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SemiCompRisks (v3.4): R-package for hierarchical models in semi-competing risks data analysis
This package provides tools for conducting parametric and semi-parametric analyses of semi-competing risks and univariate survival data. It supports:
SemiCompRisks (v3.4): R-package for hierarchical models in semi-competing risks data analysis
This package provides tools for conducting parametric and semi-parametric analyses of semi-competing risks and univariate survival data. It supports:
Hierarchical proportional hazards models
Accelerated failure time models
Applications to independent and cluster-correlated data
Complex censoring and truncation mechanisms
Resources: Download (CRAN), Reference manual, Vignettes, Software paper
mBvs (v1.5): R-package for Bayesian variable selection in multivariate data
The mBvs package offers robust Bayesian variable selection methods tailored for datasets with multivariate responses and multiple covariates (outcome-specific variable selection). Its methods are applicable for:
mBvs (v1.5): R-package for Bayesian variable selection in multivariate data
The mBvs package offers robust Bayesian variable selection methods tailored for datasets with multivariate responses and multiple covariates (outcome-specific variable selection). Its methods are applicable for:
Continuous data
Zero-inflated count data
Resources: Download (CRAN), Reference manual, Examples (GitHub)
psbcGroup (v1.7): R-package for Bayesian survival models with shrinkage and grouping priors
psbcGroup implements penalized parametric and semi-parametric Bayesian models for survival analysis, leveraging a variety of shrinkage priors:
psbcGroup (v1.7): R-package for Bayesian survival models with shrinkage and grouping priors
psbcGroup implements penalized parametric and semi-parametric Bayesian models for survival analysis, leveraging a variety of shrinkage priors:
Designed for survival data with high-dimensional covariates
Ordinary lasso, elastic-net, fused lasso, group lasso priors
Supports proportional hazards and accelerated failure time models
Resources: Download (CRAN), Reference manual
mspatPPM (v1.0): R-package for multivariate spatial point process models
The current version of the package provides multivariate log-Gaussian Cox process (LGCP) models for applications in spatial image data analysis.
mspatPPM (v1.0): R-package for multivariate spatial point process models
The current version of the package provides multivariate log-Gaussian Cox process (LGCP) models for applications in spatial image data analysis.
Bayesian multivariate LGCP models
Quantification of multivariate dependence structures in multi-type point patterns using cross-correlation and proportion of variance
Framework to synthesize image-specific quantities for multi-level datasets
MCPP (v1.0): R-package for multivariate cluster point process models
The MCPP package provides an innovative framework for modeling multivariate cluster point processes, offering flexibility for applications in spatial statistics and image analysis.
MCPP (v1.0): R-package for multivariate cluster point process models
The MCPP package provides an innovative framework for modeling multivariate cluster point processes, offering flexibility for applications in spatial statistics and image analysis.
Resources: Download (GitHub)
BVSMed (v1.0): R codes for Bayesian variable selection in high-dimensional mediation analysis
The BVSMed package implements Bayesian variable selection for high-dimensional mediation analysis, exploiting dependencies among mediators to increase the power to identify active pathways.
BVSMed (v1.0): R codes for Bayesian variable selection in high-dimensional mediation analysis
The BVSMed package implements Bayesian variable selection for high-dimensional mediation analysis, exploiting dependencies among mediators to increase the power to identify active pathways.
Resources: Download (GitHub)
GroupNormalization (v1.0): R codes for group-wise normalization in microbiome data analysis
The package implements group-wise normalization techniques for differential abundance analysis of microbiome data, effectively reducing compositional bias.
GroupNormalization (v1.0): R codes for group-wise normalization in microbiome data analysis
The package implements group-wise normalization techniques for differential abundance analysis of microbiome data, effectively reducing compositional bias.
Resources: Download (GitHub)
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