No packages match

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

cpp

13.30 score 67 stars 48 dependents 868 scripts 14k 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

bayesbayesianmarkov-chain-monte-carlomaximum-likelihoodmcmcstanvariational-inference

12.79 score 158 stars 7 dependents 10k scripts

survextrap - Bayesian Flexible Parametric Survival Modelling and Extrapolation

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 prior on the coefficients that controls over-fitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Additive hazards (relative survival) models, waning treatment effects, and mixture cure 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 Jackson (2023) <doi:10.1186/s12874-023-02094-1>.

Last updated

cpp

6.90 score 12 stars 55 scripts 625 downloads

voi - Expected Value of Information

Methods to calculate the expected value of information from a decision-analytic model. This includes the expected value of perfect information (EVPI), partial perfect information (EVPPI) and sample information (EVSI), and the expected net benefit of sampling (ENBS). A range of alternative computational methods are provided under the same user interface. See Heath et al. (2024) <doi:10.1201/9781003156109>, Jackson et al. (2022) <doi:10.1146/annurev-statistics-040120-010730>.

Last updated

6.57 score 8 stars 4 dependents 39 scripts 548 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

cpp

5.17 score 7 stars 6 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

cpp

4.81 score 10 stars 13 scripts 561 downloads

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

cpp

3.18 score 3 stars