• DPpackage

  • Referenced in 68 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...
  • BartPy

  • Referenced in 73 articles [sw40584]
  • posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis...
  • BayesTree

  • Referenced in 60 articles [sw07995]
  • posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis...
  • bnpmr

  • Referenced in 20 articles [sw11018]
  • Bayesian monotonic nonparametric regression. Implements the Bayesian nonparametric monotonic regression method described in Bornkamp & Ickstadt...
  • spBayesSurv

  • Referenced in 9 articles [sw16371]
  • models for spatial/non-spatial survival data: marginal Bayesian Nonparametric models, marginal Bayesian proportional hazards models, generalized...
  • beyondWhittle

  • Referenced in 8 articles [sw31734]
  • Stationary Time Series. Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate...
  • dirichletprocess

  • Referenced in 4 articles [sw32614]
  • Process Objects for Bayesian Modelling. Perform nonparametric Bayesian analysis using Dirichlet processes without the need...
  • msBP

  • Referenced in 4 articles [sw23268]
  • Multiscale Bernstein Polynomials for Densities. Performs Bayesian nonparametric multiscale density estimation and multiscale testing...
  • BNPMIXcluster

  • Referenced in 2 articles [sw18028]
  • package BNPMIXcluster. Bayesian Nonparametric Model for Clustering with Mixed Scale Variables. Bayesian nonparametric approach...
  • vx_dbel

  • Referenced in 3 articles [sw37455]
  • packages do not sufficiently address K-sample nonparametric comparisons of data distributions, we propose ... exact-test p-value computations. This Bayesian-type method considers tabulated critical values as prior ... likelihood function. In this case, a nonparametric Bayesian method is proposed to compute critical values...
  • dpmixsim

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

  • Referenced in 3 articles [sw28208]
  • process mixture models are flexible Bayesian nonparametric models to deal with density estimation. Estimation could...
  • phylodyn

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

  • Referenced in 31 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...
  • tsBCF

  • Referenced in 2 articles [sw40583]
  • Smooth Bayesian Causal Forests (tsBCF), a nonparametric Bayesian approach for estimating heterogeneous treatment effects which...
  • IMIFA

  • Referenced in 2 articles [sw20290]
  • Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering ... arxiv.org/abs/1701.07010”>arXiv:1701.07010>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance...
  • Bayesian Regression

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

  • Referenced in 2 articles [sw31798]
  • package bsplinePsd: Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors. Implementation of a Metropolis...
  • 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...