Showing 5 of total 5 results (show query)
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 1 days ago.
multivariate-time-to-eventsurvival-analysistime-to-eventfortranopenblascpp
14 stars 13.45 score 236 scripts 42 dependents
laplacesdemonr
LaplacesDemon:Complete Environment for Bayesian Inference
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
Maintained by Henrik Singmann. Last updated 1 years ago.
93 stars 13.45 score 1.8k scripts 60 dependents
cran
mgcv:Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.
Maintained by Simon Wood. Last updated 1 years ago.
32 stars 12.71 score 17k scripts 7.8k dependents
snoweye
EMCluster:EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution
EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.
Maintained by Wei-Chen Chen. Last updated 7 months ago.
18 stars 7.53 score 123 scripts 2 dependents
steve-the-bayesian
Boom:Bayesian Object Oriented Modeling
A C++ library for Bayesian modeling, with an emphasis on Markov chain Monte Carlo. Although boom contains a few R utilities (mainly plotting functions), its primary purpose is to install the BOOM C++ library on your system so that other packages can link against it.
Maintained by Steven L. Scott. Last updated 1 years ago.
9 stars 4.86 score 57 scripts 7 dependents