• # tsbridge

• Referenced in 146 articles [sw12354]
• tsbridge: Calculate normalising constants for Bayesian time series models. The tsbridge package contains a collection ... functions that can be used to estimate normalising constants using the bridge sampler of Meng ... variety of time series Bayesian models, where parameters are estimated using BUGS, and models themselves...
• # glmmAK

• Referenced in 22 articles [sw13218]
• Mixed Models. This package implements maximum-likelihood estimation in the logistic regression with both binary ... Poisson regression (log-linear model). Secondly, Bayesian estimation based on MCMC in the logistic...
• # Amos

• Referenced in 56 articles [sw06515]
• SPSS® Amos enables you to specify, estimate, assess and present models to show hypothesized relationships ... confirm and refine models. Uses Bayesian analysis—to improve estimates of model parameters. Offers various...
• # IBAL

• Referenced in 26 articles [sw08945]
• observations. IBAL also integrates Bayesian parameter estimation and decisiontheoretic utility maximization thoroughly into the framework...
• # bnlearn

• Referenced in 34 articles [sw08265]
• network structure learning, parameter learning and inference. Bayesian network structure learning, parameter learning and inference ... well as support for parameter estimation (maximum likelihood and Bayesian) and inference, conditional probability queries...
• # PicHunter

• Referenced in 53 articles [sw14896]
• PicHunter: Bayesian relevance feedback for image retrieval. This paper describes PicHunter, an image retrieval system ... estimate of the user’s goal image. To accomplish this, PicHunter uses Bayesian learning based ... selections made during a search to estimate the probability associated with each image. These probabilities...
• # HDDM

• Referenced in 8 articles [sw16420]
• HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python. The diffusion model ... each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power ... diffusion model), which allows fast and flexible estimation of the the drift-diffusion model ... Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation...
• # rstanarm

• Referenced in 8 articles [sw15609]
• package rstanarm: Bayesian Applied Regression Modeling via Stan. Estimates pre-compiled regression models using ... Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with...
• # BEAST

• Referenced in 33 articles [sw12588]
• platform program for Bayesian phylogenetic analysis of molecular sequences. It estimates rooted, time-measured phylogenies...
• # dlm

• Referenced in 26 articles [sw04503]
• Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models ... state space models, the Kalman filter for estimation and forecasting in dynamic linear models with ... known parameters, and maximum likelihood estimation. It also presents many specific dynamic linear models particularly ... maximum likelihood estimation and a much more elaborated one on Bayesian inference. The last chapter...
• # pacbpred

• Referenced in 4 articles [sw07805]
• package pacbpred: PAC-Bayesian Estimation and Prediction in Sparse Additive Models. This package is intended ... perform estimation and prediction in high-dimensional additive models, using a sparse PAC-Bayesian point ... Guedj and Alquier (2013), ’PAC-Bayesian Estimation and Prediction in Sparse Additive Models’, Electronic Journal...
• # spatcounts

• Referenced in 6 articles [sw13743]
• implement MCMC algorithms in $R$ for Bayesian estimation. The corresponding R library `spatcounts’ is available ... they can be used in a Bayesian context...