R package for statistical inference using partially observed Markov processes
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Updated
Jul 15, 2024 - R
R package for statistical inference using partially observed Markov processes
Code for simulation studies in "An approximate quasi-likelihood approach for error-prone failure time outcomes and exposures", Boe et al. (2021) (https://doi.org/10.1002/sim.9108)
batchtma: R package to adjust for batch effects, for example between tissue microarrays
Raw files for a document examining the use of latent variable and sum scores for secondary analysis.
Fit Poolwise Regression Models
Package to produce quantities of interest from rank order questions after correcting for bias from random responses.
Simulation Demo of Variational Bayes Model in My Master's Degree Thesis in Statistics
An R package for fitting state-space models to repeated measures of multiple individuals with covariates
lpme provides tools for analyzing latent variable models with measurement error correction, particularly in scenarios with identification restrictions. It implements various correction methods, uses partitioning and bootstrapping for standard errors, with OLS and IV-based corrections.
An R Package for Two-Stage Path Analysis (2S-PA)
A package for a more general, more straightforward, and more creative conjoint analysis
Replication files for Addressing Measurement Errors in Ranking Questions for the Social Sciences
A Shiny app that illustrates the relationship between reliability and measurement error.
Supplementary material and reproducible research files for article “A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations” by Emma Skarstein, Sara Martino and Stefanie Muff.
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