders that complicate such a relationship. Currently supported are non-parametric efficient one-step and targeted minimum loss estimators based on the formulation of Dรญaz, Hejazi, Rudolph, and van der Laan (2020) <doi:10.1093/biomet/asaa085>. Support for efficient estimation of the natural (in)direct effects is also provided, appropriate for settings in which intermediate confounders are absent. The package also supports estimation of these effects when the mediators are measured using outcome-dependent two-phase sampling designs (e.g., case-cohort).
Maintained by Nima Hejazi. Last updated 1 years ago.
causal-inferencecausal-machine-learninginverse-probability-weightsmachine-learningmediation-analysisstochastic-interventionstargeted-learningtreatment-effects
17.6 match 13 stars 4.46 score 22 scriptsnhejazi
medshift:Causal mediation analysis for stochastic interventions
Estimators of a parameter arising in the decomposition of the population intervention (in)direct effect of stochastic interventions in causal mediation analysis, including efficient one-step, targeted minimum loss (TML), re-weighting (IPW), and substitution estimators. The parameter estimated constitutes a part of each of the population intervention (in)direct effects. These estimators may be used in assessing population intervention (in)direct effects under stochastic treatment regimes, including incremental propensity score interventions and modified treatment policies. The methodology was first discussed by I Dรญaz and NS Hejazi (2020) <doi:10.1111/rssb.12362>.
Maintained by Nima Hejazi. Last updated 3 years ago.
causal-inferenceinverse-probability-weightsmachine-learningmediation-analysisstochastic-interventionstargeted-learningtreatment-effects
21.0 match 9 stars 3.73 score 12 scriptszhaoyi1026
cfma:Causal Functional Mediation Analysis
Performs causal functional mediation analysis (CFMA) for functional treatment, functional mediator, and functional outcome. This package includes two functional mediation model types: (1) a concurrent mediation model and (2) a historical influence mediation model. See Zhao et al. (2018), Functional Mediation Analysis with an Application to Functional Magnetic Resonance Imaging Data, <arXiv:1805.06923> for details.
Maintained by Yi Zhao. Last updated 5 years ago.
23.5 match 3 stars 3.18 score 8 scriptsyaoxiangli
cmmr:CEU Mass Mediator RESTful API
CEU (CEU San Pablo University) Mass Mediator is an on-line tool for aiding researchers in performing metabolite annotation. 'cmmr' (CEU Mass Mediator RESTful API) allows for programmatic access in R: batch search, batch advanced search, MS/MS (tandem mass spectrometry) search, etc. For more information about the API Endpoint please go to <https://github.com/YaoxiangLi/cmmr>.
Maintained by Yaoxiang Li. Last updated 5 months ago.
batch-searchceu-mass-mediatormetablomicsms-search
15.5 match 15 stars 4.73 score 12 scriptsqingzhaoyu
mlma:Multilevel Mediation Analysis
Do multilevel mediation analysis with generalized additive multilevel models. The analysis method is described in Yu and Li (2020), "Third-Variable Effect Analysis with Multilevel Additive Models", PLoS ONE 15(10): e0241072.
Maintained by Qingzhao Yu. Last updated 2 years ago.
18.7 match 1 stars 3.84 score 69 scriptsqingzhaoyu
mma:Multiple Mediation Analysis
Used for general multiple mediation analysis. The analysis method is described in Yu and Li (2022) (ISBN: 9780367365479) "Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS", published by Chapman and Hall/CRC; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.
Maintained by Qingzhao Yu. Last updated 2 years ago.
18.1 match 1 stars 3.96 score 61 scripts 1 dependentsbioc
Moonlight2R:Identify oncogenes and tumor suppressor genes from omics data
The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes.
Maintained by Matteo Tiberti. Last updated 2 months ago.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksurvivalgenesetenrichmentnetworkenrichment
10.8 match 5 stars 6.59 score 43 scriptssfcheung
manymome.table:Publication-Ready Tables for 'manymome' Results
Converts results from the 'manymome' package, presented in Cheung and Cheung (2023) <doi:10.3758/s13428-023-02224-z>, to publication-ready tables.
Maintained by Shu Fai Cheung. Last updated 3 months ago.
lavaanmanymomemediationmoderated-mediationmoderationregressionsemstructural-equation-modeling
17.5 match 4.00 score 2 scriptsfalkcarl
multilevelmediation:Utility Functions for Multilevel Mediation Analysis
The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for multilevel mediation, the option of a moderating variable for either the a, b, or both paths, and covariates. Currently the 1-1-1 model is supported and several options of random effects; the initial code for bootstrapping was evaluated in simulations by Falk, Vogel, Hammami, and Mioฤeviฤ (2024) <doi:10.3758/s13428-023-02079-4>. Support for Bayesian estimation using 'brms' comprises ongoing work. Currently only continuous mediators and outcomes are supported. Factors for any predictors must be numerically represented.
Maintained by Carl F. Falk. Last updated 2 months ago.
13.9 match 6 stars 4.56 score 2 scriptsjeksterslab
cTMed:Continuous Time Mediation
Calculates standard errors and confidence intervals for effects in continuous-time mediation models. This package extends the work of Deboeck and Preacher (2015) <doi:10.1080/10705511.2014.973960> and Ryan and Hamaker (2021) <doi:10.1007/s11336-021-09767-0> by providing methods to generate standard errors and confidence intervals for the total, direct, and indirect effects in these models.
Maintained by Ivan Jacob Agaloos Pesigan. Last updated 20 days ago.
centralitycontinuous-timedelta-methodmediationmonte-carlo-methodnetworkopenblascppopenmp
13.8 match 4.56 score 24 scriptsbioc
graph:graph: A package to handle graph data structures
A package that implements some simple graph handling capabilities.
Maintained by Bioconductor Package Maintainer. Last updated 11 days ago.
5.3 match 11.78 score 764 scripts 342 dependentssinnweja
regmed:Regularized Mediation Analysis
Mediation analysis for multiple mediators by penalized structural equation models with different types of penalties depending on whether there are multiple mediators and only one exposure and one outcome variable (using sparse group lasso) or multiple exposures, multiple mediators, and multiple outcome variables (using lasso, L1, penalties).
Maintained by Jason Sinnwell. Last updated 2 years ago.
20.0 match 2 stars 3.00 score 5 scriptsveronica0206
nlpsem:Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework
Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Jin Liu (2023) <arXiv:2302.03237v2>.
Maintained by Jin Liu. Last updated 4 months ago.
8.3 match 145 stars 6.91 score 16 scriptsrvlenth
emmeans:Estimated Marginal Means, aka Least-Squares Means
Obtain estimated marginal means (EMMs) for many linear, generalized linear, and mixed models. Compute contrasts or linear functions of EMMs, trends, and comparisons of slopes. Plots and other displays. Least-squares means are discussed, and the term "estimated marginal means" is suggested, in Searle, Speed, and Milliken (1980) Population marginal means in the linear model: An alternative to least squares means, The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
Maintained by Russell V. Lenth. Last updated 3 days ago.
2.8 match 377 stars 19.19 score 13k scripts 187 dependentstlverse
tmle3mediate:Targeted Learning for Causal Mediation Analysis
Targeted maximum likelihood (TML) estimation of population-level causal effects in mediation analysis. The causal effects are defined by joint static or stochastic interventions applied to the exposure and the mediator. Targeted doubly robust estimators are provided for the classical natural direct and indirect effects, as well as the more recently developed population intervention direct and indirect effects.
Maintained by Nima Hejazi. Last updated 4 years ago.
causal-inferencecausal-mediation-analysismachine-learningmediation-analysisstochastic-interventionstargeted-learningtreatment-effects
18.0 match 6 stars 2.98 score 16 scriptsumich-cphds
bama:High Dimensional Bayesian Mediation Analysis
Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. Bayesian Mediation Analysis (BAMA), developed by Song et al (2019) <doi:10.1111/biom.13189> and Song et al (2020) <arXiv:2009.11409>, relies on two Bayesian sparse linear mixed models to simultaneously analyze a relatively large number of mediators for a continuous exposure and outcome assuming a small number of mediators are truly active. This sparsity assumption also allows the extension of univariate mediator analysis by casting the identification of active mediators as a variable selection problem and applying Bayesian methods with continuous shrinkage priors on the effects.
Maintained by Mike Kleinsasser. Last updated 2 years ago.
11.0 match 4.80 score 42 scripts 1 dependentsjohnnyzhz
WebPower:Basic and Advanced Statistical Power Analysis
This is a collection of tools for conducting both basic and advanced statistical power analysis including correlation, proportion, t-test, one-way ANOVA, two-way ANOVA, linear regression, logistic regression, Poisson regression, mediation analysis, longitudinal data analysis, structural equation modeling and multilevel modeling. It also serves as the engine for conducting power analysis online at <https://webpower.psychstat.org>.
Maintained by Zhiyong Zhang. Last updated 6 months ago.
9.4 match 8 stars 5.52 score 128 scriptscanyi-chen
abima:Adaptive Bootstrap Inference for Mediation Analysis with Enhanced Statistical Power
Assess whether and how a specific continuous or categorical exposure affects the outcome of interest through one- or multi-dimensional mediators using an adaptive bootstrap (AB) approach. The AB method allows to make inference for composite null hypotheses of no mediation effect, providing valid type I error control and thus optimizes statistical power. For more technical details, refer to He, Song and Xu (2024) <doi:10.1093/jrsssb/qkad129>.
Maintained by Canyi Chen. Last updated 5 months ago.
enhanced-powermediation-analysistype-i-error-control
14.5 match 1 stars 3.30 score 7 scriptsaalfons
robmedExtra:Extra Functionality for (Robust) Mediation Analysis
This companion package extends the package 'robmed' (Alfons, Ates & Groenen, 2022b; <doi:10.18637/jss.v103.i13>) in various ways. Most notably, it provides a graphical user interface for the robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>) to make the method more accessible to less proficient 'R' users, as well as functions to export the results as a table in a 'Microsoft Word' or 'Microsoft Powerpoint' document, or as a 'LaTeX' table. Furthermore, the package contains a 'shiny' app to compare various bootstrap procedures for mediation analysis on simulated data.
Maintained by Andreas Alfons. Last updated 4 months ago.
17.3 match 1 stars 2.70 scoredanielmc1603
twangMediation:Twang Causal Mediation Modeling via Weighting
Provides functions for estimating natural direct and indirect effects for mediation analysis. It uses weighting where the weights are functions of estimates of the probability of exposure or treatment assignment (Hong, G (2010). <https://cepa.stanford.edu/sites/default/files/workshops/GH_JSM%20Proceedings%202010.pdf> Huber, M. (2014). <doi:10.1002/jae.2341>). Estimation of probabilities can use generalized boosting or logistic regression. Additional functions provide diagnostics of the model fit and weights. The vignette provides details and examples.
Maintained by Dan McCaffrey. Last updated 3 years ago.
23.1 match 2.00 scorechrisaberson
pwr2ppl:Power Analyses for Common Designs (Power to the People)
Statistical power analysis for designs including t-tests, correlations, multiple regression, ANOVA, mediation, and logistic regression. Functions accompany Aberson (2019) <doi:10.4324/9781315171500>.
Maintained by Chris Aberson. Last updated 3 years ago.
11.1 match 17 stars 4.16 score 17 scriptsjasonmoy28
psycModel:Integrated Toolkit for Psychological Analysis and Modeling in R
A beginner-friendly R package for modeling in psychology or related field. It allows fitting models, plotting, checking goodness of fit, and model assumption violations all in one place. It also produces beautiful and easy-to-read output.
Maintained by Jason Moy. Last updated 6 months ago.
7.9 match 4 stars 5.59 score 14 scriptsncysur
intmed:Mediation Analysis using Interventional Effects
Implementing the interventional effects for mediation analysis for up to 3 mediators. The methods used are based on VanderWeele, Vansteelandt and Robins (2014) <doi:10.1097/ede.0000000000000034>, Vansteelandt and Daniel (2017) <doi:10.1097/ede.0000000000000596> and Chan and Leung (2020; unpublished manuscript, available on request from the author of this package). Linear regression, logistic regression and Poisson regression are used for continuous, binary and count mediator/outcome variables respectively.
Maintained by Gary Chan. Last updated 5 years ago.
16.2 match 2.70 score 1 scriptsjinghuazhao
gap:Genetic Analysis Package
As first reported [Zhao, J. H. 2007. "gap: Genetic Analysis Package". J Stat Soft 23(8):1-18. <doi:10.18637/jss.v023.i08>], it is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap).
Maintained by Jing Hua Zhao. Last updated 16 days ago.
3.6 match 12 stars 11.88 score 448 scripts 16 dependentstidymodels
broom:Convert Statistical Objects into Tidy Tibbles
Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
Maintained by Simon Couch. Last updated 4 months ago.
1.9 match 1.5k stars 21.56 score 37k scripts 1.4k dependentsumich-cphds
messi:Mediation with External Summary Statistic Information
Fits the MESSI, hard constraint, and unconstrained models in Boss et al. (2023) <doi:10.48550/arXiv.2306.17347> for mediation analyses with external summary-level information on the total effect.
Maintained by Michael Kleinsasser. Last updated 2 months ago.
12.4 match 3.18 score 3 scriptspdwaggoner
purging:Simple Method for Purging Mediation Effects among Independent Variables
Simple method of purging independent variables of mediating effects. First, regress the direct variable on the indirect variable. Then, used the stored residuals as the new purged (direct) variable in the updated specification. This purging process allows for use of a new direct variable uncorrelated with the indirect variable. Please cite the method and/or package using Waggoner, Philip D. (2018) <doi:10.1177/1532673X18759644>.
Maintained by Philip D. Waggoner. Last updated 6 years ago.
11.8 match 2 stars 3.00 score 5 scriptsbelayb
BayesGmed:Bayesian Causal Mediation Analysis using 'Stan'
Performs parametric mediation analysis using the Bayesian g-formula approach for binary and continuous outcomes. The methodology is based on Comment (2018) <doi:10.5281/zenodo.1285275> and a demonstration of its application can be found at Yimer et al. (2022) <doi:10.48550/arXiv.2210.08499>.
Maintained by Belay Birlie Yimer. Last updated 11 months ago.
10.4 match 2 stars 3.30 score 3 scriptspsychbruce
bruceR:Broadly Useful Convenient and Efficient R Functions
Broadly useful convenient and efficient R functions that bring users concise and elegant R data analyses. This package includes easy-to-use functions for (1) basic R programming (e.g., set working directory to the path of currently opened file; import/export data from/to files in any format; print tables to Microsoft Word); (2) multivariate computation (e.g., compute scale sums/means/... with reverse scoring); (3) reliability analyses and factor analyses; (4) descriptive statistics and correlation analyses; (5) t-test, multi-factor analysis of variance (ANOVA), simple-effect analysis, and post-hoc multiple comparison; (6) tidy report of statistical models (to R Console and Microsoft Word); (7) mediation and moderation analyses (PROCESS); and (8) additional toolbox for statistics and graphics.
Maintained by Han-Wu-Shuang Bao. Last updated 9 months ago.
anovadata-analysisdata-sciencelinear-modelslinear-regressionmultilevel-modelsstatisticstoolbox
4.3 match 176 stars 7.87 score 316 scripts 3 dependentsmbq
vistla:Detecting Influence Paths with Information Theory
Traces information spread through interactions between features, utilising information theory measures and a higher-order generalisation of the concept of widest paths in graphs. In particular, 'vistla' can be used to better understand the results of high-throughput biomedical experiments, by organising the effects of the investigated intervention in a tree-like hierarchy from direct to indirect ones, following the plausible information relay circuits. Due to its higher-order nature, 'vistla' can handle multi-modality and assign multiple roles to a single feature.
Maintained by Miron B. Kursa. Last updated 25 days ago.
6.9 match 4.78 score 3 scriptsqingzhaoyu
mmabig:Multiple Mediation Analysis for Big Data Sets
Used for general multiple mediation analysis with big data sets.
Maintained by Qingzhao Yu. Last updated 2 years ago.
15.3 match 2.15 score 14 scriptstfletcher05
QuantPsyc:Quantitative Psychology Tools
Contains tools useful for data screening, testing moderation (Cohen et. al. 2003)<doi:10.4324/9780203774441>, mediation (MacKinnon et. al. 2002)<doi:10.1037/1082-989x.7.1.83> and estimating power (Murphy & Myors 2014)<ISBN:9781315773155>.
Maintained by Thomas D. Fletcher. Last updated 3 years ago.
6.3 match 5.12 score 578 scriptschaochengstat
mediateP:Mediation Analysis Based on the Product Method
Functions for calculating the point and interval estimates of the natural indirect effect (NIE), total effect (TE), and mediation proportion (MP), based on the product approach. We perform the methods considered in Cheng, Spiegelman, and Li (2021) Estimating the natural indirect effect and the mediation proportion via the product method.
Maintained by Chao Cheng. Last updated 3 years ago.
31.1 match 1.00 scoresfcheung
modelbpp:Model BIC Posterior Probability
Fits the neighboring models of a fitted structural equation model and assesses the model uncertainty of the fitted model based on BIC posterior probabilities, using the method presented in Wu, Cheung, and Leung (2020) <doi:10.1080/00273171.2019.1574546>.
Maintained by Shu Fai Cheung. Last updated 6 months ago.
lavaanmodel-comparisonmodel-comparison-and-selectionmodel-selectionstructural-equation-modeling
6.8 match 4.54 score 2 scriptsbioc
MultiMed:Testing multiple biological mediators simultaneously
Implements methods for testing multiple mediators
Maintained by Simina M. Boca. Last updated 5 months ago.
multiplecomparisonstatisticalmethodsoftware
7.0 match 4.30 score 8 scriptscran
rosetta:Parallel Use of Statistical Packages in Teaching
When teaching statistics, it can often be desirable to uncouple the content from specific software packages. To ease such efforts, the Rosetta Stats website (<https://rosettastats.com>) allows comparing analyses in different packages. This package is the companion to the Rosetta Stats website, aiming to provide functions that produce output that is similar to output from other statistical packages, thereby facilitating 'software-agnostic' teaching of statistics.
Maintained by Gjalt-Jorn Peters. Last updated 2 years ago.
10.4 match 2.70 scoremetinbulus
PowerUpR:Power Analysis Tools for Multilevel Randomized Experiments
Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments with continuous outcomes. Some of the functions can assist with planning multilevel randomized experiments sensetive to detect multilevel moderation (2-1-1, 2-1-2, 2-2-1, and 2-2-2 designs) and multilevel mediation (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs). See 'PowerUp!' Excel series at <https://www.causalevaluation.org/>.
Maintained by Metin Bulus. Last updated 4 years ago.
5.6 match 2 stars 4.68 score 24 scriptsnickch-k
causaldata:Example Data Sets for Causal Inference Textbooks
Example data sets to run the example problems from causal inference textbooks. Currently, contains data sets for Huntington-Klein, Nick (2021) "The Effect" <https://theeffectbook.net>, first and second edition, Cunningham, Scott (2021, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", and Hernรกn, Miguel and James Robins (2020) "Causal Inference: What If" <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.
Maintained by Nick Huntington-Klein. Last updated 4 months ago.
3.5 match 136 stars 7.43 score 144 scripts 1 dependentsr-causal
quartets:Datasets to Help Teach Statistics
In the spirit of Anscombe's quartet, this package includes datasets that demonstrate the importance of visualizing your data, the importance of not relying on statistical summary measures alone, and why additional assumptions about the data generating mechanism are needed when estimating causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>, D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.1080/26939169.2023.2276446>, "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.
Maintained by Lucy DAgostino McGowan. Last updated 1 years ago.
4.5 match 42 stars 5.75 score 27 scriptsbbolker
broom.mixed:Tidying Methods for Mixed Models
Convert fitted objects from various R mixed-model packages into tidy data frames along the lines of the 'broom' package. The package provides three S3 generics for each model: tidy(), which summarizes a model's statistical findings such as coefficients of a regression; augment(), which adds columns to the original data such as predictions, residuals and cluster assignments; and glance(), which provides a one-row summary of model-level statistics.
Maintained by Ben Bolker. Last updated 3 months ago.
1.7 match 231 stars 15.22 score 4.0k scripts 37 dependentsqingzhaoyu
BayesianMediationA:Bayesian Mediation Analysis
We perform general mediation analysis in the Bayesian setting using the methods described in Yu and Li (2022, ISBN:9780367365479). With the package, the mediation analysis can be performed on different types of outcomes (e.g., continuous, binary, categorical, or time-to-event), with default or user-defined priors and predictive models. The Bayesian estimates and credible sets of mediation effects are reported as analytic results.
Maintained by Qingzhao Yu. Last updated 2 years ago.
12.4 match 2.00 score 2 scriptsavi-kenny
vaccine:Statistical Tools for Immune Correlates Analysis of Vaccine Clinical Trial Data
Various semiparametric and nonparametric statistical tools for immune correlates analysis of vaccine clinical trial data. This includes calculation of summary statistics and estimation of risk, vaccine efficacy, controlled effects (controlled risk and controlled vaccine efficacy), and mediation effects (natural direct effect, natural indirect effect, proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022) <doi:10.1093/biostatistics/kxac024> and Fay MP and Follmann DA (2023) <doi:10.48550/arXiv.2208.06465>.
Maintained by Avi Kenny. Last updated 23 days ago.
4.5 match 4 stars 5.42 score 11 scriptsr-causal
ggdag:Analyze and Create Elegant Directed Acyclic Graphs
Tidy, analyze, and plot directed acyclic graphs (DAGs). 'ggdag' is built on top of 'dagitty', an R package that uses the 'DAGitty' web tool (<https://dagitty.net/>) for creating and analyzing DAGs. 'ggdag' makes it easy to tidy and plot 'dagitty' objects using 'ggplot2' and 'ggraph', as well as common analytic and graphical functions, such as determining adjustment sets and node relationships.
Maintained by Malcolm Barrett. Last updated 8 months ago.
causal-inferencedagggplot-extension
2.0 match 443 stars 11.78 score 1.8k scripts 5 dependentsbioc
CHRONOS:CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis
A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs.
Maintained by Panos Balomenos. Last updated 5 months ago.
systemsbiologygraphandnetworkpathwayskeggopenjdk
6.0 match 3.86 score 12 scriptsropensci
rgbif:Interface to the Global Biodiversity Information Facility API
A programmatic interface to the Web Service methods provided by the Global Biodiversity Information Facility (GBIF; <https://www.gbif.org/developer/summary>). GBIF is a database of species occurrence records from sources all over the globe. rgbif includes functions for searching for taxonomic names, retrieving information on data providers, getting species occurrence records, getting counts of occurrence records, and using the GBIF tile map service to make rasters summarizing huge amounts of data.
Maintained by John Waller. Last updated 3 days ago.
gbifspecimensapiweb-servicesoccurrencesspeciestaxonomybiodiversitydatalifewatchoscibiospocc
1.8 match 161 stars 13.26 score 2.1k scripts 20 dependentsrempsyc
lavaanExtra:Convenience Functions for Package 'lavaan'
Affords an alternative, vector-based syntax to 'lavaan', as well as other convenience functions such as naming paths and defining indirect links automatically, in addition to convenience formatting optimized for a publication and script sharing workflow.
Maintained by Rรฉmi Thรฉriault. Last updated 9 months ago.
convenience-functionslavaanpsychologystatisticsstructural-equation-modeling
3.3 match 18 stars 6.95 score 33 scriptsbioc
mia:Microbiome analysis
mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization.
Maintained by Tuomas Borman. Last updated 2 days ago.
microbiomesoftwaredataimportanalysisbioconductor
2.0 match 52 stars 11.50 score 316 scripts 5 dependentsgitlzg
MarZIC:Marginal Mediation Effects with Zero-Inflated Compositional Mediator
A way to estimate and test marginal mediation effects for zero-inflated compositional mediators. Estimates of Natural Indirect Effect (NIE), Natural Direct Effect (NDE) of each taxon, as well as their standard errors and confident intervals, were provided as outputs. Zeros will not be imputed during analysis. See Wu et al. (2022) <doi:10.3390/genes13061049>.
Maintained by Zhigang Li. Last updated 1 years ago.
8.5 match 2.70 score 7 scriptsmattblackwell
DirectEffects:Estimating Controlled Direct Effects for Explaining Causal Findings
A set of functions to estimate the controlled direct effect of treatment fixing a potential mediator to a specific value. Implements the sequential g-estimation estimator described in Vansteelandt (2009) <doi:10.1097/EDE.0b013e3181b6f4c9> and Acharya, Blackwell, and Sen (2016) <doi:10.1017/S0003055416000216> and the telescope matching estimator described in Blackwell and Strezhnev (2020) <doi:10.1111/rssa.12759>.
Maintained by Matthew Blackwell. Last updated 20 days ago.
3.7 match 18 stars 6.09 score 17 scriptscwatson
brainGraph:Graph Theory Analysis of Brain MRI Data
A set of tools for performing graph theory analysis of brain MRI data. It works with data from a Freesurfer analysis (cortical thickness, volumes, local gyrification index, surface area), diffusion tensor tractography data (e.g., from FSL) and resting-state fMRI data (e.g., from DPABI). It contains a graphical user interface for graph visualization and data exploration, along with several functions for generating useful figures.
Maintained by Christopher G. Watson. Last updated 1 years ago.
brain-connectivitybrain-imagingcomplex-networksconnectomeconnectomicsfmrigraph-theorymrinetwork-analysisneuroimagingneurosciencestatisticstractography
2.9 match 188 stars 7.86 score 107 scripts 3 dependentsadwolfer
santaR:Short Asynchronous Time-Series Analysis
A graphical and automated pipeline for the analysis of short time-series in R ('santaR'). This approach is designed to accommodate asynchronous time sampling (i.e. different time points for different individuals), inter-individual variability, noisy measurements and large numbers of variables. Based on a smoothing splines functional model, 'santaR' is able to detect variables highlighting significantly different temporal trajectories between study groups. Designed initially for metabolic phenotyping, 'santaR' is also suited for other Systems Biology disciplines. Command line and graphical analysis (via a 'shiny' application) enable fast and parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting options for non-specialist users.
Maintained by Arnaud Wolfer. Last updated 1 years ago.
3.5 match 11 stars 6.44 score 63 scriptsr-forge
WRS2:A Collection of Robust Statistical Methods
A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.
Maintained by Patrick Mair. Last updated 3 months ago.
2.5 match 8.96 score 402 scripts 7 dependentsvankesteren
cmfilter:Coordinate-Wise Mediation Filter
Functions to discover, plot, and select multiple mediators from an x -> M -> y linear system. This exploratory mediation analysis is performed using the Coordinate-wise Mediation Filter as introduced by Van Kesteren and Oberski (2019) <doi: 10.1080/10705511.2019.1588124>.
Maintained by Erik-Jan van Kesteren. Last updated 2 years ago.
9.6 match 4 stars 2.30 score 4 scriptsintegrated-inferences
CausalQueries:Make, Update, and Query Binary Causal Models
Users can declare causal models over binary nodes, update beliefs about causal types given data, and calculate arbitrary queries. Updating is implemented in 'stan'. See Humphreys and Jacobs, 2023, Integrated Inferences (<DOI: 10.1017/9781316718636>) and Pearl, 2009 Causality (<DOI:10.1017/CBO9780511803161>).
Maintained by Till Tietz. Last updated 23 days ago.
bayescausaldagsmixedmethodsstancpp
2.5 match 27 stars 9.03 score 54 scriptsxu-qin
MultisiteMediation:Causal Mediation Analysis in Multisite Trials
We implement multisite causal mediation analysis using the methods proposed by Qin and Hong (in press). It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions.
Maintained by Xu Qin. Last updated 8 years ago.
7.5 match 2.70 score 6 scriptsrevelle
psychTools:Tools to Accompany the 'psych' Package for Psychological Research
Support functions, data sets, and vignettes for the 'psych' package. Contains several of the biggest data sets for the 'psych' package as well as four vignettes. A few helper functions for file manipulation are included as well. For more information, see the <https://personality-project.org/r/> web page.
Maintained by William Revelle. Last updated 12 months ago.
3.4 match 5.89 score 178 scripts 5 dependentsmetrumresearchgroup
mrgsolve:Simulate from ODE-Based Models
Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.
Maintained by Kyle T Baron. Last updated 1 months ago.
1.8 match 138 stars 10.90 score 1.2k scripts 3 dependentsbioc
miaViz:Microbiome Analysis Plotting and Visualization
The miaViz package implements functions to visualize TreeSummarizedExperiment objects especially in the context of microbiome analysis. Part of the mia family of R/Bioconductor packages.
Maintained by Tuomas Borman. Last updated 2 days ago.
microbiomesoftwarevisualizationbioconductormicrobiome-analysisplotting
2.3 match 10 stars 8.65 score 81 scripts 1 dependentssfcheung
semhelpinghands:Helper Functions for Structural Equation Modeling
An assortment of helper functions for doing structural equation modeling, mainly by 'lavaan' for now. Most of them are time-saving functions for common tasks in doing structural equation modeling and reading the output. This package is not for functions that implement advanced statistical procedures. It is a light-weight package for simple functions that do simple tasks conveniently, with as few dependencies as possible.
Maintained by Shu Fai Cheung. Last updated 5 months ago.
bootstrappinglavaanstructural-equation-modeling
3.8 match 5.13 score 27 scriptsjohnnyzhz
bmem:Mediation Analysis with Missing Data Using Bootstrap
Four methods for mediation analysis with missing data: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included. Bootstrap confidence intervals for mediation effects are obtained. The robust method is also implemented for TS-ML. Since version 1.4, bmem adds the capability to conduct power analysis for mediation models. Details about the methods used can be found in these articles. Zhang and Wang (2003) <doi:10.1007/s11336-012-9301-5>. Zhang (2014) <doi:10.3758/s13428-013-0424-0>.
Maintained by Zhiyong Zhang. Last updated 2 years ago.
14.3 match 2 stars 1.30 score 6 scriptssfcheung
betaselectr:Betas-Select in Structural Equation Models and Linear Models
It computes betas-select, coefficients after standardization in structural equation models and regression models, standardizing only selected variables. Supports models with moderation, with product terms formed after standardization. It also offers confidence intervals that account for standardization, including bootstrap confidence intervals as proposed by Cheung et al. (2022) <doi:10.1037/hea0001188>.
Maintained by Shu Fai Cheung. Last updated 4 months ago.
bootstrappingconfidence-intervalsgeneralized-linear-modelslavaanlogistic-regressionregressionsemstandardizationstructural-equation-modeling
3.8 match 1 stars 4.95 score 8 scriptsumich-cphds
medScan:Large Scale Single Mediator Hypothesis Testing
A collection of methods for large scale single mediator hypothesis testing. The six included methods for testing the mediation effect are Sobel's test, Max P test, joint significance test under the composite null hypothesis, high dimensional mediation testing, divide-aggregate composite null test, and Sobel's test under the composite null hypothesis. Du et al (2023) <doi:10.1002/gepi.22510>.
Maintained by Michael Kleinsasser. Last updated 1 years ago.
5.6 match 3.00 score 5 scriptshugometric
causalweight:Estimation Methods for Causal Inference Based on Inverse Probability Weighting
Various estimators of causal effects based on inverse probability weighting, doubly robust estimation, and double machine learning. Specifically, the package includes methods for estimating average treatment effects, direct and indirect effects in causal mediation analysis, and dynamic treatment effects. The models refer to studies of Froelich (2007) <doi:10.1016/j.jeconom.2006.06.004>, Huber (2012) <doi:10.3102/1076998611411917>, Huber (2014) <doi:10.1080/07474938.2013.806197>, Huber (2014) <doi:10.1002/jae.2341>, Froelich and Huber (2017) <doi:10.1111/rssb.12232>, Hsu, Huber, Lee, and Lettry (2020) <doi:10.1002/jae.2765>, and others.
Maintained by Hugo Bodory. Last updated 8 months ago.
10.1 match 2 stars 1.64 score 22 scriptsuscbiostats
hJAM:Hierarchical Joint Analysis of Marginal Summary Statistics
Provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." <bioRxiv><doi:10.1101/2020.02.03.924241>.
Maintained by Lai Jiang. Last updated 1 years ago.
2.9 match 9 stars 5.13 score 5 scriptsrjacobucci
regsem:Regularized Structural Equation Modeling
Uses both ridge and lasso penalties (and extensions) to penalize specific parameters in structural equation models. The package offers additional cost functions, cross validation, and other extensions beyond traditional structural equation models. Also contains a function to perform exploratory mediation (XMed).
Maintained by Ross Jacobucci. Last updated 2 years ago.
2.2 match 14 stars 6.63 score 77 scriptsbioc
cleanUpdTSeq:cleanUpdTSeq cleans up artifacts from polyadenylation sites from oligo(dT)-mediated 3' end RNA sequending data
This package implements a Naive Bayes classifier for accurately differentiating true polyadenylation sites (pA sites) from oligo(dT)-mediated 3' end sequencing such as PAS-Seq, PolyA-Seq and RNA-Seq by filtering out false polyadenylation sites, mainly due to oligo(dT)-mediated internal priming during reverse transcription. The classifer is highly accurate and outperforms other heuristic methods.
Maintained by Jianhong Ou. Last updated 2 months ago.
sequencing3 end sequencingpolyadenylation siteinternal priming
3.5 match 4.26 score 8 scripts 1 dependentsdaniel258
GEEmediate:Mediation Analysis for Generalized Linear Models Using the Difference Method
Causal mediation analysis for a single exposure/treatment and a single mediator, both allowed to be either continuous or binary. The package implements the difference method and provides point and interval estimates as well as testing for the natural direct and indirect effects and the mediation proportion. Nevo, Xiao and Spiegelman (2017) <doi:10.1515/ijb-2017-0006>.
Maintained by Daniel Nevo. Last updated 2 years ago.
5.4 match 2.70 score 1 scriptscran
MRmediation:A Causal Mediation Method with Methylated Region (MR) as the Mediator
A causal mediation approach under the counterfactual framework to test the significance of total, direct and indirect effects. In this approach, a group of methylated sites from a predefined region are utilized as the mediator, and the functional transformation is used to reduce the possible high dimension in the region-based methylated sites and account for their location information.
Maintained by Qi Yan. Last updated 4 years ago.
14.5 match 1.00 scoreyouyifong
kyotil:Utility Functions for Statistical Analysis Report Generation and Monte Carlo Studies
Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.
Maintained by Youyi Fong. Last updated 8 days ago.
1.8 match 7.87 score 236 scripts 7 dependentscran
iMediate:Likelihood Methods for Mediation Analysis
Implements likelihood based methods for mediation analysis.
Maintained by Kai Wang. Last updated 6 years ago.
13.5 match 1.00 score 9 scriptsoeysan
bfw:Bayesian Framework for Computational Modeling
Derived from the work of Kruschke (2015, <ISBN:9780124058880>), the present package aims to provide a framework for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer, 2003, <https://mcmc-jags.sourceforge.io>). The initial version includes several modules for conducting Bayesian equivalents of chi-squared tests, analysis of variance (ANOVA), multiple (hierarchical) regression, softmax regression, and for fitting data (e.g., structural equation modeling).
Maintained by รystein Olav Skaar. Last updated 3 years ago.
bayesian-data-analysisbayesian-statisticsjagsmcmcpsychological-sciencecpp
2.3 match 10 stars 5.89 score 31 scriptsbioc
epistasisGA:An R package to identify multi-snp effects in nuclear family studies using the GADGETS method
This package runs the GADGETS method to identify epistatic effects in nuclear family studies. It also provides functions for permutation-based inference and graphical visualization of the results.
Maintained by Michael Nodzenski. Last updated 5 months ago.
geneticssnpgeneticvariabilityopenblascpp
2.7 match 1 stars 4.48 score 5 scriptsdeclaredesign
DesignLibrary:Library of Research Designs
A simple interface to build designs using the package 'DeclareDesign'. In one line of code, users can specify the parameters of individual designs and diagnose their properties. The designers can also be used to compare performance of a given design across a range of combinations of parameters, such as effect size, sample size, and assignment probabilities.
Maintained by Jasper Cooper. Last updated 1 months ago.
1.9 match 30 stars 6.30 score 144 scriptsxiangzhou09
rbw:Residual Balancing Weights for Marginal Structural Models
Residual balancing is a robust method of constructing weights for marginal structural models, which can be used to estimate (a) the average treatment effect in a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020 <doi:10.1017/pan.2020.2>). This package provides three functions, rbwPoint(), rbwMed(), and rbwPanel(), that produce residual balancing weights for estimating (a), (b), (c), respectively.
Maintained by Xiang Zhou. Last updated 3 years ago.
2.5 match 9 stars 4.65 score 5 scriptscran
bda:Binned Data Analysis
Algorithms developed for binned data analysis, gene expression data analysis and measurement error models for ordinal data analysis.
Maintained by Bin Wang. Last updated 7 months ago.
4.3 match 2.61 score 82 scriptsjinkim3
kim:A Toolkit for Behavioral Scientists
A collection of functions for analyzing data typically collected or used by behavioral scientists. Examples of the functions include a function that compares groups in a factorial experimental design, a function that conducts two-way analysis of variance (ANOVA), and a function that cleans a data set generated by Qualtrics surveys. Some of the functions will require installing additional package(s). Such packages and other references are cited within the section describing the relevant functions. Many functions in this package rely heavily on these two popular R packages: Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>. Wickham et al. (2021) <https://CRAN.R-project.org/package=ggplot2>.
Maintained by Jin Kim. Last updated 19 days ago.
2.3 match 7 stars 4.66 score 3 scriptsjohnnyzhz
bmemLavaan:Mediation Analysis with Missing Data and Non-Normal Data
Methods for mediation analysis with missing data and non-normal data are implemented. For missing data, four methods are available: Listwise deletion, Pairwise deletion, Multiple imputation, and Two Stage Maximum Likelihood algorithm. For MI and TS-ML, auxiliary variables can be included to handle missing data. For handling non-normal data, bootstrap and two-stage robust methods can be used. Technical details of the methods can be found in Zhang and Wang (2013, <doi:10.1007/s11336-012-9301-5>), Zhang (2014, <doi:10.3758/s13428-013-0424-0>), and Yuan and Zhang (2012, <doi:10.1007/s11336-012-9282-4>).
Maintained by Zhiyong Zhang. Last updated 3 years ago.
5.2 match 2.00 scorekcgchan
MED:Mediation by Tilted Balancing
Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) <arXiv:1601.03501>.
Maintained by Gary Chan. Last updated 7 years ago.
5.0 match 2.00 score 6 scriptsbioc
ontoProc:processing of ontologies of anatomy, cell lines, and so on
Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies.
Maintained by Vincent Carey. Last updated 3 days ago.
infrastructuregobioinformaticsgenomicsontology
1.6 match 3 stars 6.37 score 75 scripts 2 dependentsbioc
iSEEtree:Interactive visualisation for microbiome data
iSEEtree is an extension of iSEE for the TreeSummarizedExperiment. It leverages the functionality from the miaViz package for microbiome data visualisation to create panels that are specific for TreeSummarizedExperiment objects. Not surprisingly, it also depends on the generic panels from iSEE.
Maintained by Giulio Benedetti. Last updated 6 days ago.
microbiomesoftwarevisualizationguishinyappsdataimportshiny-appsvisualisation
1.5 match 3 stars 6.26 score 5 scriptscran
callback:Computes Statistics from Discrimination Experimental Data
In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. The number of non negative answers are first examined in details looking for outcome differences. Then various statistics are computed. This package can also be used for analyzing the results from random experiments.
Maintained by Emmanuel Duguet. Last updated 13 days ago.
3.3 match 2.78 scorecran
MARVEL:Revealing Splicing Dynamics at Single-Cell Resolution
Alternative splicing represents an additional and underappreciated layer of complexity underlying gene expression profiles. Nevertheless, there remains hitherto a paucity of software to investigate splicing dynamics at single-cell resolution. 'MARVEL' enables splicing analysis of single-cell RNA-sequencing data generated from plate- and droplet-based library preparation methods.
Maintained by Sean Wen. Last updated 2 years ago.
3.3 match 2.71 score 51 scriptsbioc
GRaNIE:GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data
Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach.
Maintained by Christian Arnold. Last updated 5 months ago.
softwaregeneexpressiongeneregulationnetworkinferencegenesetenrichmentbiomedicalinformaticsgeneticstranscriptomicsatacseqrnaseqgraphandnetworkregressiontranscriptionchipseq
1.6 match 5.40 score 24 scriptssyeonkang
causal.decomp:Causal Decomposition Analysis
We implement causal decomposition analysis using the methods proposed by Park, Lee, and Qin (2020) and Park, Kang, and Lee (2021+) <arXiv:2109.06940>. This package allows researchers to use the multiple-mediator-imputation, single-mediator-imputation, and product-of-coefficients regression methods to estimate the initial disparity, disparity reduction, and disparity remaining. It also allows to make the inference conditional on baseline covariates. We also implement sensitivity analysis for the causal decomposition analysis using R-squared values as sensitivity parameters (Park, Kang, Lee, and Ma, 2023).
Maintained by Suyeon Kang. Last updated 2 years ago.
4.4 match 2.00 score 4 scriptsrandel
GMAC:Genomic Mediation Analysis with Adaptive Confounding Adjustment
Performs genomic mediation analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/078683>. It implements large scale mediation analysis and adaptively selects potential confounding variables to adjust for each mediation test from a pool of candidate confounders. The package is tailored for but not limited to genomic mediation analysis (e.g., cis-gene mediating trans-gene regulation pattern where an eQTL, its cis-linking gene transcript, and its trans-gene transcript play the roles as treatment, mediator and the outcome, respectively), restricting to scenarios with the presence of cis-association (i.e., treatment-mediator association) and random eQTL (i.e., treatment).
Maintained by Jiebiao Wang. Last updated 3 years ago.
5.8 match 1.48 score 3 scriptsjaydevine
pheble:Classifying High-Dimensional Phenotypes with Ensemble Learning
A system for binary and multi-class classification of high-dimensional phenotypic data using ensemble learning. By combining predictions from different classification models, this package attempts to improve performance over individual learners. The pre-processing, training, validation, and testing are performed end-to-end to minimize user input and simplify the process of classification.
Maintained by Jay Devine. Last updated 2 years ago.
3.1 match 2.70 scoresem-in-r
seminr:Building and Estimating Structural Equation Models
A powerful, easy to syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis. Methods described in Ray, Danks, and Valdez (2021).
Maintained by Nicholas Patrick Danks. Last updated 3 years ago.
common-factorscompositesconstructpls-models
1.1 match 62 stars 7.46 score 284 scriptsjohn-harrold
ubiquity:PKPD, PBPK, and Systems Pharmacology Modeling Tools
Complete work flow for the analysis of pharmacokinetic pharmacodynamic (PKPD), physiologically-based pharmacokinetic (PBPK) and systems pharmacology models including: creation of ordinary differential equation-based models, pooled parameter estimation, individual/population based simulations, rule-based simulations for clinical trial design and modeling assays, deployment with a customizable 'Shiny' app, and non-compartmental analysis. System-specific analysis templates can be generated and each element includes integrated reporting with 'PowerPoint' and 'Word'.
Maintained by John Harrold. Last updated 17 days ago.
1.2 match 13 stars 7.14 score 33 scriptsnie-xiuquan
EMAS:Epigenome-Wide Mediation Analysis Study
DNA methylation is essential for human, and environment can change the DNA methylation and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide. For more information on the methods we used, please see the following references: Tingley, D. (2014) <doi:10.18637/jss.v059.i05>, Turner, S. D. (2018) <doi:10.21105/joss.00731>, Rosseel, D. (2012) <doi:10.18637/jss.v048.i02>.
Maintained by Xiuquan Nie. Last updated 3 years ago.
8.3 match 1.00 scorelaylaparast
freebird:Estimation and Inference for High Dimensional Mediation and Surrogate Analysis
Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) <doi:10.1002/sim.9352> and Zhou et al (2020) <doi:10.1093/biomet/asaa016>. This package relies on the optimization software 'MOSEK', <https://www.mosek.com>.
Maintained by Layla Parast. Last updated 2 years ago.
5.4 match 1.52 score 11 scripts 1 dependentscran
ccmm:Compositional Mediation Model
Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision).
Maintained by Michael B. Sohn. Last updated 8 years ago.
8.1 match 1.00 score 9 scriptscran
macc:Mediation Analysis of Causality under Confounding
Performs causal mediation analysis under confounding or correlated errors. This package includes a single level mediation model, a two-level mediation model, and a three-level mediation model for data with hierarchical structures. Under the two/three-level mediation model, the correlation parameter is identifiable and is estimated based on a hierarchical-likelihood, a marginal-likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2014), Estimating Mediation Effects under Correlated Errors with an Application to fMRI, <arXiv:1410.7217> for details.
Maintained by Yi Zhao. Last updated 8 years ago.
7.9 match 1.00 scorecribbie
negligible:A Collection of Functions for Negligible Effect/Equivalence Testing
Researchers often want to evaluate whether there is a negligible relationship among variables. The 'negligible' package provides functions that are useful for conducting negligible effect testing (also called equivalence testing). For example, there are functions for evaluating the equivalence of means or the presence of a negligible association (correlation or regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) <doi:10.20982/tqmp.16.4.p424>. Beribisky, N., Davidson, H., Cribbie, R. A. (2019) <doi:10.7717/peerj.6853>. Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) <doi:10.20982/tqmp.14.3.p167>. Mara, C. & Cribbie, R. A. (2017) <doi:10.1080/00220973.2017.1301356>. Counsell, A. & Cribbie, R. A. (2015) <doi:10.1111/bmsp.12045>. van Wieringen, K. & Cribbie, R. A. (2014) <doi:10.1111/bmsp.12015>. Goertzen, J. R. & Cribbie, R. A. (2010) <doi:10.1348/000711009x475853>. Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) <doi:10.1002/jclp.10217>.
Maintained by Robert Cribbie. Last updated 4 months ago.
equivalence-testingnegligible-effect-statistical-testingnegligible-effect-testingstatistics
1.9 match 4 stars 4.00 score 10 scriptsqingzhaoyu
hdbma:Bayesian Mediation Analysis with High-Dimensional Data
Mediation analysis is used to identify and quantify intermediate effects from factors that intervene the observed relationship between an exposure/predicting variable and an outcome. We use a Bayesian adaptive lasso method to take care of the hierarchical structures and high dimensional exposures or mediators.
Maintained by Qingzhao Yu. Last updated 1 years ago.
7.3 match 1.00 scorecran
multilevel:Multilevel Functions
Tools used by organizational researchers for the analysis of multilevel data. Includes four broad sets of tools. First, functions for estimating within-group agreement and reliability indices. Second, functions for manipulating multilevel and longitudinal (panel) data. Third, simulations for estimating power and generating multilevel data. Fourth, miscellaneous functions for estimating reliability and performing simple calculations and data transformations.
Maintained by Paul Bliese. Last updated 3 years ago.
1.8 match 3.79 score 4 dependentsjohnfergusonnuig
graphPAF:Estimating and Displaying Population Attributable Fractions
Estimation and display of various types of population attributable fraction and impact fractions. As well as the usual calculations of attributable fractions and impact fractions, functions are provided for attributable fraction nomograms and fan plots, continuous exposures, for pathway specific population attributable fractions, and for joint, average and sequential population attributable fractions.
Maintained by John Ferguson. Last updated 7 months ago.
1.8 match 3 stars 3.78 score 6 scriptsbuybnb
CMMs:Compositional Mediation Model
A compositional mediation model for continuous outcome and binary outcomes to deal with mediators that are compositional data. Lin, Ziqiang et al. (2022) <doi:10.1016/j.jad.2021.12.019>.
Maintained by Ziqiang Lin. Last updated 2 years ago.
6.1 match 1.00 scoretdjorgensen
simsem:SIMulated Structural Equation Modeling
Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
Maintained by Terrence D. Jorgensen. Last updated 4 years ago.
1.8 match 3.40 score 276 scriptssduxbury
ergMargins:Process Analysis for Exponential Random Graph Models
Calculates marginal effects and conducts process analysis in exponential family random graph models (ERGM). Includes functions to conduct mediation and moderation analyses and to diagnose multicollinearity. URL: <https://github.com/sduxbury/ergMargins>. BugReports: <https://github.com/sduxbury/ergMargins/issues>. Duxbury, Scott W (2021) <doi:10.1177/0049124120986178>. Long, J. Scott, and Sarah Mustillo (2018) <doi:10.1177/0049124118799374>. Mize, Trenton D. (2019) <doi:10.15195/v6.a4>. Karlson, Kristian Bernt, Anders Holm, and Richard Breen (2012) <doi:10.1177/0081175012444861>. Duxbury, Scott W (2018) <doi:10.1177/0049124118782543>. Duxbury, Scott W, Jenna Wertsching (2023) <doi:10.1016/j.socnet.2023.02.003>. Huang, Peng, Carter Butts (2023) <doi:10.1016/j.socnet.2023.07.001>.
Maintained by Scott Duxbury. Last updated 10 months ago.
3.9 match 1.48 score 3 scripts 1 dependentsbioc
DeepTarget:Deep characterization of cancer drugs
This package predicts a drugโs primary target(s) or secondary target(s) by integrating large-scale genetic and drug screens from the Cancer Dependency Map project run by the Broad Institute. It further investigates whether the drug specifically targets the wild-type or mutated target forms. To show how to use this package in practice, we provided sample data along with step-by-step example.
Maintained by Trinh Nguyen. Last updated 5 months ago.
genetargetgenepredictionpathwaysgeneexpressionrnaseqimmunooncologydifferentialexpressiongenesetenrichmentreportwritingcrispr
1.1 match 4.54 score 1 scriptsargeorgeson
phantSEM:Create Phantom Variables in Structural Equation Models for Sensitivity Analyses
Create phantom variables, which are variables that were not observed, for the purpose of sensitivity analyses for structural equation models. The package makes it easier for a user to test different combinations of covariances between the phantom variable(s) and observed variables. The package may be used to assess a model's or effect's sensitivity to temporal bias (e.g., if cross-sectional data were collected) or confounding bias.
Maintained by Alexis Georgeson. Last updated 5 months ago.
1.3 match 3.70 score 7 scriptssduxbury
netmediate:Micro-Macro Analysis for Social Networks
Estimates micro effects on macro structures (MEMS) and average micro mediated effects (AMME). URL: <https://github.com/sduxbury/netmediate>. BugReports: <https://github.com/sduxbury/netmediate/issues>. Robins, Garry, Phillipa Pattison, and Jodie Woolcock (2005) <doi:10.1086/427322>. Snijders, Tom A. B., and Christian E. G. Steglich (2015) <doi:10.1177/0049124113494573>. Imai, Kosuke, Luke Keele, and Dustin Tingley (2010) <doi:10.1037/a0020761>. Duxbury, Scott (2023) <doi:10.1177/00811750231209040>. Duxbury, Scott (2024) <doi:10.1177/00811750231220950>.
Maintained by Scott Duxbury. Last updated 9 months ago.
2.2 match 2.00 scoretacazares
SeedMatchR:Find Matches to Canonical SiRNA Seeds in Genomic Features
On-target gene knockdown using siRNA ideally results from binding fully complementary regions in mRNA transcripts to induce cleavage. Off-target siRNA gene knockdown can occur through several modes, one being a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated off-target effects occur when the ~8 nucleotides at the 5โ end of the guide strand, called a seed region, bind the 3โ untranslated regions of mRNA, causing reduced translation. Experiments using siRNA knockdown paired with RNA-seq can be used to detect siRNA sequences with potential off-target effects driven by the seed region. 'SeedMatchR' provides tools for exploring and detecting potential seed-mediated off-target effects of siRNA in RNA-seq experiments. 'SeedMatchR' is designed to extend current differential expression analysis tools, such as 'DESeq2', by annotating results with predicted seed matches. Using publicly available data, we demonstrate the ability of 'SeedMatchR' to detect cumulative changes in differential gene expression attributed to siRNA seed regions.
Maintained by Tareian Cazares. Last updated 1 years ago.
deseq2-analysismirnarna-seqsirnatranscriptomics
0.9 match 7 stars 4.54 score 7 scriptsuscbiostats
cit:Causal Inference Test
A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. Described in Millstein, Chen, and Breton (2016), <DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.
Maintained by Joshua Millstein. Last updated 9 months ago.
1.0 match 2 stars 3.81 score 32 scriptssynergisticcauselearning
CoOL:Causes of Outcome Learning
Implementing the computational phase of the Causes of Outcome Learning approach as described in Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <doi:10.1093/ije/dyac078>. The optional 'ggtree' package can be obtained through Bioconductor.
Maintained by Andreas Rieckmann. Last updated 3 years ago.
2.3 match 1.70 score 6 scriptscran
R4HCR:R for Health Care Research
A collection of datasets that accompany the forthcoming book "R for Health Care Research".
Maintained by Jason L. Oke. Last updated 6 months ago.
3.5 match 1.00 scoresteveculpepper
pathmodelfit:Path Component Fit Indices for Latent Structural Equation Models
Functions for computing fit indices for evaluating the path component of latent variable structural equation models. Available fit indices include RMSEA-P and NSCI-P originally presented and evaluated by Williams and O'Boyle (2011) <doi:10.1177/1094428110391472> and demonstrated by O'Boyle and Williams (2011) <doi:10.1037/a0020539> and Williams, O'Boyle, & Yu (2020) <doi:10.1177/1094428117736137>. Also included are fit indices described by Hancock and Mueller (2011) <doi:10.1177/0013164410384856>.
Maintained by Steven Andrew Culpepper. Last updated 5 years ago.
3.4 match 1.00 scoremattansb
MSBMisc:Some functions I wrote that I find useful
misc. functions.
Maintained by Mattan S. Ben-Shachar. Last updated 2 years ago.
1.9 match 1 stars 1.70 score 2 scriptscran
LoopRig:Integration and Analysis of Chromatin Loop Data
Common coordinate-based workflows involving processed chromatin loop and genomic element data are considered and packaged into appropriate customizable functions. Includes methods for linking element sets via chromatin loops and creating consensus loop datasets.
Maintained by Hassaan Maan. Last updated 5 years ago.
1.2 match 2.70 scorepersonalizedtransplantcare
banffIT:Automatize Diagnosis Standardized Assignation Using the Banff Classification
Assigns standardized diagnoses using the Banff Classification (Category 1 to 6 diagnoses, including Acute and Chronic active T-cell mediated rejection as well as Active, Chronic active, and Chronic antibody mediated rejection). The main function considers a minimal dataset containing biopsies information in a specific format (described by a data dictionary), verifies its content and format (based on the data dictionary), assigns diagnoses, and creates a summary report. The package is developed on the reference guide to the Banff classification of renal allograft pathology Roufosse C, Simmonds N, Clahsen-van Groningen M, et al. A (2018) <doi:10.1097/TP.0000000000002366>. The full description of the Banff classification is available at <https://banfffoundation.org/>.
Maintained by Guillaume Fabre. Last updated 10 months ago.
0.8 match 4.00 scoremdtrinh
paths:An Imputation Approach to Estimating Path-Specific Causal Effects
In causal mediation analysis with multiple causally ordered mediators, a set of path-specific effects are identified under standard ignorability assumptions. This package implements an imputation approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis (Zhou and Yamamoto <doi:10.31235/osf.io/2rx6p>). It contains two main functions: paths() for estimating path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified using the nonparametric bootstrap.
Maintained by Minh Trinh. Last updated 4 years ago.
0.8 match 3.71 score 102 scriptsbioc
SPONGE:Sparse Partial Correlations On Gene Expression
This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.
Maintained by Markus List. Last updated 5 months ago.
geneexpressiontranscriptiongeneregulationnetworkinferencetranscriptomicssystemsbiologyregressionrandomforestmachinelearning
0.5 match 5.36 score 38 scripts 1 dependentsmaartenbijlsma
cfdecomp:Counterfactual Decomposition: MC Integration of the G-Formula
Provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.
Maintained by Maarten Jacob Bijlsma. Last updated 4 years ago.
0.9 match 1 stars 2.70 score 5 scriptsanitalindmark
sensmediation:Parametric Estimation and Sensitivity Analysis of Direct and Indirect Effects
We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620> and Lindmark (2022) <doi:10.1007/s10260-021-00611-4>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.
Maintained by Anita Lindmark. Last updated 6 months ago.
2.4 match 1.00 score 4 scriptscran
rSPARCS:Sites, Population, and Records Cleaning Skills
Data cleaning including 1) generating datasets for time-series and case-crossover analyses based on raw hospital records, 2) linking individuals to an areal map, 3) picking out cases living within a buffer of certain size surrounding a site, etc. For more information, please refer to Zhang W,etc. (2018) <doi:10.1016/j.envpol.2018.08.030>.
Maintained by Wangjian Zhang. Last updated 1 years ago.
2.3 match 1.00 scoreposit-dev
connectcreds:Manage 'OAuth' Credentials from 'Posit Connect'
A toolkit for making use of credentials mediated by 'Posit Connect'. It handles the details of communicating with the Connect API correctly, 'OAuth' token caching, and refresh behaviour.
Maintained by Aaron Jacobs. Last updated 1 months ago.
0.5 match 4 stars 4.00 score 1 scriptsdswatson
leakyIV:Leaky Instrumental Variables
Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical methods rely on strong assumptions such as the exclusion criterion, which states that instrumental effects must be entirely mediated by treatments. In the so-called "leaky" IV setting, candidate instruments are allowed to have some direct influence on outcomes, rendering the average treatment effect (ATE) unidentifiable. But with limits on the amount of information leakage, we may still recover sharp bounds on the ATE, providing partial identification. This package implements methods for ATE bounding in the leaky IV setting with linear structural equations. For details, see Watson et al. (2024) <doi:10.48550/arXiv.2404.04446>.
Maintained by David S. Watson. Last updated 10 months ago.
0.5 match 1 stars 3.85 score 1 scriptslumenlearning
rise:Conduct RISE Analysis
Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) <doi:10.19173/irrodl.v18i2.2952>. Automates the process of identifying learning materials that are not effectively supporting student learning in technology-mediated courses by synthesizing information about access to course content and performance on assessments.
Maintained by David Wiley. Last updated 6 years ago.
continuous-improvementlearning-analyticsopen-educational-resources
0.5 match 7 stars 3.54 score 7 scriptsjohnnyzhz
networksem:Network Structural Equation Modeling
Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the 'lavaan' package, ensuring ease of use for researchers. Technical details and examples can be found at <https://bigsem.psychstat.org>.
Maintained by Zhiyong Zhang. Last updated 1 months ago.
1.8 match 1.00 scorepboutros
SeqKat:Detection of Kataegis
Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 <doi:10.1101/287839>.
Maintained by Paul C. Boutros. Last updated 5 years ago.
0.5 match 2.11 score 13 scriptsjnpeng
endogeneity:Recursive Two-Stage Models to Address Endogeneity
Various recursive two-stage models to address the endogeneity issue of treatment variables in observational study or mediators in experiments. The details of the models are discussed in Peng (2023) <doi:10.1287/isre.2022.1113>.
Maintained by Jing Peng. Last updated 2 months ago.
0.5 match 2.00 score 2 scripts