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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 2 months ago.

24 stars 7.22 score 23 scripts

meierluk

hdi:High-Dimensional Inference

Implementation of multiple approaches to perform inference in high-dimensional models.

Maintained by Lukas Meier. Last updated 4 years ago.

2 stars 4.47 score 139 scripts 7 dependents

sen-zhao

Grace:Graph-Constrained Estimation and Hypothesis Tests

Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.

Maintained by Sen Zhao. Last updated 8 years ago.

1 stars 3.70 score 7 scripts

sami-leon

plsmmLasso:Variable Selection and Inference for Partial Semiparametric Linear Mixed-Effects Model

Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function. The model also accommodates interactions between the nonlinear function and a grouping variable, allowing for the capture of group-specific nonlinearities. Nonlinear functions are modeled using a set of bases functions. Estimation is conducted using a penalized Expectation-Maximization algorithm, and the package offers flexibility in choosing between various information criteria for model selection. Post-selection inference is carried out using a debiasing method, while inference on the nonlinear functions employs a bootstrap approach.

Maintained by Sami Leon. Last updated 10 months ago.

3.60 score 1 scripts

seunghyunmin

EAinference:Estimator Augmentation and Simulation-Based Inference

Estimator augmentation methods for statistical inference on high-dimensional data, as described in Zhou, Q. (2014) <arXiv:1401.4425v2> and Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309>. It provides several simulation-based inference methods: (a) Gaussian and wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group lasso and their de-biased estimators, (b) importance sampler for approximating p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with applications in post-selection inference.

Maintained by Seunghyun Min. Last updated 6 years ago.

3.11 score 13 scripts

feiyoung

TOSI:Two-Directional Simultaneous Inference for High-Dimensional Models

A general framework of two directional simultaneous inference is provided for high-dimensional as well as the fixed dimensional models with manifest variable or latent variable structure, such as high-dimensional mean models, high- dimensional sparse regression models, and high-dimensional latent factors models. It is making the simultaneous inference on a set of parameters from two directions, one is testing whether the estimated zero parameters indeed are zero and the other is testing whether there exists zero in the parameter set of non-zero. More details can be referred to Wei Liu, et al. (2023) <doi:10.1080/07350015.2023.2191672>.

Maintained by Wei Liu. Last updated 2 years ago.

2.70 score

jmbh

inet:Performing Inference on Networks with Regularization

Performs inference with the lasso in Gaussian Graphical Models. The package consists of wrappers for functions from the hdi package.

Maintained by Jonas Haslbeck. Last updated 3 years ago.

2.00 score 7 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 11 months ago.

1.70 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 3 years ago.

1.52 score 11 scripts 1 dependents

zhangxiany-tamu

SILM:Simultaneous Inference for Linear Models

Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) <doi:10.1080/01621459.2016.1166114>.

Maintained by Xianyang Zhang. Last updated 6 years ago.

1.00 score 4 scripts

kkunji

BigQuic:Big Quadratic Inverse Covariance Estimation

Use Newton's method, coordinate descent, and METIS clustering to solve the L1 regularized Gaussian MLE inverse covariance matrix estimation problem.

Maintained by Khalid B. Kunji. Last updated 2 years ago.

cppopenmp

1 stars 1.00 score 10 scripts

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