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hesim-dev
hesim:Health Economic Simulation Modeling and Decision Analysis
A modular and computationally efficient R package for parameterizing, simulating, and analyzing health economic simulation models. The package supports cohort discrete time state transition models (Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>, N-state partitioned survival models (Glasziou et al. 1990) <doi:10.1002/sim.4780091106>, and individual-level continuous time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>, encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, <doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table make individual-patient simulation, probabilistic sensitivity analysis, and incorporation of patient heterogeneity fast.
Maintained by Devin Incerti. Last updated 7 months ago.
health-economic-evaluationmicrosimulationsimulation-modelingcpp
67 stars 8.12 score 41 scripts
jluchman
domir:Tools to Support Relative Importance Analysis
Methods to apply decomposition-based relative importance analysis for R functions. This package supports the application of decomposition methods by providing 'lapply'- or 'Map'-like meta-functions that compute dominance analysis (Azen, R., & Budescu, D. V. (2003) <doi:10.1037/1082-989X.8.2.129>; Grömping, U. (2007) <doi:10.1198/000313007X188252>) an extension of Shapley value regression (Lipovetsky, S., & Conklin, M. (2001) <doi:10.1002/asmb.446>) based on the values returned from other functions.
Maintained by Joseph Luchman. Last updated 11 months ago.
dominance-analysisrelative-importanceshapley-valuevariable-importance
5 stars 5.79 score 30 scripts
inlabru-org
dirinla:Hierarchical Bayesian Dirichlet regression models using Integrated Nested Laplace Approximation
The R-package dirinla allows the user to fit models in the compositional data context. In particular, it allows fit Dirichlet regression models using the Integrated Nested Laplace Approximation (INLA) methodology.
Maintained by Joaquín Martínez-Minaya. Last updated 4 months ago.
4 stars 3.89 score 13 scripts