• GMRFLib

  • Referenced in 308 articles [sw06641]
  • data, spatio-temporal models, graphical models, and semi-parametric statistics. With so many applications ... GMRFs in complex hierarchical models, in which statistical inference is only possible using Markov Chain...
  • spdep

  • Referenced in 38 articles [sw04578]
  • unweighted SAR and CAR spatial regression models, semi-parametric and Moran eigenvector spatial filtering...
  • ssym

  • Referenced in 6 articles [sw14396]
  • package ssym: Fitting Semi-Parametric log-Symmetric Regression Models ... tools to fit a semi-parametric regression model suitable for analysis of data sets ... response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components ... splines. Supported distributions for the model error include log-normal, log-Student-t, log-power...
  • BayesGESM

  • Referenced in 3 articles [sw22576]
  • BayesGESM: Bayesian Analysis of Generalized Elliptical Semi-Parametric Models and Flexible Measurement Error Models ... based on the Bayesian approach for regression models under the assumption that independent additive errors ... this package are: (i) Generalized elliptical semi-parametric models, where both location and dispersion parameters...
  • frailtySurv

  • Referenced in 4 articles [sw19047]
  • frailtySurv for simulating and fitting semi-parametric shared frailty models is introduced. frailtySurv implements semi ... clustered by the hard drive manufacturer and model...
  • BNSP

  • Referenced in 2 articles [sw24225]
  • package BNSP: Bayesian Non- And Semi-Parametric Model Fitting. Markov chain Monte Carlo algorithms ... semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab for variable selection in mean/variance...
  • LSEbA

  • Referenced in 2 articles [sw08763]
  • estimation by analogy in a semi-parametric model for software cost estimation. The importance ... clearly portrayed by the utilization of several models and methods, appeared ... known techniques, namely the parametric Regression Analysis and the non-parametric Estimation by Analogy. Despite ... this paper, we introduce a semi-parametric technique, called LSEbA that achieves to combine...
  • YPmodel

  • Referenced in 2 articles [sw40189]
  • Short-Term and Long-Term Hazard Ratio Model for Survival Data. Inference procedures accommodate ... ratio patterns with a two-sample semi-parametric model. This model contains the proportional hazards...
  • frailtyEM

  • Referenced in 5 articles [sw19525]
  • functions for fitting shared frailty models with a semi-parametric baseline hazard with the Expectation...
  • truncSP

  • Referenced in 3 articles [sw27005]
  • Package for Estimation of Semi-Parametric Truncated Linear Regression Models. Problems with truncated data occur ... complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent ... designed for the estimation of truncated regression models, have been developed. This paper presents ... estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed...
  • depend.truncation

  • Referenced in 5 articles [sw11100]
  • semiparametric inferences for models based on dependently truncated data. Semi-parametric approaches are based...
  • SemiCompRisks

  • Referenced in 3 articles [sw18177]
  • package SemiCompRisks. Hierarchical Models for Parametric and Semi-Parametric Analyses of Semi-Competing Risks Data ... Parametric and semi-parametric analyses of semi-competing risks/univariate survival data. For semi-competing risks ... data, the package contains implementations of hierarchical models for independent data...
  • NPMLEcmprsk

  • Referenced in 1 article [sw29206]
  • their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described...
  • SmoothHazard

  • Referenced in 4 articles [sw14710]
  • models allow for covariates on each of the 3 transitions. We use either a parametric ... approach with Weibull baseline intensities or a semi-parametric approach with M-splines approximation...
  • sodavis

  • Referenced in 0 articles [sw20294]
  • Logistic Regression, Quadratic Discriminant and General Index Models. Variable and interaction selection are essential ... with the variable selection problem for semi-parametric models with continuous responses...
  • VarReg

  • Referenced in 1 article [sw42075]
  • Regression. Methods for fitting semi-parametric mean and variance models, with normal or censored data...
  • XTCSD

  • Referenced in 3 articles [sw31880]
  • data models with small T and large N by implementing two semi-parametric tests proposed...
  • PAmeasures

  • Referenced in 1 article [sw31556]
  • model. The regression model can be linear or nonlinear, parametric, semi-parametric, or nonparametric ... classical R-squared statistic for a linear model, quantifying the prediction function’s ability...
  • RobMixReg

  • Referenced in 1 article [sw33919]
  • modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They ... different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive...
  • TULIP

  • Referenced in 2 articles [sw28276]
  • user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing ... Functions are included for covariate adjustment, model fitting, cross validation and prediction...