An R-Shiny application implementing a method of sexing the human os coxae based on logistic regressions and Bruzek's nonmetric traits <doi:10.1002/ajpa.23855>.

Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper
    (2023) <doi:10.31234/osf.io/6phs5>. In meta-analysis, there are
    often between-study differences. These can be coded as moderator variables,
    and controlled for using meta-regression. However, if the number of
    moderators is large relative to the number of studies, such an analysis may
    be overfit. Penalized meta-regression is useful in these cases, because
    it shrinks the regression slopes of irrelevant moderators towards zero.

A system contains easy-to-use tools for the conditional estimation of the prevalence of an emerging or rare infectious diseases using the methods proposed in Guerrier et al. (2023) <arXiv:2012.10745>.

Utility functions for the handling, analysis and visualisation of data from portable emissions
            measurement systems ('PEMS') and other similar mobile activity monitoring devices. The package
            includes a dedicated 'pems' data class that manages many of the quality control, unit handling and
            data archiving issues that can hinder efforts to standardise 'PEMS' research.

We aim for fitting a multinomial regression model with Lasso penalty and doing statistical inference (calculating confidence intervals of coefficients and p-values for individual variables). It implements 1) the coordinate descent algorithm to fit an l1-penalized multinomial regression model (parameterized with a reference level); 2) the debiasing approach to obtain the inference results, which is described in "Tian, Y., Rusinek, H., Masurkar, A. V., & Feng, Y. (2024). L1‐Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes. Statistics in Medicine, 43(30), 5711-5747."

The semiparametric accelerated failure time (AFT) model is an attractive alternative to the Cox proportional hazards model. This package provides a suite of functions for fitting one popular rank-based estimator of the semiparametric AFT model, the regularized Gehan estimator. Specifically, we provide functions for cross-validation, prediction, coefficient extraction, and visualizing both trace plots and cross-validation curves. For further details, please see Suder, P. M. and Molstad, A. J., (2022) Scalable algorithms for semiparametric accelerated failure time models in high dimensions, Statistics in Medicine <doi:10.1002/sim.9264>.