CRAN

mixdir 0.2.0

Cluster High Dimensional Categorical Datasets

Released Mar 11, 2019 by Constantin Ahlmann-Eltze

Dependencies

extraDistr 1.8.10 Rcpp

Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet (Process) mixture of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used to reduce the dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically infer an appropriate number of latent classes or find k classes, as defined by the user. The model is based on a paper by Dunson and Xing (2009) , but implements a scalable variational inference algorithm so that it is applicable to large datasets. It is described and tested in the accompanying paper by Ahlmann-Eltze and Yau (2018) .

Installation

Maven

This package can be included as a dependency from a Java or Scala project by including the following your project's pom.xml file. Read more about embedding Renjin in JVM-based projects.

<dependencies>
  <dependency>
    <groupId>org.renjin.cran</groupId>
    <artifactId>mixdir</artifactId>
    <version>0.2.0-b1</version>
  </dependency>
</dependencies>
<repositories>
  <repository>
    <id>bedatadriven</id>
    <name>bedatadriven public repo</name>
    <url>https://nexus.bedatadriven.com/content/groups/public/</url>
  </repository>
</repositories>

View build log

Renjin CLI

If you're using Renjin from the command line, you load this library by invoking:

library('org.renjin.cran:mixdir')

Test Results

This package was last tested against Renjin 0.9.2724 on Mar 13, 2019.

• Prediction.finding_the_defining_answers_works
• Prediction.finding_the_most_representative_answers_works
• Prediction.finding_the_most_typical_answers_works
• Prediction.predict_class_works
• Prediction.predict_mixdir_works
• Prediction.predict_mixdir_works_with_DP
• Rcpp_Implementations.expec_log_xij_works
• Rcpp_Implementations.zeta_update_works
• Rcpp_Implementations.zeta_update_works_for_dp
• Variational_Inference.VI_can_handle_missign_values
• Variational_Inference.VI_can_handle_more_complex_models
• Variational_Inference.VI_can_handle_the_mushroom_dataset
• Variational_Inference.VI_works_for_simple_models
• Variational_Inference_DP.VI_DP_can_handle_missing_values
• Variational_Inference_DP.VI_DP_works_for_simple_models
• Variational_Inference_DP.mixdir_can_handle_missing_values_as_category
• Variational_Inference_DP.mixdir_repetitions_selects_the_best_run
• find_predictive_features-examples
• find_typical_features-examples
• mixdir-examples
• mushroom-examples
• predict.mixdir-examples
• testthat

Source

View GitHub Mirror

Release History

• 0.2.0
• 0.1.0