• DPpackage

  • Referenced in 62 articles [sw10495]
  • DPpackage: Bayesian Semi- and Nonparametric Modeling in R. Data analysis sometimes requires the relaxation ... specification of the probability model. In the Bayesian context, this is accomplished by placing ... programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently...
  • BayesTree

  • Referenced in 58 articles [sw07995]
  • posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis...
  • bnpmr

  • Referenced in 19 articles [sw11018]
  • Bayesian monotonic nonparametric regression. Implements the Bayesian nonparametric monotonic regression method described in Bornkamp & Ickstadt...
  • spBayesSurv

  • Referenced in 7 articles [sw16371]
  • models for spatial/non-spatial survival data: marginal Bayesian Nonparametric models, marginal Bayesian proportional hazards models, generalized...
  • beyondWhittle

  • Referenced in 6 articles [sw31734]
  • Stationary Time Series. Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate...
  • dpmixsim

  • Referenced in 3 articles [sw24713]
  • segmentation. The DPM model is a Bayesian nonparametric methodology that relies on MCMC simulations...
  • phylodyn

  • Referenced in 3 articles [sw19813]
  • genealogies. The package main functionality is Bayesian nonparametric estimation of effective population size fluctuations over...
  • dirichletprocess

  • Referenced in 2 articles [sw32614]
  • Process Objects for Bayesian Modelling. Perform nonparametric Bayesian analysis using Dirichlet processes without the need...
  • Bayesian Regression

  • Referenced in 2 articles [sw19268]
  • menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis...
  • ConfBands

  • Referenced in 29 articles [sw12330]
  • methods: (a) as a standard (fixed effect) nonparametric model, (b) using the mixed-model framework ... random effects, and (c) a full Bayesian approach. The volume-of-tube formula is applied...
  • msBP

  • Referenced in 2 articles [sw23268]
  • Multiscale Bernstein Polynomials for Densities. Performs Bayesian nonparametric multiscale density estimation and multiscale testing...
  • BNPmix

  • Referenced in 2 articles [sw28208]
  • process mixture models are flexible Bayesian nonparametric models to deal with density estimation. Estimation could...
  • BNPMIXcluster

  • Referenced in 1 article [sw18028]
  • package BNPMIXcluster. Bayesian Nonparametric Model for Clustering with Mixed Scale Variables. Bayesian nonparametric approach...
  • MixedDataImpute

  • Referenced in 1 article [sw18615]
  • Continuous and Categorical Data using Nonparametric Bayesian Joint Models. Missing data imputation for continuous ... categorical data, using nonparametric Bayesian joint models (specifically the hierarchically coupled mixture model with local...
  • GLFM

  • Referenced in 1 article [sw35679]
  • this paper, we introduce a general Bayesian nonparametric latent feature allocation model suitable for heterogeneous ... attributes per MCMC iteration. Second, the Bayesian nonparametric component allows us to place a prior...
  • bsplinePsd

  • Referenced in 1 article [sw31798]
  • package bsplinePsd: Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors. Implementation of a Metropolis...
  • ParticleMDI

  • Referenced in 1 article [sw35255]
  • subtype identification. We present a novel nonparametric Bayesian approach for performing cluster analysis...
  • CSI

  • Referenced in 1 article [sw13904]
  • nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data. Here...
  • BayClone2

  • Referenced in 1 article [sw31387]
  • data, we characterize tumor heterogeneity using Bayesian nonparametric inference. Specifically, we estimate the number...
  • MOTABAR

  • Referenced in 2 articles [sw12739]
  • Moving Taylor Bayesian regression for nonparametric multidimensional function estimation with possibly correlated errors. We study ... novel nonparametric method for estimating the value and several derivatives of an unknown, sufficiently smooth ... correlation structure. The method, moving Taylor Bayesian regression (MOTABAR), uses Bayesian updating to find...