• mclust

  • Referenced in 244 articles [sw00563]
  • Estimation , Normal Mixture Modeling fitted via EM algorithm for Model-Based Clustering, Classification, and Density...
  • flexmix

  • Referenced in 97 articles [sw06087]
  • mixtures of regression models using the EM algorithm. FlexMix provides the E-step...
  • RegEM

  • Referenced in 15 articles [sw04943]
  • Maximization The modules implement the regularized EM algorithm described in T. Schneider, 2001: Analysis ... Climate, 14, 853-871. The EM algorithm for Gaussian data is based on iterated linear ... regression analyses. In the regularized EM algorithm, a regularized estimation method replaces the conditional maximum ... regression parameters in the conventional EM algorithm for Gaussian data. The modules here provide truncated...
  • Stem

  • Referenced in 29 articles [sw12287]
  • spatio-temporal model using the EM algorithm, estimation of the parameter standard errors using...
  • EMMIX

  • Referenced in 19 articles [sw08192]
  • using maximum likelihood via the EM algorithm of Dempster, Laird, and Rubin ... full examination of the EM algorithm and related topics, see McLachlan and Krishnan (1997). Many...
  • FAMT

  • Referenced in 25 articles [sw11123]
  • parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well...
  • HAPLO

  • Referenced in 23 articles [sw04580]
  • HAPLO: A Program Using the EM Algorithm to Estimate the Frequencies of Multi-site Haplotypes...
  • PRISM

  • Referenced in 33 articles [sw23359]
  • examples with the help of the EM learning algorithm. As a knowledge representation language appropriate...
  • EMMIX-skew

  • Referenced in 13 articles [sw07968]
  • EMMIXskew: The EM Algorithm and Skew Mixture Distribution EM algorithm for Mixture of Multivariate Skew...
  • GAKREM

  • Referenced in 10 articles [sw02712]
  • characteristics of the K-means and EM algorithms but avoids their weaknesses such ... goals, genetic algorithms for estimating parameters and initializing starting points for the EM are used ... regression instead of running the conventional EM algorithm until its convergence. Another novelty ... comparing its performance with the conventional EM algorithm, the K-means algorithm, and the likelihood...
  • bivpois

  • Referenced in 15 articles [sw20812]
  • Poisson regression models. An Expectation-Maximization (EM) algorithm is implemented. Inflated models allow for modelling...
  • funHDDC

  • Referenced in 15 articles [sw11130]
  • estimation procedure based on the EM algorithm is proposed for determining both the model parameters...
  • SQUAREM

  • Referenced in 14 articles [sw12282]
  • extrapolation methods for accelerating fixed-point iterations. Algorithms for accelerating the convergence of slow, monotone ... smooth, contraction mapping such as the EM algorithm. It can be used to accelerate...
  • R/qtl

  • Referenced in 13 articles [sw20451]
  • data. We have implemented the main HMM algorithms, with allowance for the presence of genotyping ... scans, by interval mapping (with the EM algorithm), Haley-Knott regression, and multiple imputation...
  • System Identification Toolbox

  • Referenced in 160 articles [sw05686]
  • parametric, subspace-based, and prediction-error algorithms coupled (in the latter case) with either MIMO ... parametrizations, and the employment of Expectation Maximization (EM) methods...
  • admixture

  • Referenced in 5 articles [sw26449]
  • allele frequencies using the expectation maximization (EM) algorithm. (See admixture.barebones.R and admixture.barebones.demo ... improve the very slow convergence of EM. The ADMIXTURE software is implemented using a quasi ... quickly to a solution than the EM algorithm. I’ve modified the model to allow...
  • PSPMCM

  • Referenced in 7 articles [sw27707]
  • parametric models and through an EM algorithm for the Cox’s proportional hazards mixture cure...
  • CHIME

  • Referenced in 4 articles [sw28514]
  • high-dimensional Gaussian mixtures with EM algorithm and its optimality. Unsupervised learning is an important ... CHIME, that is based on the EM algorithm and a direct estimation method ... that is based on the classical EM algorithm...
  • spinyReg

  • Referenced in 4 articles [sw14821]
  • Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high ... likelihood maximization based on an EM algorithm. Model selection is performed afterwards relying on Occam ... path of models found by the EM algorithm. Numerical comparisons between our method, called spinyReg...
  • Mixmod

  • Referenced in 35 articles [sw06991]
  • large variety of algorithms to estimate the mixture parameters are proposed (EM, Classification EM, Stochastic...