R package ramcmc. Robust Adaptive Metropolis Algorithm. Function for adapting the shape of the random walk Metropolis proposal as specified by robust adaptive Metropolis algorithm by Vihola (2012) <<a href=”http://dx.doi.org/10.1007/s11222-011-9269-5”>doi:10.1007/s11222-011-9269-5</a>>. Package also includes fast functions for rank-one Cholesky update and downdate. These functions can be used directly from R or the corresponding C++ header files can be easily linked to other R packages.

References in zbMATH (referenced in 16 articles )

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  1. Chen, Nan; Majda, Andrew J.: Predicting observed and hidden extreme events in complex nonlinear dynamical systems with partial observations and short training time series (2020)
  2. Karimi, Belhal; Lavielle, Marc; Moulines, Eric: f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models (2020)
  3. Kowal, Daniel R.; Canale, Antonio: Simultaneous transformation and rounding (STAR) models for integer-valued data (2020)
  4. Seelig, Stefan A.; Rabe, Maximilian M.; Malem-Shinitski, Noa; Risse, Sarah; Reich, Sebastian; Engbert, Ralf: Bayesian parameter estimation for the Swift model of eye-movement control during reading (2020)
  5. Hart, Joseph L.; Bessac, Julie; Constantinescu, Emil M.: Global sensitivity analysis for statistical model parameters (2019)
  6. Ardia, David; Bluteau, Keven; Hoogerheide, Lennart F.: Methods for computing numerical standard errors: review and application to value-at-risk estimation (2018)
  7. Remo, Flavia; Luboobi, Livingstone S.; Mabalawata, Isambi Sailon; Nannyonga, Betty K.: A mathematical model for the dynamics and MCMC analysis of tomato bacterial wilt disease (2018)
  8. Rosenthal, Jeffrey S.; Yang, Jinyoung: Ergodicity of combocontinuous adaptive MCMC algorithms (2018)
  9. Švendová, Vendula; Schimek, Michael G.: A novel method for estimating the common signals for consensus across multiple ranked lists (2017)
  10. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  11. Avanzi, Benjamin; Taylor, Greg; Vu, Phuong Anh; Wong, Bernard: Stochastic loss reserving with dependence: a flexible multivariate Tweedie approach (2016)
  12. Chen, Yuxin; Keyes, David; Law, Kody J. H.; Ltaief, Hatem: Accelerated dimension-independent adaptive metropolis (2016)
  13. Green, Peter J.; Łatuszyński, Krzysztof; Pereyra, Marcelo; Robert, Christian P.: Bayesian computation: a summary of the current state, and samples backwards and forwards (2015)
  14. Mbalawata, Isambi S.; Särkkä, Simo; Vihola, Matti; Haario, Heikki: Adaptive metropolis algorithm using variational Bayesian adaptive Kalman filter (2015)
  15. Särkkä, Simo; Hartikainen, Jouni; Mbalawata, Isambi Sailon; Haario, Heikki: Posterior inference on parameters of stochastic differential equations via non-linear Gaussian filtering and adaptive MCMC (2015)
  16. Vihola, Matti: Robust adaptive Metropolis algorithm with coerced acceptance rate (2012)