• MIM

  • Referenced in 134 articles [sw26139]
  • describe particular families of models, including log-linear models, Gaussian models, and models for mixed...
  • PROC GENMOD

  • Referenced in 33 articles [sw11741]
  • linear models with normal errors, logistic and probit models for binary data, and log-linear...
  • glmmAK

  • Referenced in 26 articles [sw13218]
  • Package glmmAK: Generalized Linear Mixed Models. This package implements maximum-likelihood estimation in the logistic ... model), and in the Poisson regression (log-linear model). Secondly, Bayesian estimation based on MCMC...
  • nnet

  • Referenced in 16 articles [sw07922]
  • Feed-forward Neural Networks and Multinomial Log-Linear Models. Software for feed-forward neural networks ... single hidden layer, and for multinomial log-linear models...
  • Uhlig Toolkit

  • Referenced in 49 articles [sw15482]
  • dynamic discrete-time stochastic models easily, building on log-linearizing the necessary equations characterizing...
  • gRbase

  • Referenced in 13 articles [sw10198]
  • datasets used in the book Graphical Models with R (2012) are contained in gRbase. gRbase ... conditional independence. gRbase illustrates how hierarchical log-linear models (hllm) may be implemented and describes...
  • CONTRAfold

  • Referenced in 8 articles [sw17117]
  • structure prediction method based on conditional log-linear models (CLLMs), a flexible class of probabilistic...
  • PROC CATMOD

  • Referenced in 4 articles [sw12082]
  • used for linear modeling, log-linear modeling, logistic regression, and repeated measurement analysis. PROC CATMOD ... likelihood (ML) estimation of parameters for log-linear models and the analysis of generalized logits...
  • exactLoglinTest

  • Referenced in 3 articles [sw25926]
  • Exact Hypothesis Tests for Log-linear Models with exactLoglinTest. This manuscript overviews exact testing ... linear models using the R package exactLoglinTest. This package evaluates model fit for Poisson log ... linear models by conditioning on minimal sufficient statistics to remove nuisance parameters. A Monte Carlo ... also be viewed as conditional Poisson log-linear models and hence can be performed...
  • gllm

  • Referenced in 3 articles [sw15073]
  • gllm R package. gllm: Generalised log-linear model. Routines for log-linear models of incomplete...
  • kequate

  • Referenced in 5 articles [sw20025]
  • search for a proper log-linear model in the pre-smoothing step...
  • confreq

  • Referenced in 2 articles [sw11694]
  • antitype. CFA is similar to log-linear modeling. In log-linear modeling the goal ... significant residuals of a log-linear model. The book describes...
  • eMLEloglin

  • Referenced in 2 articles [sw17885]
  • package eMLEloglin. Fitting log-Linear Models in Sparse Contingency Tables. Log-linear modeling...
  • gRim

  • Referenced in 3 articles [sw20816]
  • Models for for contingency tables (i.e. log-linear models) Graphical Gaussian models for multivariate normal...
  • PREG

  • Referenced in 3 articles [sw26380]
  • linear regression model, (ii) a log-linear regression model, and (iii) a nonlinear regression model...
  • algstat

  • Referenced in 3 articles [sw20716]
  • Current applications include exact inference in log-linear models for contingency table data, analysis...
  • AS 207

  • Referenced in 2 articles [sw27891]
  • Algorithm AS 207: Fitting a General Log-Linear Model...
  • multinom

  • Referenced in 2 articles [sw16499]
  • Plus package multinom. Fits multinomial log-linear models via neural networks...
  • gvs_BUGS

  • Referenced in 4 articles [sw26317]
  • within BUGS environment when the number of models is limited. We illustrate the application ... problems including linear regression, log-linear and binomial response models...
  • bayesloglin

  • Referenced in 1 article [sw19810]
  • Data. The function MC3() searches for log-linear models with the highest posterior probability ... from the posterior distribution of the log-linear parameters. The functions findPostMean() and findPostCov() compute ... posterior mean and covariance matrix for decomposable models which, for these models, is available...