Discrete Laplace Mixture Inference using the EM Algorithm

Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).


Changes in version 1.6.2

  • Minor technical changes for keeping CRAN checks happy

Changes in version 1.6.1

  • Plot functionality for disclapmix was broken for fits with only one subpopulation/cluster.

Changes in version 1.6

  • Plot functionality for disclapmix fit takes an argument for the distances between clusters (defaults to clusterdist(x)). This can be used if the distances have been precalculated.
  • Minor technical changes for keeping CRAN checks happy

Changes in version 1.5

  • Added AICc (Akaike Information Criterium with finite sample correction)
  • Added plot functionality for a disclapmix fit
  • Corrected number of model observations, leading to corrected BIC values (AIC not affected)
  • Error in full likelihood ratio corrected (not used for model selection)

Changes in version 1.4

  • Added separation of two persons mixtures (see example in ?rank_contributor_pairs)
  • Added AIC (Akaike Information Criterium) for a model disclapmixfit

Changes in version 1.3

  • Added simulate from fitted model
  • Added haplotype diversity calculation from a fitted model
  • Added parameter to control number of IRLS iterations (glm_control_maxit)
  • Added parameter to control IRLS convergence (glm_control_eps)
  • Changed default iterations to 100L, glm_control_maxit to 50L and glm_control_eps to 1e-6
  • Fixed error with no IRLS output for verbose = 2L expect for glm.fit
  • Small changes in verbose output

Changes in version 1.2

  • Changed default glm_method to internal_coef due to speed considerations
  • Fixed issues when having one cluster, one locus or both

Changes in version 1.1

  • Code changes for OS X, Windows and Solaris compatibility

Changes in version 1.0

  • Totally rewritten such that the algorithm is more memory and CPU efficient and can be used to analyse larger datasets
  • Internal IRLS algorithm implemented, much faster than glm.fit for this kind of data. glm.fit is still available. See glm_method in ?disclapmix.
  • NOTE: Updated API

Changes in version 0.4

  • Fixed error when there is no variation in a subpopulation

Changes in version 0.3

  • Fixed error such that verbose = 1 outputs progress information
  • Number of model parameters corrected, which changes marginal and full BIC slightly, but only with a constant change meaning that for the same data, the optimal number of centers is the same

Changes in version 0.2 * disclapmix: verbose more fine grained * FIXED: disclapmix: use.parallel = TRUE created problems under Windows * Corrected minor documentation errors

Changes in version 0.1 * Initial release

Reference manual

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1.7 by Mikkel Meyer Andersen, 2 months ago

http://dx.doi.org/10.1016/j.jtbi.2013.03.009 http://arxiv.org/abs/1304.2129

Report a bug at https://github.com/mikldk/disclapmix/issues

Browse source code at https://github.com/cran/disclapmix

Authors: Mikkel Meyer Andersen [aut, cre], Poul Svante Eriksen [aut]

Documentation:   PDF Manual  

GPL (>= 2) | file LICENSE license

Imports Rcpp, disclap, cluster, MASS, stats, graphics, methods, utils

Suggests knitr, ggplot2, gridExtra, ggdendro, scales, seriation, fwsim, testthat

Linking to Rcpp, RcppProgress

System requirements: C++11

See at CRAN