• # BayesLogit

• Referenced in 44 articles [sw09312]
• PolyaGamma Sampling. Bayesian inference for logistic models using Pólya-Gamma latent variables. We propose ... data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals...
• # bfa

• Referenced in 23 articles [sw07430]
• package bfa Bayesian Gaussian copula factor models for mixed data. Gaussian factor models have proven ... extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent ... Gaussian measured variables, the latent variables typically influence both the dependence structure and the form ... novel class of Bayesian Gaussian copula factor models that decouple the latent factors from...
• # FastGP

• Referenced in 1 article [sw21250]
• matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz ... normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling...
• # BMRV

• Referenced in 0 articles [sw17527]
• Rare Variant Association Analysis. Provides two Bayesian models for detecting the association between rare genetic ... continuous, ordinal or binary. Bayesian latent variable collapsing model (BLVCM) detects interaction effect ... also be applied to independent samples. Hierarchical Bayesian multiple regression model (HBMR) incorporates genotype uncertainty...
• # GLFM

• Referenced in 1 article [sw35679]
• linear combination of latent variables. These models are often used to make predictions either ... extensive literature on latent feature allocation models for homogeneous datasets, where all the attributes that ... introduce a general Bayesian nonparametric latent feature allocation model suitable for heterogeneous datasets, where ... valued, categorical, ordinal and count variables. The proposed model presents several important properties. First...
• # BayesFM

• Referenced in 2 articles [sw17545]
• variety of factor models. Currently, it includes: Bayesian Exploratory Factor Analysis (befa), an approach ... number of latent factors, as well as the allocation of the manifest variables...
• # AMIDST

• Referenced in 5 articles [sw21741]
• present an approach for scaling up Bayesian learning using variational methods by exploiting distributed computing ... wide range of conjugate exponential family models. We evaluate the proposed algorithm on three real ... using several models (LDA, factor analysis, mixture of Gaussians and linear regression models). Our approach ... billion nodes and approx. $75%$ latent variables using a computer cluster with 128 processing units...
• # UPG

• Referenced in 1 article [sw36813]
• Highly efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions ... Carlo algorithms are based on the latent variable representations and boosting algorithms outlined in Frühwirth...
• # HMSC

• Referenced in 1 article [sw22951]
• uses Bayesian hierarchical modelling to account for environment, traits and phylogeny to model species communities ... include and spatially (or temporally) autocorrelated latent variables to measure association among species. This...
• # binomlogit

• Referenced in 1 article [sw36816]
• binomial (or binary) logit model within a Bayesian framework: a data-augmented independence MH-sampler ... estimated by rewriting the logit model as a latent variable model called difference random utility...
• # BNPMIXcluster

• Referenced in 1 article [sw18028]
• Bayesian nonparametric approach for clustering that is capable to combine different types of variables (continuous ... survey design. The model is based on a location mixture model with a Poisson-Dirichlet ... location parameters of the associated latent variables. The package performs the clustering model described...
• # spBFA

• Referenced in 1 article [sw31476]
• Bayesian non-parametric prior, the probit stick-breaking process. Areal spatial data is modeled using ... Gaussian process. The response variable can be modeled as Gaussian, probit, Tobit, or Binomial (using ... augmentation). Temporal correlation is introduced for the latent factors through a hierarchical structure...
• # iBATCGH

• Referenced in 1 article [sw19388]
• hierarchical Bayesian model. Through the specification of a measurement error model we relate the gene ... expression levels to latent copy number states which, in turn, are related to the observed ... hidden Markov model. Selection of relevant associations is performed employing variable selection priors that explicitly...
• # boral

• Referenced in 0 articles [sw16062]
• package boral. Bayesian approaches for analyzing multivariate data in ecology. Estimation is performed using Markov ... Three types of models may be fitted: 1) With explanatory variables only, boral fits independent ... column of the response matrix; 2) With latent variables only, boral fits a purely latent...
• # bayesMCClust

• Referenced in 0 articles [sw27221]
• observing a categorical variable with several states (in a Bayesian approach). In order to analyze ... approaches by formulating a probabilistic model for the latent group indicators within the Bayesian classification...
• # PottsMix

• Referenced in 1 article [sw32783]
• Bayesian spatial finite mixture model that builds on the mesostate-space model developed by Daunizeau ... NeuroImage 2007; 38, 67-81]. Our new model incorporates two major extensions: (i) We combine ... model for dealing with the two modalities simultaneously; (ii) we incorporate the Potts model ... spatiotemporal model and derive an efficient procedure for simultaneous point estimation and model selection based...