flexsurv - Flexible Parametric Survival and Multi-State Models

Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions. Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard. There are also tools for fitting and predicting from fully parametric multi-state models, based on either cause-specific hazards or mixture models.

Last updated 19 days ago

cpp

13.28 score 57 stars 42 dependents 632 scripts 7.1k downloads

cmdstanr - R Interface to 'CmdStan'

A lightweight interface to 'Stan' <https://mc-stan.org>. The 'CmdStanR' interface is an alternative to 'RStan' that calls the command line interface for compilation and running algorithms instead of interfacing with C++ via 'Rcpp'. This has many benefits including always being compatible with the latest version of Stan, fewer installation errors, fewer unexpected crashes in RStudio, and a more permissive license.

Last updated 3 days ago

bayesbayesianmarkov-chain-monte-carlomaximum-likelihoodmcmcstanvariational-inference

12.61 score 145 stars 8 dependents 5.2k scripts

survextrap - Survival Extrapolation with a Flexible Parametric Model and External Data

Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a hierarchical prior on the coefficients to control overfitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Mixture cure models, additive hazards (relative survival) models and waning treatment effects models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using Stan, and outputs are arranged in a tidy format. See See Jackson (2023) <doi:10.48550/arXiv.2306.03957>.

Last updated 4 months ago

cpp

4.95 score 10 stars 10 scripts

disbayes - Bayesian Multi-State Modelling of Chronic Disease Burden Data

Estimation of incidence and case fatality for a chronic disease, given partial information, using a multi-state model. Given data on age-specific mortality and either incidence or prevalence, Bayesian inference is used to estimate the posterior distributions of incidence, case fatality, and functions of these such as prevalence. The methods are described in Jackson et al. (2023) <doi:10.1093/jrsssa/qnac015>.

Last updated 1 years ago

cpp

4.60 score 8 stars 10 scripts 196 downloads

msmbayes - Bayesian Multi-State Models for Intermittently-Observed Data

Bayesian multi-state models for intermittently-observed data. Markov and phase-type semi-Markov models, and misclassification hidden Markov models.

Last updated 3 months ago

4.26 score 4 stars 3 scripts

a2bcovid - Inferring COVID-19 Transmission Events from Sequence and Location Data

A tool which combines genome sequence and the locations of infected individuals, using a statistical and evolutionary model, to estimate the likelihood that transmission occurred between particular individuals, and then to identify clusters of infections. It is currently designed to apply to COVID-19 infection dynamics on hospital wards.

Last updated 3 years ago

cpp

3.18 score 3 stars