Showing 20 of total 20 results (show query)
therneau
survival:Survival Analysis
Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.
Maintained by Terry M Therneau. Last updated 3 months ago.
400 stars 20.40 score 29k scripts 3.9k dependents
stan-dev
rstanarm:Bayesian Applied Regression Modeling via Stan
Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.
Maintained by Ben Goodrich. Last updated 10 days ago.
bayesianbayesian-data-analysisbayesian-inferencebayesian-methodsbayesian-statisticsmultilevel-modelsrstanrstanarmstanstatistical-modelingcpp
393 stars 15.70 score 5.0k scripts 13 dependents
kkholst
mets:Analysis of Multivariate Event Times
Implementation of various statistical models for multivariate event history data <doi:10.1007/s10985-013-9244-x>. Including multivariate cumulative incidence models <doi:10.1002/sim.6016>, and bivariate random effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>. Modern methods for survival analysis, including regression modelling (Cox, Fine-Gray, Ghosh-Lin, Binomial regression) with fast computation of influence functions.
Maintained by Klaus K. Holst. Last updated 6 hours ago.
multivariate-time-to-eventsurvival-analysistime-to-eventfortranopenblascpp
14 stars 13.45 score 236 scripts 42 dependents
pharmaverse
ggsurvfit:Flexible Time-to-Event Figures
Ease the creation of time-to-event (i.e. survival) endpoint figures. The modular functions create figures ready for publication. Each of the functions that add to or modify the figure are written as proper 'ggplot2' geoms or stat methods, allowing the functions from this package to be combined with any function or customization from 'ggplot2' and other 'ggplot2' extension packages.
Maintained by Daniel D. Sjoberg. Last updated 2 months ago.
78 stars 10.57 score 640 scripts 2 dependents
jmsigner
amt:Animal Movement Tools
Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions.
Maintained by Johannes Signer. Last updated 5 months ago.
41 stars 10.54 score 418 scripts
goranbrostrom
eha:Event History Analysis
Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. Parametric accelerated failure time models for left truncated and right censored data. Proportional hazards models for tabular and register data. Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Broström (2022) <doi:10.1201/9780429503764>.
Maintained by Göran Broström. Last updated 10 months ago.
7 stars 9.76 score 308 scripts 10 dependents
dmphillippo
multinma:Bayesian Network Meta-Analysis of Individual and Aggregate Data
Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.
Maintained by David M. Phillippo. Last updated 3 days ago.
35 stars 9.34 score 163 scripts
finnishcancerregistry
popEpi:Functions for Epidemiological Analysis using Population Data
Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean survival times, relative/net survival (in particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and standardized incidence and mortality ratios, all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') and other computations achieved using 'data.table'.
Maintained by Joonas Miettinen. Last updated 2 months ago.
adjust-estimatesage-adjustingdirect-adjustingepidemiologyindirect-adjustingsurvival
8 stars 8.05 score 117 scripts 1 dependents
nerler
JointAI:Joint Analysis and Imputation of Incomplete Data
Joint analysis and imputation of incomplete data in the Bayesian framework, using (generalized) linear (mixed) models and extensions there of, survival models, or joint models for longitudinal and survival data, as described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>. Incomplete covariates, if present, are automatically imputed. The package performs some preprocessing of the data and creates a 'JAGS' model, which will then automatically be passed to 'JAGS' <https://mcmc-jags.sourceforge.io/> with the help of the package 'rjags'.
Maintained by Nicole S. Erler. Last updated 12 months ago.
bayesiangeneralized-linear-modelsglmglmmimputationimputationsjagsjoint-analysislinear-mixed-modelslinear-regression-modelsmcmc-samplemcmc-samplingmissing-datamissing-valuessurvivalcpp
28 stars 7.30 score 59 scripts 1 dependents
mskcc-epi-bio
tidycmprsk:Competing Risks Estimation
Provides an intuitive interface for working with the competing risk endpoints. The package wraps the 'cmprsk' package, and exports functions for univariate cumulative incidence estimates and competing risk regression. Methods follow those introduced in Fine and Gray (1999) <doi:10.1002/sim.7501>.
Maintained by Daniel D. Sjoberg. Last updated 8 months ago.
23 stars 7.06 score 157 scripts 1 dependents
ddsjoberg
dcurves:Decision Curve Analysis for Model Evaluation
Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes, but often require collection of additional information may be cumbersome to apply to models that yield a continuous result. Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. See the following references for details on the methods: Vickers (2006) <doi:10.1177/0272989X06295361>, Vickers (2008) <doi:10.1186/1472-6947-8-53>, and Pfeiffer (2020) <doi:10.1002/bimj.201800240>.
Maintained by Daniel D. Sjoberg. Last updated 8 months ago.
40 stars 6.77 score 95 scripts
sachsmc
eventglm:Regression Models for Event History Outcomes
A user friendly, easy to understand way of doing event history regression for marginal estimands of interest, including the cumulative incidence and the restricted mean survival, using the pseudo observation framework for estimation. For a review of the methodology, see Andersen and Pohar Perme (2010) <doi:10.1177/0962280209105020> or Sachs and Gabriel (2022) <doi:10.18637/jss.v102.i09>. The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and corrected variance estimation.
Maintained by Michael C Sachs. Last updated 29 days ago.
5 stars 6.33 score 24 scripts 1 dependents
wenjie2wang
dynsurv:Dynamic Models for Survival Data
Time-varying coefficient models for interval censored and right censored survival data including 1) Bayesian Cox model with time-independent, time-varying or dynamic coefficients for right censored and interval censored data studied by Sinha et al. (1999) <doi:10.1111/j.0006-341X.1999.00585.x> and Wang et al. (2013) <doi:10.1007/s10985-013-9246-8>, 2) Spline based time-varying coefficient Cox model for right censored data proposed by Perperoglou et al. (2006) <doi:10.1016/j.cmpb.2005.11.006>, and 3) Transformation model with time-varying coefficients for right censored data using estimating equations proposed by Peng and Huang (2007) <doi:10.1093/biomet/asm058>.
Maintained by Wenjie Wang. Last updated 9 months ago.
bayesiancox-modelinterval-censoredsurvival-analysistime-varyingcpp
7 stars 4.19 score 22 scripts
stc04003
aftgee:Accelerated Failure Time Model with Generalized Estimating Equations
A collection of methods for both the rank-based estimates and least-square estimates to the Accelerated Failure Time (AFT) model. For rank-based estimation, it provides approaches that include the computationally efficient Gehan's weight and the general's weight such as the logrank weight. Details of the rank-based estimation can be found in Chiou et al. (2014) <doi:10.1007/s11222-013-9388-2> and Chiou et al. (2015) <doi:10.1002/sim.6415>. For the least-square estimation, the estimating equation is solved with generalized estimating equations (GEE). Moreover, in multivariate cases, the dependence working correlation structure can be specified in GEE's setting. Details on the least-squares estimation can be found in Chiou et al. (2014) <doi:10.1007/s10985-014-9292-x>.
Maintained by Sy Han Chiou. Last updated 6 months ago.
3 stars 4.00 score 11 scripts 1 dependents
mattiminn
dpasurv:Dynamic Path Analysis of Survival Data via Aalen's Additive Hazards Model
Dynamic path analysis with estimation of the corresponding direct, indirect, and total effects, based on Fosen et al., (2006) <doi:10.1007/s10985-006-9004-2>. The main outcome of interest is a counting process from survival analysis (or recurrent events) data. At each time of event, ordinary linear regression is used to estimate the relation between the covariates, while Aalen's additive hazard model is used for the regression of the counting process on the covariates.
Maintained by Matthias Kormaksson. Last updated 7 months ago.
3.36 score 23 scripts
georgheinze
SurvCorr:Correlation of Bivariate Survival Times
Estimates correlation coefficients with associated confidence limits for bivariate, partially censored survival times. Uses the iterative multiple imputation approach proposed by Schemper, Kaider, Wakounig and Heinze (2013) <doi:10.1002/sim.5874>. Provides a scatterplot function to visualize the bivariate distribution, either on the original time scale or as copula.
Maintained by Georg Heinze. Last updated 2 years ago.
2.70 score 2 scriptssachsmc
drsurv:Doubly Robust Estimation of Survival Differences with Censored Data
An implementation of several doubly robust estimators for the survival difference at a given time point and one more complex doubly robust estimator for the survival curve process. The estimators are doubly robust in the sense that they are consistent if the censoring model is correctly specified for censoring and either the outcome model is correctly specified for confounding or the exposure model is correctly specified for confounding. See <doi:10.48550/arXiv.2310.16207> for more details and examples.
Maintained by Michael C Sachs. Last updated 1 years ago.
1 stars 2.70 score
fchenunswms
coxsei:Fitting a CoxSEI Model
Fit a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.
Maintained by Feng Chen. Last updated 5 years ago.
1.08 score 12 scripts