CRAN
modi 0.1.0
Multivariate Outlier Detection and Imputation for Incomplete Survey Data
Released Nov 20, 2018 by Martin Sterchi
Dependencies
Algorithms for multivariate outlier detection when missing values occur. Algorithms are based on Mahalanobis distance or data depth. Imputation is based on the multivariate normal model or uses nearest neighbour donors. The algorithms take sample designs, in particular weighting, into account. The methods are described in Bill and Hulliger (2016)
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>modi</artifactId>
<version>0.1.0-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>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:modi')
Test Results
This package was last tested against Renjin 0.9.2710 on Nov 22, 2018.
- BEM-examples
- BEM.Length_of_output_vector_is_equal_to_no__of_rows_of_input_data
- EAdet-examples
- EAdet.Length_of_vector_with_infected_nodes_is_equal_to_no__of_rows_of_input_data
- EAimp-examples
- EAimp.Dimension_of_imputed_data_is_equal_to_dimension_of_input_data
- ER-examples
- ER.Sum_of_good_observations_and_outliers_is_equal_to_number_of_rows_of_data
- GIMCD-examples
- MDmiss-examples
- Mahalanobis_distance_with_missing_values.MDmiss_without_missings_outputs_the_same_result_as_mahalanobis_(stats)
- POEM-examples
- POEM.Dimension_of_imputed_data_is_equal_to_dimension_of_input_data
- PlotMD-examples
- TRC-examples
- TRC.Length_of_output_vector_is_equal_to_no__of_rows_of_input_data
- Weighted_Quantile.weighted_quantile_by_default_returns_median
- Weighted_Quantile.weighted_quantile_equals_quantile_if_weights_are_missing
- Weighted_Quantile.weighted_quantile_reacts_correctly_if_x_contains_NA
- Weighted_Variance.weighted_var_equals_var_if_weights_are_missing
- Weighted_Variance.weighted_var_reacts_correctly_if_x_contains_NA_E1
- Weighted_Variance.weighted_var_reacts_correctly_if_x_contains_NA_E2
- Winsimp-examples
- bushfire-examples
- bushfire.weights-examples
- bushfirem-examples
- sepe-examples
- testthat