R-universe search: mediator

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 dependents

sfcheung

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.

bootstrapping confidence-interval lavaan manymome mediation moderated-mediation moderation regression sem standardized-effect-size structural-equation-modeling

133.7 match 1 stars 8.06 score 172 scripts 4 dependents

cedricbatailler

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 Cédric Batailler. Last updated 2 months ago.

mediation mediation-analysis psychology

62.6 match 8 stars 6.06 score 36 scripts

krisrs1128

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.

coverage microbiome regression sequencing software statisticalmethod structuralequationmodels causal-inference data-integration mediation-analysis

63.0 match 1 stars 5.56 score 13 scripts

kaz-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-inference mediation-analysis

37.9 match 29 stars 6.84 score 40 scripts

yinanzheng

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 scripts

tysonstanley

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-effects margins mediation mediation-analysis mediator partial-effects rstudio

42.0 match 3 stars 4.29 score 13 scripts

aalfons

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 dependents

kkholst

mets:Analysis of Multivariate Event Times

Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.

Maintained by Klaus K. Holst. Last updated 3 days ago.

multivariate-time-to-event survival-analysis time-to-event fortran openblas cpp

11.2 match 14 stars 13.47 score 236 scripts 42 dependents

jmpsteen

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-inference flexible-modeling mediation-analysis

18.8 match 23 stars 7.09 score 54 scripts

revelle

psych:Procedures for Psychological, Psychometric, and Personality Research

A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.

Maintained by William Revelle. Last updated 3 months ago.

9.1 match 52 stars 13.94 score 29k scripts 317 dependents

mvuorre

bmlm:Bayesian Multilevel Mediation

Easy estimation of Bayesian multilevel mediation models with Stan.

Maintained by Matti Vuorre. Last updated 4 months ago.

bayesian-data-analysis multilevel-mediation-models statistics cpp

21.1 match 42 stars 5.81 score 34 scripts

dcoffman

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 scripts

wjzhong

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.

cpp

24.1 match 1 stars 4.91 score 27 scripts 2 dependents

kimberlywebb

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 scripts

weiliang

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 dependents

ams329

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 scripts

caubm

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 scripts

sfcheung

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-intervals lavaan likelihood-based profile-likelihood structural-equation-modeling

16.6 match 1 stars 5.93 score 188 scripts

data-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-inference confidence-intervals likelihood-ratio-test mediation mediation-analysis

23.0 match 1 stars 4.10 score 25 scripts

meilinjiang

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.

cpp

23.7 match 3.70 score 2 scripts

easystats

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-factors bayesfactor bayesian bayesian-framework credible-interval easystats hacktoberfest hdi map posterior-distributions rope

4.8 match 579 stars 16.84 score 2.2k scripts 82 dependents

nhejazi

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 Díaz, 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-inference causal-machine-learning inverse-probability-weights machine-learning mediation-analysis stochastic-interventions targeted-learning treatment-effects

17.6 match 13 stars 4.46 score 22 scripts

nhejazi

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 Díaz and NS Hejazi (2020) <doi:10.1111/rssb.12362>.

Maintained by Nima Hejazi. Last updated 3 years ago.

causal-inference inverse-probability-weights machine-learning mediation-analysis stochastic-interventions targeted-learning treatment-effects

21.0 match 9 stars 3.73 score 12 scripts

zhaoyi1026

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 scripts

cran

moderate.mediation:Causal Moderated Mediation Analysis

Causal moderated mediation analysis using the methods proposed by Qin and Wang (2023) <doi:10.3758/s13428-023-02095-4>. Causal moderated mediation analysis is crucial for investigating how, for whom, and where a treatment is effective by assessing the heterogeneity of mediation mechanism across individuals and contexts. This package enables researchers to estimate and test the conditional and moderated mediation effects, assess their sensitivity to unmeasured pre-treatment confounding, and visualize the results. The package is built based on the quasi-Bayesian Monte Carlo method, because it has relatively better performance at small sample sizes, and its running speed is the fastest. The package is applicable to a treatment of any scale, a binary or continuous mediator, a binary or continuous outcome, and one or more moderators of any scale.

Maintained by Xu Qin. Last updated 10 months ago.

50.3 match 1.48 score

yaoxiangli

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-search ceu-mass-mediator metablomics ms-search

15.5 match 15 stars 4.73 score 12 scripts

zuchaoshen

odr:Optimal Design and Statistical Power for Experimental Studies Investigating Main, Mediation, and Moderation Effects

Calculate the optimal sample size allocation that produces the highest statistical power for experimental studies under a budget constraint, and perform power analyses with and without accommodating cost structures of sampling. The designs cover single-level and multilevel experiments detecting main, mediation, and moderation effects (and some combinations). The references for the proposed methods include: (1) Shen, Z., & Kelcey, B. (2020). Optimal sample allocation under unequal costs in cluster-randomized trials. Journal of Educational and Behavioral Statistics, 45(4): 446-474. <doi:10.3102/1076998620912418>. (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for three-level multisite cluster-randomized trials. Journal of Research on Educational Effectiveness, 15 (1), 130-150. <doi:10.1080/19345747.2021.1953200>. (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in multisite randomized trials. The Journal of Experimental Education. <doi:10.1080/00220973.2020.1830361>. (4) Champely, S. (2020). pwr: Basic functions for power analysis (Version 1.3-0) [Software]. Available from <https://CRAN.R-project.org/package=pwr>.

Maintained by Zuchao Shen. Last updated 2 years ago.

21.8 match 3.30 score 20 scripts

qingzhaoyu

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 scripts

qingzhaoyu

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 dependents

bioc

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.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network survival genesetenrichment networkenrichment

10.8 match 5 stars 6.59 score 43 scripts

sfcheung

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.

lavaan manymome mediation moderated-mediation moderation regression sem structural-equation-modeling

17.5 match 4.00 score 2 scripts

yelleknek

MBESS:The MBESS R Package

Implements methods that are useful in designing research studies and analyzing data, with particular emphasis on methods that are developed for or used within the behavioral, educational, and social sciences (broadly defined). That being said, many of the methods implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a suite of functions for a variety of related topics, such as effect sizes, confidence intervals for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and minimum-risk point estimation perspectives), mediation analysis, various properties of distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but MBESS became more general and now contains methods applicable and used in a wide variety of fields and is an orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has greatly benefited from others, see <https://www3.nd.edu/~kkelley/site/MBESS.html> for a detailed list of those that have contributed and other details.

Maintained by Ken Kelley. Last updated 1 years ago.

7.8 match 2 stars 8.21 score 274 scripts 23 dependents

falkcarl

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 Miočević (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 scripts

jeksterslab

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.

centrality continuous-time delta-method mediation monte-carlo-method network openblas cpp openmp

13.8 match 4.56 score 24 scripts

bioc

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.

graphandnetwork

5.3 match 11.78 score 764 scripts 342 dependents

sinnweja

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.

openblas cpp

20.0 match 2 stars 3.00 score 5 scripts

veronica0206

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 scripts

rvlenth

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 dependents

tlverse

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-inference causal-mediation-analysis machine-learning mediation-analysis stochastic-interventions targeted-learning treatment-effects

18.0 match 6 stars 2.98 score 16 scripts

umich-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.

openblas cpp

11.0 match 4.80 score 42 scripts 1 dependents

johnnyzhz

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 scripts

cran

hdmed:Methods for Mediation Analysis with High-Dimensional Mediators

A suite of functions for performing mediation analysis with high-dimensional mediators. In addition to centralizing code from several existing packages for high-dimensional mediation analysis, we provide organized, well-documented functions for a handle of methods which, though programmed their original authors, have not previously been formalized into R packages or been made presentable for public use. The methods we include cover a broad array of approaches and objectives, and are described in detail by both our companion manuscript---"Methods for Mediation Analysis with High-Dimensional DNA Methylation Data: Possible Choices and Comparison"---and the original publications that proposed them. The specific methods offered by our package include the Bayesian sparse linear mixed model (BSLMM) by Song et al. (2019); high-dimensional mediation analysis (HDMA) by Gao et al. (2019); high-dimensional multivariate mediation (HDMM) by Chén et al. (2018); high-dimensional linear mediation analysis (HILMA) by Zhou et al. (2020); high-dimensional mediation analysis (HIMA) by Zhang et al. (2016); latent variable mediation analysis (LVMA) by Derkach et al. (2019); mediation by fixed-effect model (MedFix) by Zhang (2021); pathway LASSO by Zhao & Luo (2022); principal component mediation analysis (PCMA) by Huang & Pan (2016); and sparse principal component mediation analysis (SPCMA) by Zhao et al. (2020). Citations for the corresponding papers can be found in their respective functions.

Maintained by Dylan Clark-Boucher. Last updated 10 months ago.

30.7 match 1.70 score

canyi-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-power mediation-analysis type-i-error-control

14.5 match 1 stars 3.30 score 7 scripts

aalfons

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 score

danielmc1603

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 score

chrisaberson

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 scripts

jasonmoy28

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 scripts

ncysur

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 scripts

jinghuazhao

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.

genetics imputation lmm fortran

3.6 match 12 stars 11.88 score 448 scripts 16 dependents

xiw021

ccmEstimator:Comparative Causal Mediation Estimation

Functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.

Maintained by Xiaohan Wu. Last updated 4 years ago.

11.3 match 3.70 score 3 scripts

tidymodels

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.

modeling tidy-data

1.9 match 1.5k stars 21.56 score 37k scripts 1.4k dependents

umich-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 scripts

pdwaggoner

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 scripts

belayb

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.

cpp

10.4 match 2 stars 3.30 score 3 scripts

psychbruce

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.

anova data-analysis data-science linear-models linear-regression multilevel-models statistics toolbox

4.3 match 176 stars 7.87 score 316 scripts 3 dependents

banovaapp

BANOVA:Hierarchical Bayesian ANOVA Models

It covers several Bayesian Analysis of Variance (BANOVA) models used in analysis of experimental designs in which both within- and between- subjects factors are manipulated. They can be applied to data that are common in the behavioral and social sciences. The package includes: Hierarchical Bayes ANOVA models with normal response, t response, Binomial (Bernoulli) response, Poisson response, ordered multinomial response and multinomial response variables. All models accommodate unobserved heterogeneity by including a normal distribution of the parameters across individuals. Outputs of the package include tables of sums of squares, effect sizes and p-values, and tables of predictions, which are easily interpretable for behavioral and social researchers. The floodlight analysis and mediation analysis based on these models are also provided. BANOVA uses 'Stan' and 'JAGS' as the computational platform. References: Dong and Wedel (2017) <doi:10.18637/jss.v081.i09>; Wedel and Dong (2020) <doi:10.1002/jcpy.1111>.

Maintained by Chen Dong. Last updated 3 years ago.

jags cpp

9.6 match 2 stars 3.48 score 19 scripts

mbq

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.

openmp

6.9 match 4.78 score 3 scripts

qingzhaoyu

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 scripts

tfletcher05

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 scripts

psyen0824

HDMAADMM:ADMM for High-Dimensional Mediation Models

We use the Alternating Direction Method of Multipliers (ADMM) for parameter estimation in high-dimensional, single-modality mediation models. To improve the sensitivity and specificity of estimated mediation effects, we offer the sure independence screening (SIS) function for dimension reduction. The available penalty options include Lasso, Elastic Net, Pathway Lasso, and Network-constrained Penalty. The methods employed in the package are based on Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). <doi:10.1561/2200000016>, Fan, J., & Lv, J. (2008) <doi:10.1111/j.1467-9868.2008.00674.x>, Li, C., & Li, H. (2008) <doi:10.1093/bioinformatics/btn081>, Tibshirani, R. (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Zhao, Y., & Luo, X. (2022) <doi:10.4310/21-sii673>, and Zou, H., & Hastie, T. (2005) <doi:10.1111/j.1467-9868.2005.00503.x>.

Maintained by Pei-Shan Yen. Last updated 7 months ago.

cpp

9.0 match 3.54 score

dziakj1

funmediation:Functional Mediation for a Distal Outcome

Fits a functional mediation model with a scalar distal outcome. The method is described in detail by Coffman, Dziak, Litson, Chakraborti, Piper & Li (2021) <arXiv:2112.03960>. The model is similar to that of Lindquist (2012) <doi:10.1080/01621459.2012.695640> although allowing a binary outcome as an alternative to a numerical outcome. The current version is a minor bug fix in the vignette. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Maintained by John J. Dziak. Last updated 1 years ago.

9.5 match 2 stars 3.30 score 2 scripts

chaochengstat

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 score

sfcheung

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.

lavaan model-comparison model-comparison-and-selection model-selection structural-equation-modeling

6.8 match 4.54 score 2 scripts

bioc

MultiMed:Testing multiple biological mediators simultaneously

Implements methods for testing multiple mediators

Maintained by Simina M. Boca. Last updated 5 months ago.

multiplecomparison statisticalmethod software

7.0 match 4.30 score 8 scripts

cran

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 score

metinbulus

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 scripts

nickch-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 Hernán, 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 dependents

r-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 scripts

bbolker

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 dependents

qingzhaoyu

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.

jags cpp

12.4 match 2.00 score 2 scripts

avi-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 scripts

cran

hdbm:High Dimensional Bayesian Mediation Analysis

Perform mediation analysis in the presence of high-dimensional mediators based on the potential outcome framework. High dimensional Bayesian mediation (HDBM), developed by Song et al (2018) <doi:10.1101/467399>, 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 Alexander Rix. Last updated 6 years ago.

openblas cpp

8.9 match 2.70 score

r-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-inference dag ggplot-extension

2.0 match 443 stars 11.78 score 1.8k scripts 5 dependents

bioc

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.

systemsbiology graphandnetwork pathways kegg openjdk

6.0 match 3.86 score 12 scripts

ropensci

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.

gbif specimens api web-services occurrences species taxonomy biodiversity data lifewatch oscibio spocc

1.8 match 161 stars 13.26 score 2.1k scripts 20 dependents

rempsyc

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 Rémi Thériault. Last updated 9 months ago.

convenience-functions lavaan psychology statistics structural-equation-modeling

3.3 match 18 stars 6.95 score 33 scripts

bioc

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.

microbiome software dataimport analysis bioconductor

2.0 match 52 stars 11.50 score 316 scripts 5 dependents

gitlzg

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.

cpp

8.5 match 2.70 score 7 scripts

mattblackwell

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 scripts

cwatson

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-connectivity brain-imaging complex-networks connectome connectomics fmri graph-theory mri network-analysis neuroimaging neuroscience statistics tractography

2.9 match 188 stars 7.86 score 107 scripts 3 dependents

adwolfer

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.

pcamethods (>= 1.92.0)

3.5 match 11 stars 6.44 score 63 scripts

r-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 dependents

pcruniversum

chipPCR:Toolkit of Helper Functions to Pre-Process Amplification Data

A collection of functions to pre-process amplification curve data from polymerase chain reaction (PCR) or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems ('VideoScan HCU', capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. For more information see: Roediger et al. (2015) <doi:10.1093/bioinformatics/btv205>.

Maintained by Stefan Roediger. Last updated 4 years ago.

3.3 match 8 stars 6.84 score 97 scripts 1 dependents

vankesteren

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.

openblas cpp openmp

9.6 match 4 stars 2.30 score 4 scripts

integrated-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.

bayes causal dags mixedmethods stan cpp

2.5 match 27 stars 9.03 score 54 scripts

cran

frailtypack:Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

The following several classes of frailty models using a penalized likelihood estimation on the hazard function but also a parametric estimation can be fit using this R package: 1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox proportional hazard model. Clustered and recurrent survival times can be studied. 2) Additive frailty models for proportional hazard models with two correlated random effects (intercept random effect with random slope). 3) Nested frailty models for hierarchically clustered data (with 2 levels of clustering) by including two iid gamma random effects. 4) Joint frailty models in the context of the joint modelling for recurrent events with terminal event for clustered data or not. A joint frailty model for two semi-competing risks and clustered data is also proposed. 5) Joint general frailty models in the context of the joint modelling for recurrent events with terminal event data with two independent frailty terms. 6) Joint Nested frailty models in the context of the joint modelling for recurrent events with terminal event, for hierarchically clustered data (with two levels of clustering) by including two iid gamma random effects. 7) Multivariate joint frailty models for two types of recurrent events and a terminal event. 8) Joint models for longitudinal data and a terminal event. 9) Trivariate joint models for longitudinal data, recurrent events and a terminal event. 10) Joint frailty models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time and/or longitudinal endpoints with the possibility to use a mediation analysis model. 11) Conditional and Marginal two-part joint models for longitudinal semicontinuous data and a terminal event. 12) Joint frailty-copula models for the validation of surrogate endpoints in multiple randomized clinical trials with failure-time endpoints. 13) Generalized shared and joint frailty models for recurrent and terminal events. Proportional hazards (PH), additive hazard (AH), proportional odds (PO) and probit models are available in a fully parametric framework. For PH and AH models, it is possible to consider type-varying coefficients and flexible semiparametric hazard function. Prediction values are available (for a terminal event or for a new recurrent event). Left-truncated (not for Joint model), right-censored data, interval-censored data (only for Cox proportional hazard and shared frailty model) and strata are allowed. In each model, the random effects have the gamma or normal distribution. Now, you can also consider time-varying covariates effects in Cox, shared and joint frailty models (1-5). The package includes concordance measures for Cox proportional hazards models and for shared frailty models. 14) Competing Joint Frailty Model: A single type of recurrent event and two terminal events. 15) functions to compute power and sample size for four Gamma-frailty-based designs: Shared Frailty Models, Nested Frailty Models, Joint Frailty Models, and General Joint Frailty Models. Each design includes two primary functions: a power function, which computes power given a specified sample size; and a sample size function, which computes the required sample size to achieve a specified power. Moreover, the package can be used with its shiny application, in a local mode or by following the link below.

Maintained by Virginie Rondeau. Last updated 10 days ago.

fortran openmp

3.8 match 7 stars 5.56 score 1 dependents

xu-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 scripts

revelle

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 dependents

metrumresearchgroup

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.

mrgsolve ode openblas cpp

1.8 match 138 stars 10.90 score 1.2k scripts 3 dependents

metinbulus

pwrss:Statistical Power and Sample Size Calculation Tools

Statistical power and minimum required sample size calculations for (1) testing a proportion (one-sample) against a constant, (2) testing a mean (one-sample) against a constant, (3) testing difference between two proportions (independent samples), (4) testing difference between two means or groups (parametric and non-parametric tests for independent and paired samples), (5) testing a correlation (one-sample) against a constant, (6) testing difference between two correlations (independent samples), (7) testing a single coefficient in multiple linear regression, logistic regression, and Poisson regression (with standardized or unstandardized coefficients, with no covariates or covariate adjusted), (8) testing an indirect effect (with standardized or unstandardized coefficients, with no covariates or covariate adjusted) in the mediation analysis (Sobel, Joint, and Monte Carlo tests), (9) testing an R-squared against zero in linear regression, (10) testing an R-squared difference against zero in hierarchical regression, (11) testing an eta-squared or f-squared (for main and interaction effects) against zero in analysis of variance (could be one-way, two-way, and three-way), (12) testing an eta-squared or f-squared (for main and interaction effects) against zero in analysis of covariance (could be one-way, two-way, and three-way), (13) testing an eta-squared or f-squared (for between, within, and interaction effects) against zero in one-way repeated measures analysis of variance (with non-sphericity correction and repeated measures correlation), and (14) testing goodness-of-fit or independence for contingency tables. Alternative hypothesis can be formulated as "not equal", "less", "greater", "non-inferior", "superior", or "equivalent" in (1), (2), (3), and (4); as "not equal", "less", or "greater" in (5), (6), (7) and (8); but always as "greater" in (9), (10), (11), (12), (13), and (14). Reference: Bulus and Polat (2023) <https://osf.io/ua5fc>.

Maintained by Metin Bulus. Last updated 3 months ago.

4.2 match 1 stars 4.67 score 57 scripts

bioc

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.

microbiome software visualization bioconductor microbiome-analysis plotting

2.3 match 10 stars 8.65 score 81 scripts 1 dependents

sfcheung

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.

bootstrapping lavaan structural-equation-modeling

3.8 match 5.13 score 27 scripts

johnnyzhz

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 scripts

sfcheung

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.

bootstrapping confidence-intervals generalized-linear-models lavaan logistic-regression regression sem standardization structural-equation-modeling

3.8 match 1 stars 4.95 score 8 scripts

umich-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 scripts

hugometric

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 scripts

uscbiostats

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 scripts

rjacobucci

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.

openblas cpp

2.2 match 14 stars 6.63 score 77 scripts

bioc

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.

sequencing 3 end sequencing polyadenylation site internal priming

3.5 match 4.26 score 8 scripts 1 dependents

daniel258

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 scripts

cran

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 score

cran

HDMT:A Multiple Testing Procedure for High-Dimensional Mediation Hypotheses

A multiple-testing procedure for high-dimensional mediation hypotheses. Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods for mediation analyses, the popular joint significance (JS) test yields an overly conservative type I error rate and therefore low power. In the R package 'HDMT' we implement a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when using JS for testing high-dimensional mediation hypotheses. The core of our procedure is based on estimating the proportions of three component null hypotheses and deriving the corresponding mixture distribution of null p-values. Results of the data examples include better-behaved quantile-quantile plots and improved detection of novel mediation relationships on the role of DNA methylation in genetic regulation of gene expression. With increasing interest in mediation by molecular intermediaries such as gene expression, the proposed method addresses an unmet methodological challenge. Methods used in the package refer to James Y. Dai, Janet L. Stanford & Michael LeBlanc (2020) <doi:10.1080/01621459.2020.1765785>.

Maintained by James Dai. Last updated 3 years ago.

5.0 match 2.86 score 12 scripts 2 dependents

jinkim3

exploratory:A Tool for Large-Scale Exploratory Analyses

Conduct numerous exploratory analyses in an instant with a point-and-click interface. With one simple command, this tool launches a Shiny App on the local machine. Drag and drop variables in a data set to categorize them as possible independent, dependent, moderating, or mediating variables. Then run dozens (or hundreds) of analyses instantly to uncover any statistically significant relationships among variables. Any relationship thus uncovered should be tested in follow-up studies. This tool is designed only to facilitate exploratory analyses and should NEVER be used for p-hacking. Many of the functions used in this package are previous versions of functions in the R Packages 'kim' and 'ezr'. Selected References: Chang et al. (2021) <https://CRAN.R-project.org/package=shiny>. Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>. Kim (2023) <https://jinkim.science/docs/kim.pdf>. Kim (2021) <doi:10.5281/zenodo.4619237>. Kim (2020) <https://CRAN.R-project.org/package=ezr>. Simmons et al. (2011) <doi:10.1177/0956797611417632> Tingley et al. (2019) <https://CRAN.R-project.org/package=mediation>. Wickham et al. (2020) <https://CRAN.R-project.org/package=ggplot2>.

Maintained by Jin Kim. Last updated 1 years ago.

3.0 match 5 stars 4.75 score 45 scripts

youyifong

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.

openblas

1.8 match 7.87 score 236 scripts 7 dependents

cran

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 scripts

oeysan

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 Øystein Olav Skaar. Last updated 3 years ago.

bayesian-data-analysis bayesian-statistics jags mcmc psychological-science cpp

2.3 match 10 stars 5.89 score 31 scripts

bioc

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.

genetics snp geneticvariability openblas cpp

2.7 match 1 stars 4.48 score 5 scripts

nancyfcheng

maczic:Mediation Analysis for Count and Zero-Inflated Count Data

Performs causal mediation analysis for count and zero-inflated count data without or with a post-treatment confounder; calculates power to detect prespecified causal mediation effects, direct effects, and total effects; performs sensitivity analysis when there is a treatment- induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F., Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) <doi:10.1177/0962280216686131>. Implements Instrumental Variable (IV) method to estimate the controlled (natural) direct and mediation effects, and compute the bootstrap Confidence Intervals as described by Guo, Z., Small, D.S., Gansky, S.A., Cheng, J. (2018) <doi:10.1111/rssc.12233>. This software was made possible by Grant R03DE028410 from the National Institute of Dental and Craniofacial Research, a component of the National Institutes of Health.

Maintained by Nancy Cheng. Last updated 2 years ago.

12.2 match 1.00 score 1 scripts

declaredesign

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 scripts

xiangzhou09

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 scripts

cran

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 scripts

qyaozh

Keng:Knock Errors Off Nice Guesses

Miscellaneous functions and data used in psychological research and teaching. Keng currently has a built-in dataset depress, and could (1) scale a vector; (2) compute the cut-off values of Pearson's r with known sample size; (3) test the significance and compute the post-hoc power for Pearson's r with known sample size; (4) conduct a priori power analysis and plan the sample size for Pearson's r; (5) compare lm()'s fitted outputs using R-squared, f_squared, post-hoc power, and PRE (Proportional Reduction in Error, also called partial R-squared or partial Eta-squared); (6) calculate PRE from partial correlation, Cohen's f, or f_squared; (7) conduct a priori power analysis and plan the sample size for one or a set of predictors in regression analysis; (8) conduct post-hoc power analysis for one or a set of predictors in regression analysis with known sample size.

Maintained by Qingyao Zhang. Last updated 1 months ago.

2.0 match 5.42 score 11 scripts

ytzhong

RsqMed:Total Mediation Effect Size Measure for High-Dimensional Mediators

An implementation of calculating the R-squared measure as a total mediation effect size measure and its confidence interval for moderate- or high-dimensional mediator models. It gives an option to filter out non-mediators using variable selection methods. The original R package is directly related to the paper Yang et al (2021) "Estimation of mediation effect for high-dimensional omics mediators with application to the Framingham Heart Study" <doi:10.1101/774877>. The new version contains a choice of using cross-fitting, which is computationally faster. The details of the cross-fitting method are available in the paper Xu et al (2023) "Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting" <doi:10.1101/2023.02.06.527391>.

Maintained by Tianzhong Yang. Last updated 1 years ago.

10.6 match 1.00 score

jinkim3

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 scripts

johnnyzhz

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 score

kcgchan

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 scripts

bioc

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.

infrastructure go bioinformatics genomics ontology

1.6 match 3 stars 6.37 score 75 scripts 2 dependents

bioc

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.

microbiome software visualization gui shinyapps dataimport shiny-apps visualisation

1.5 match 3 stars 6.26 score 5 scripts

cran

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 score

cran

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 scripts

cran

rmpw:Causal Mediation Analysis Using Weighting Approach

We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.

Maintained by Xu Qin. Last updated 1 years ago.

9.0 match 1.00 score

bioc

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.

software geneexpression generegulation networkinference genesetenrichment biomedicalinformatics genetics transcriptomics atacseq rnaseq graphandnetwork regression transcription chipseq

1.6 match 5.40 score 24 scripts

syeonkang

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 scripts

randel

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 scripts

jaydevine

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 score

sem-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-factors composites construct pls-models

1.1 match 62 stars 7.46 score 284 scripts

john-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.

modeling pkpd

1.2 match 13 stars 7.14 score 33 scripts

nie-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 score

laylaparast

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 dependents

cran

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 scripts

zhaoyi1026

gma:Granger Mediation Analysis

Performs Granger mediation analysis (GMA) for time series. This package includes a single level GMA model and a two-level GMA model, for time series with hierarchically nested structure. The single level GMA model for the time series of a single participant performs the causal mediation analysis which integrates the structural equation modeling and the Granger causality frameworks. A vector autoregressive model of order p is employed to account for the spatiotemporal dependencies in the data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under the two-level model, by leveraging the variabilities across participants, the parameters are identifiable and consistently estimated based on a full conditional likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2017), Granger Mediation Analysis of Multiple Time Series with an Application to fMRI, <arXiv:1709.05328> for details.

Maintained by Yi Zhao. Last updated 7 years ago.

8.0 match 1 stars 1.00 score 7 scripts

cran

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 score

cran

rcausim:Generate Causally-Simulated Data

Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.

Maintained by Herdiantri Sufriyana. Last updated 9 months ago.

2.5 match 3.00 score

cribbie

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-testing negligible-effect-statistical-testing negligible-effect-testing statistics

1.9 match 4 stars 4.00 score 10 scripts

qingzhaoyu

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.

jags cpp

7.3 match 1.00 score

cran

mediationsens:Simulation-Based Sensitivity Analysis for Causal Mediation Studies

Simulation-based sensitivity analysis for causal mediation studies. It numerically and graphically evaluates the sensitivity of causal mediation analysis results to the presence of unmeasured pretreatment confounding. The proposed method has primary advantages over existing methods. First, using an unmeasured pretreatment confounder conditional associations with the treatment, mediator, and outcome as sensitivity parameters, the method enables users to intuitively assess sensitivity in reference to prior knowledge about the strength of a potential unmeasured pretreatment confounder. Second, the method accurately reflects the influence of unmeasured pretreatment confounding on the efficiency of estimation of the causal effects. Third, the method can be implemented in different causal mediation analysis approaches, including regression-based, simulation-based, and propensity score-based methods. It is applicable to both randomized experiments and observational studies.

Maintained by Xu Qin. Last updated 1 years ago.

7.2 match 1.00 score

cran

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 dependents

johnfergusonnuig

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 scripts

buybnb

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 score

tdjorgensen

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 scripts

sduxbury

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 dependents

bioc

DeepTarget:Deep characterization of cancer drugs

This package predicts a drug’s 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.

genetarget geneprediction pathways geneexpression rnaseq immunooncology differentialexpression genesetenrichment reportwriting crispr

1.1 match 4.54 score 1 scripts

argeorgeson

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 scripts

cran

misty:Miscellaneous Functions 'T. Yanagida'

Miscellaneous functions for (1) data management (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation, multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), (6) statistical analysis (e.g., bootstrap confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination), and (7) functions to interact with 'Blimp' and 'Mplus'.

Maintained by Takuya Yanagida. Last updated 6 days ago.

1.7 match 1 stars 2.82 score 1 dependents

sduxbury

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 score

tacazares

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 5’ end of the guide strand, called a seed region, bind the 3’ 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-analysis mirna rna-seq sirna transcriptomics

0.9 match 7 stars 4.54 score 7 scripts

uscbiostats

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.

gsl cpp

1.0 match 2 stars 3.81 score 32 scripts

synergisticcauselearning

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.

openblas cpp

2.3 match 1.70 score 6 scripts

cran

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 score

steveculpepper

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 score

mattansb

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 scripts

cran

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 score

personalizedtransplantcare

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 score

mdtrinh

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 scripts

bioc

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.

geneexpression transcription generegulation networkinference transcriptomics systemsbiology regression randomforest machinelearning

0.5 match 5.36 score 38 scripts 1 dependents

yijuanhu

LDM:Testing Hypotheses About the Microbiome using the Linear Decomposition Model

A single analysis path that includes distance-based ordination, global tests of any effect of the microbiome, and tests of the effects of individual taxa with false-discovery-rate (FDR) control. It accommodates both continuous and discrete covariates as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based p-values that can control for sample correlations. It can be applied to transformed data, and an omnibus test can combine results from analyses conducted on different transformation scales. It can also be used for testing presence-absence associations based on infinite number of rarefaction replicates, testing mediation effects of the microbiome, analyzing censored time-to-event outcomes, and for compositional analysis by fitting linear models to centered-log-ratio taxa count data.

Maintained by Yi-Juan Hu. Last updated 2 years ago.

0.5 match 7 stars 4.91 score 23 scripts

maartenbijlsma

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 scripts

anitalindmark

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 scripts

cran

tmle:Targeted Maximum Likelihood Estimation

Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.

Maintained by Susan Gruber. Last updated 10 months ago.

0.5 match 1 stars 4.73 score 300 scripts 3 dependents

cran

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 score

posit-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 scripts

dswatson

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 scripts

tombjjj

silp:Conditional Process Analysis (CPA) via SEM Approach

Utilizes the Reliability-Adjusted Product Indicator (RAPI) method to estimate effects among latent variables, thus allowing for more precise definition and analysis of mediation and moderation models. Our simulation studies reveal that while 'silp' may exhibit instability with smaller sample sizes and lower reliability scores (e.g., N = 100, 'omega' = 0.7), implementing nearest positive definite matrix correction and bootstrap confidence interval estimation can significantly ameliorate this volatility. When these adjustments are applied, 'silp' achieves estimations akin in quality to those derived from LMS. In conclusion, the 'silp' package is a valuable tool for researchers seeking to explore complex relational structures between variables without resorting to commercial software. Cheung et al.(2021)<doi:10.1007/s10869-020-09717-0> Hsiao et al.(2018)<doi:10.1177/0013164416679877>.

Maintained by Yi-Hsuan Tseng. Last updated 5 days ago.

0.5 match 3.78 score 2 scripts

ryansunwork

csmGmm:Conditionally Symmetric Multidimensional Gaussian Mixture Model

Implements the conditionally symmetric multidimensional Gaussian mixture model (csmGmm) for large-scale testing of composite null hypotheses in genetic association applications such as mediation analysis, pleiotropy analysis, and replication analysis. In such analyses, we typically have J sets of K test statistics where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each one of the J sets, we want to know if we can reject all K individual nulls. Please see the vignette for a quickstart guide. The paper describing these methods is "Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies" by Sun R, McCaw Z, & Lin X (2024, <doi:10.1080/01621459.2024.2422124>). The paper is accepted and published online (but not yet in print) in the Journal of the American Statistical Association as of Dec 1 2024.

Maintained by Ryan Sun. Last updated 3 months ago.

0.5 match 3.73 score 108 scripts

lumenlearning

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-improvement learning-analytics open-educational-resources

0.5 match 7 stars 3.54 score 7 scripts

johnnyzhz

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 score

pboutros

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.

cpp

0.5 match 2.11 score 13 scripts

jnpeng

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.

cpp

0.5 match 2.00 score 2 scripts