Showing 12 of total 12 results (show query)
cdriveraus
ctsem:Continuous Time Structural Equation Modelling
Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . Stan based functions are not available on 32 bit Windows systems at present. <https://cdriver.netlify.app/> contains some tutorial blog posts.
Maintained by Charles Driver. Last updated 25 days ago.
stochastic-differential-equationstime-seriescpp
42 stars 9.58 score 366 scripts 1 dependents
poissonconsulting
extras:Helper Functions for Bayesian Analyses
Functions to 'numericise' 'R' objects (coerce to numeric objects), summarise 'MCMC' (Monte Carlo Markov Chain) samples and calculate deviance residuals as well as 'R' translations of some 'BUGS' (Bayesian Using Gibbs Sampling), 'JAGS' (Just Another Gibbs Sampler), 'STAN' and 'TMB' (Template Model Builder) functions.
Maintained by Nicole Hill. Last updated 3 months ago.
9 stars 8.49 score 15 scripts 16 dependents
novartis
RBesT:R Bayesian Evidence Synthesis Tools
Tool-set to support Bayesian evidence synthesis. This includes meta-analysis, (robust) prior derivation from historical data, operating characteristics and analysis (1 and 2 sample cases). Please refer to Weber et al. (2021) <doi:10.18637/jss.v100.i19> for details on applying this package while Neuenschwander et al. (2010) <doi:10.1177/1740774509356002> and Schmidli et al. (2014) <doi:10.1111/biom.12242> explain details on the methodology.
Maintained by Sebastian Weber. Last updated 2 months ago.
bayesianclinicalhistorical-datameta-analysiscpp
23 stars 7.94 score 115 scripts 5 dependents
ghislainv
jSDM:Joint Species Distribution Models
Fits joint species distribution models ('jSDM') in a hierarchical Bayesian framework (Warton and al. 2015 <doi:10.1016/j.tree.2015.09.007>). The Gibbs sampler is written in 'C++'. It uses 'Rcpp', 'Armadillo' and 'GSL' to maximize computation efficiency.
Maintained by Ghislain Vieilledent. Last updated 2 years ago.
11 stars 5.87 score 68 scripts
josie-athens
pubh:A Toolbox for Public Health and Epidemiology
A toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. Includes a function to report coefficients and confidence intervals from models using robust standard errors (when available), functions that expand 'ggplot2' plots and functions relevant for introductory papers in Epidemiology or Public Health. Please note that use of the provided data sets is for educational purposes only.
Maintained by Josie Athens. Last updated 6 months ago.
5 stars 5.73 score 72 scripts
glenmartin31
predRupdate:Prediction Model Validation and Updating
Evaluate the predictive performance of an existing (i.e. previously developed) prediction/ prognostic model given relevant information about the existing prediction model (e.g. coefficients) and a new dataset. Provides a range of model updating methods that help tailor the existing model to the new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques to aggregate multiple existing prediction models on the new data are also provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. (2018) <doi:10.1002/sim.7586>).
Maintained by Glen P. Martin. Last updated 7 months ago.
7 stars 5.62 score 9 scripts
staffanbetner
rethinking:Statistical Rethinking book package
Utilities for fitting and comparing models
Maintained by Richard McElreath. Last updated 4 months ago.
5.42 score 4.4k scripts
atlas-aai
dcm2:Calculating the M2 Model Fit Statistic for Diagnostic Classification Models
A collection of functions for calculating the M2 model fit statistic for diagnostic classification models as described by Liu et al. (2016) <DOI:10.3102/1076998615621293>. These functions provide multiple sources of information for model fit according to the M2 statistic, including the M2 statistic, the *p* value for that M2 statistic, and the Root Mean Square Error of Approximation based on the M2 statistic.
Maintained by Jeffrey Hoover. Last updated 11 months ago.
4.18 score 3 scripts 1 dependents
bristol-vaccine-centre
testerror:Uncertainty in Multiplex Panel Testing
Provides methods to support the estimation of epidemiological parameters based on the results of multiplex panel tests.
Maintained by Robert Challen. Last updated 12 months ago.
1 stars 3.40 score 4 scripts
boehringer-ingelheim
BPrinStratTTE:Causal Effects in Principal Strata Defined by Antidrug Antibodies
Bayesian models to estimate causal effects of biological treatments on time-to-event endpoints in clinical trials with principal strata defined by the occurrence of antidrug antibodies. The methodology is based on Frangakis and Rubin (2002) <doi:10.1111/j.0006-341x.2002.00021.x> and Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>, and here adapted to a specific time-to-event setting.
Maintained by Christian Stock. Last updated 12 months ago.
bayesian-methodscausal-inferenceclinical-trialestimandmcmc-methodspharmaceutical-developmentprincipal-stratificationsimulationstantime-to-eventcpp
3.18 score
ghurault
HuraultMisc:Guillem Hurault Functions' Library
Contains various functions for data analysis, notably helpers and diagnostics for Bayesian modelling using Stan.
Maintained by Guillem Hurault. Last updated 4 months ago.
bayesian-statisticsdata-analysisstatistical-models
2.95 score 18 scripts
weberse2
OncoBayes2:Bayesian Logistic Regression for Oncology Dose-Escalation Trials
Bayesian logistic regression model with optional EXchangeability-NonEXchangeability parameter modelling for flexible borrowing from historical or concurrent data-sources. The safety model can guide dose-escalation decisions for adaptive oncology Phase I dose-escalation trials which involve an arbitrary number of drugs. Please refer to Neuenschwander et al. (2008) <doi:10.1002/sim.3230> and Neuenschwander et al. (2016) <doi:10.1080/19466315.2016.1174149> for details on the methodology.
Maintained by Sebastian Weber. Last updated 13 days ago.
2.78 score 15 scripts