• SemiPar

  • Referenced in 761 articles [sw07116]
  • They refer to this combination as semiparametric regression. The approach to semiparametric regression is based ... scientists with only a moderate background in regression, though familiarity with matrix and linear algebra ... desire to begin using more flexible semiparametric models. There is enough new material...
  • bootlib

  • Referenced in 461 articles [sw40642]
  • extends the basic ideas to several samples, semiparametric and smooth models. Significance and confidence intervals ... deal with resampling methods appropriate for linear regression models, generalized linear models and nonlinear models ... yield improved simulations. Chapter 10 describes various semiparametric versions of the likelihood function...
  • tgp

  • Referenced in 44 articles [sw07921]
  • treed Gaussian process models. Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes...
  • timereg

  • Referenced in 88 articles [sw08068]
  • Regression Models for Survival Data’, Springer Verlag. Plus more recent developments. Additive survival model, semiparametric ... risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling...
  • ICsurv

  • Referenced in 6 articles [sw23156]
  • package ICsurv: A package for semiparametric regression analysis of interval-censored data. Currently using ... model. More methods under other semiparametric regression models will be included in later versions...
  • DPpackage

  • Referenced in 72 articles [sw10495]
  • probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function ... implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models ... characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered...
  • np

  • Referenced in 103 articles [sw10543]
  • package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among ... consistent model specification tests for parametric mean regression models and parametric quantile regression models, among...
  • PSPMCM

  • Referenced in 9 articles [sw27707]
  • macro to estimate parametric and semiparametric mixture cure models with covariates. The cure fraction ... modelled by various binary regression models. Parametric and semiparametric models can be used to model...
  • deepTL

  • Referenced in 2 articles [sw41464]
  • deep learning semiparametric regression for adjusting complex confounding structures. Deep Treatment Learning (deepTL), a robust ... efficient deep learning-based semiparametric regression approach, is proposed to adjust the complex confounding structures ... scheme for flexible function approximation. An improved semiparametric procedure is further developed to enhance...
  • AdaptFit

  • Referenced in 3 articles [sw10486]
  • SemiPar package fits semiparametric regression models with spatially adaptive penalized splines...
  • McSpatial

  • Referenced in 2 articles [sw16522]
  • package McSpatial. Locally weighted regression, semiparametric and conditionally parametric regression, fourier and cubic spline functions...
  • JointModel

  • Referenced in 2 articles [sw38473]
  • Counting Processes. Joint fit of a semiparametric regression model for longitudinal responses and a semiparametric...
  • ICBayes

  • Referenced in 2 articles [sw26412]
  • Data. Contains functions to fit Bayesian semiparametric regression survival models (proportional hazards model, proportional odds...
  • PCDSpline

  • Referenced in 1 article [sw31379]
  • package PCDSpline: Semiparametric regression analysis of panel count data using monotone splines. Semiparametric regression analysis...
  • semibart

  • Referenced in 1 article [sw31324]
  • semiparametric modeling approach using Bayesian additive regression trees with an application to evaluate heterogeneous treatment ... effects. Bayesian Additive Regression Trees (BART) is a flexible machine learning algorithm capable of capturing ... covariates. We extend BART to a semiparametric regression framework in which the conditional expectation ... form. The result is a Bayesian semiparametric linear regression model where the posterior distribution...
  • regpro

  • Referenced in 1 article [sw19267]
  • nonparametric regression (kernel, local linear), (2) semiparametric regression (single index, additive models), and (3) quantile...
  • CVarE

  • Referenced in 2 articles [sw41271]
  • while ECVE generalizes to non-additive error regression models. They operate under the assumption that ... loss of information. It is a semiparametric forward regression model based exhaustive sufficient dimension reduction...
  • FoSIntro

  • Referenced in 2 articles [sw36459]
  • package to: An introduction to semiparametric function-on-scalar regression. Function-on-scalar regression models ... introduction for a flexible semiparametric approach for function-on-scalar regression, using spatially referenced time...
  • semsfa

  • Referenced in 2 articles [sw33881]
  • procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric...
  • plreg

  • Referenced in 2 articles [sw37354]
  • Difference-based semiparametric estimation of partial linear regression models. This article describes the plreg command...