Showing 4 of total 4 results (show query)
giabaio
survHE:Survival Analysis in Health Economic Evaluation
Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed using 'remotes::install_github' from their GitHub repositories: (<https://github.com/giabaio/survHEhmc> and <https://github.com/giabaio/survHEinla/> respectively). 'survHEinla' is based on the package INLA, which is available for download at <https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). <doi:10.18637/jss.v095.i14>.
Maintained by Gianluca Baio. Last updated 23 days ago.
frequentisthamiltonian-monte-carlohealth-economic-evaluationinlaplotting-survival-curvesrstansurvival-analysissurvival-modelsuncertaintyopenjdk
42 stars 6.88 score 2 dependents
murrayefford
secrdesign:Sampling Design for Spatially Explicit Capture-Recapture
Tools for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing.
Maintained by Murray Efford. Last updated 3 days ago.
4.35 score 56 scripts
kjellpk
fit.models:Compare Fitted Models
The fit.models function and its associated methods (coefficients, print, summary, plot, etc.) were originally provided in the robust package to compare robustly and classically fitted model objects. See chapters 2, 3, and 5 in Insightful (2002) 'Robust Library User's Guide' <http://robust.r-forge.r-project.org/Robust.pdf>). The aim of the fit.models package is to separate this fitted model object comparison functionality from the robust package and to extend it to support fitting methods (e.g., classical, robust, Bayesian, regularized, etc.) more generally.
Maintained by Kjell Konis. Last updated 5 years ago.
3.74 score 45 scripts 10 dependents
cran
FIT:Transcriptomic Dynamics Models in Field Conditions
Provides functionality for constructing statistical models of transcriptomic dynamics in field conditions. It further offers the function to predict expression of a gene given the attributes of samples and meteorological data. Nagano, A. J., Sato, Y., Mihara, M., Antonio, B. A., Motoyama, R., Itoh, H., Naganuma, Y., and Izawa, T. (2012). <doi:10.1016/j.cell.2012.10.048>. Iwayama, K., Aisaka, Y., Kutsuna, N., and Nagano, A. J. (2017). <doi:10.1093/bioinformatics/btx049>.
Maintained by Koji Iwayama. Last updated 6 years ago.
3.00 score