person("WPS publisher", role = c("fnd"),
Author: Alexandra Lenhard [aut] (<https://orcid.org/0000-0001-8680-4381>),

Title: Automated C Code Generation for Use with the "deSolve" and
Description: Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". The package also provides functions to symbolically compute Jacobians, sensitivity equations and adjoint sensitivities being the basis for sensitivity analysis.

Title: Automated C Code Generation for Use with the 'deSolve' and
Description: Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". Also provides functions to symbolically compute Jacobians, sensitivity equations and adjoint sensitivities being the basis for sensitivity analysis.

Description: Generates all necessary C functions allowing the user to work with
the compiled-code interface of ode() and bvptwp(). The implementation supports
"forcings" and "events". Also provides functions to symbolically compute
Jacobians, sensitivity equations and adjoint sensitivities being the basis for

Title: Automated C Code Generation for 'deSolve', 'bvpSolve' and
sensitivity analysis. Alternatively to 'deSolve', the Sundials 'CVODES' solver is
implemented for computation of model sensitivities.

Title: Automated C Code Generation for 'deSolve', 'bvpSolve'

Title: Efficient and Customized Preconditioned Conjugate Gradient
Method for Solving System of Linear Equations
Description: Solves system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated 'C++' linear algebra library, and flexibility for user-specified preconditioning method. Please check <https://github.com/styvon/cPCG> for latest updates.

Title: Constructs a Correlated Pseudo-Marginal Sampler
Authors@R: person("Taylor", "Brown", email = "trb5me@virginia.edu",
Description: The primary function makeCPMSampler() generates a sampler function which performs the correlated pseudo-marginal method of Deligiannidis, Doucet and Pitt (2017) <arXiv:1511.04992>. If the 'rho=' argument of makeCPMSampler() is set to 0, then the generated sampler function performs the original pseudo-marginal method of Andrieu and Roberts (2009) <DOI:10.1214/07-AOS574>. The sampler function is constructed with the user's choice of prior, parameter proposal distribution, and the likelihood approximation scheme. Note that this algorithm is not automatically tuned--each one of these arguments must be carefully chosen.
Packaged: 2021-07-07 18:09:13 UTC; clare.obrien.brown