This package is considered a duplicate. The official version of this package is found at:https://uscbiostats.r-universe.dev/partition

Package: partition 0.2.2

Malcolm Barrett

partition: Agglomerative Partitioning Framework for Dimension Reduction

A fast and flexible framework for agglomerative partitioning. 'partition' uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. 'partition' is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. 'partition' is based on the Partition framework discussed in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>.

Authors:Joshua Millstein [aut], Malcolm Barrett [aut, cre], Katelyn Queen [aut]

partition_0.2.2.tar.gz
partition_0.2.2.tar.gz(r-4.5-noble)partition_0.2.2.tar.gz(r-4.4-noble)
partition_0.2.2.tgz(r-4.4-emscripten)partition_0.2.2.tgz(r-4.3-emscripten)
partition.pdf |partition.html✨
partition/json (API)
NEWS

# Install 'partition' in R:

install.packages('partition', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/uscbiostats/partition/issues

Pkgdown site:https://uscbiostats.github.io

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

baxter_clinical - Microbiome data
baxter_data_dictionary - Microbiome data
baxter_family - Microbiome data
baxter_genus - Microbiome data
baxter_otu - Microbiome data

openblas cpp

4.26 score 1 packages 27 scripts 328 downloads 2 mentions 50 exports 44 dependencies

Last updated 3 months agofrom:12c9447aa6. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 09 2024
R-4.5-linux-x86_64	OK	Dec 09 2024

Exports:%>%as_director as_measure as_partition_step as_partitioner as_reducer corr direct_distance direct_distance_pearson direct_distance_spearman direct_k_cluster filter_reduced icc is_partition is_partition_step is_partitioner map_cluster map_partition mapping_groups mapping_key measure_icc measure_min_icc measure_min_r2 measure_std_mutualinfo measure_variance_explained mutual_information part_icc part_kmeans part_minr2 part_pc1 part_stdmi partition partition_scores permute_df plot_area_clusters plot_information plot_ncluster plot_permutation plot_stacked_area_clusters reduce_cluster reduce_first_component reduce_kmeans reduce_scaled_mean replace_partitioner scaled_mean simulate_block_data super_partition test_permutation unnest_mappings unnest_reduced

Dependencies:cli colorspace cpp11 crayon dplyr fansi farver forcats generics ggplot2 glue gtable hms infotheo isoband labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme pillar pkgconfig prettyunits progress purrr R6 RColorBrewer Rcpp RcppArmadillo rlang scales stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

Extending partition

Malcolm Barrett

Rendered fromextending-partition.Rmdusingknitr::rmarkdownon Dec 09 2024.

Last update: 2024-05-23
Started: 2019-05-17

Introduction to Partition

Malcolm Barrett

Rendered fromintroduction-to-partition.Rmdusingknitr::rmarkdownon Dec 09 2024.

Last update: 2024-05-23
Started: 2019-05-17

Help page	Topics
Create a custom director	as_director
Create a custom metric	as_measure
Create a partition object from a data frame	as_partition_step
Create a partitioner	as_partitioner
Create a custom reducer	as_reducer
Microbiome data	baxter_clinical baxter_data baxter_data_dictionary baxter_family baxter_genus baxter_otu
Efficiently fit correlation coefficient for matrix or two vectors	corr
Target based on minimum distance matrix	direct_distance direct_distance_pearson direct_distance_spearman
Target based on K-means clustering	direct_k_cluster
Filter the reduced mappings	filter_reduced unnest_reduced
Calculate the intraclass correlation coefficient	icc
Is this object a partition?	is_partition
Is this object a 'partition_step'?	is_partition_step
Is this object a partitioner?	is_partitioner
Map a partition across a range of minimum information	map_partition
Return partition mapping key	mapping_groups mapping_key unnest_mappings
Measure the information loss of reduction using intraclass correlation coefficient	measure_icc
Measure the information loss of reduction using the minimum intraclass correlation coefficient	measure_min_icc
Measure the information loss of reduction using minimum R-squared	measure_min_r2
Measure the information loss of reduction using standardized mutual information	measure_std_mutualinfo
Measure the information loss of reduction using the variance explained.	measure_variance_explained
Calculate the standardized mutual information of a data set	mutual_information
Partitioner: distance, ICC, scaled means	part_icc
Partitioner: K-means, ICC, scaled means	part_kmeans
Partitioner: distance, minimum R-squared, scaled means	part_minr2
Partitioner: distance, first principal component, scaled means	part_pc1
Partitioner: distance, mutual information, scaled means	part_stdmi
Agglomerative partitioning	partition
Return the reduced data from a partition	fitted.partition partition_scores
Permute a data set	permute_df
Plot partitions	plot_area_clusters plot_information plot_ncluster plot_stacked_area_clusters
Plot permutation tests	plot_permutation
Reduce a target	map_cluster reduce_cluster
Reduce selected variables to first principal component	reduce_first_component
Reduce selected variables to scaled means	reduce_kmeans
Reduce selected variables to scaled means	reduce_scaled_mean
Replace the director, metric, or reducer for a partitioner	replace_partitioner
Average and scale rows in a 'data.frame'	scaled_mean
Simulate correlated blocks of variables	simulate_block_data
super_partition	super_partition
Permute partitions	test_permutation

Package: partition 0.2.2

partition: Agglomerative Partitioning Framework for Dimension Reduction

Extending partition

Introduction to Partition

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)