Package: ddalpha 1.3.16

ddalpha: Depth-Based Classification and Calculation of Data Depth

Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. (Pokotylo, Mozharovskyi and Dyckerhoff, 2019 <doi:10.18637/jss.v091.i05>).

Authors:Oleksii Pokotylo [aut, cre], Pavlo Mozharovskyi [aut], Rainer Dyckerhoff [aut], Stanislav Nagy [aut]

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ddalpha/json (API)

# Install 'ddalpha' in R:

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

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

baby - Data for Classification
banknoten - Data for Classification
biomed - Data for Classification
bloodtransfusion - Data for Classification
breast_cancer_wisconsin - Data for Classification
bupa - Data for Classification
chemdiab_1vs2 - Data for Classification
chemdiab_1vs3 - Data for Classification
chemdiab_2vs3 - Data for Classification
cloud - Data for Classification
crabB_MvsF - Data for Classification
crabF_BvsO - Data for Classification
crabM_BvsO - Data for Classification
crabO_MvsF - Data for Classification
crab_BvsO - Data for Classification
crab_MvsF - Data for Classification
cricket_CvsP - Data for Classification
diabetes - Data for Classification
ecoli_cpvsim - Data for Classification
ecoli_cpvspp - Data for Classification
ecoli_imvspp - Data for Classification
gemsen_MvsF - Data for Classification
geneexp - Gene Expression Profile Data
glass - Data for Classification
groessen_MvsF - Data for Classification
growth - Berkeley Growth Study Data
haberman - Data for Classification
heart - Data for Classification
hemophilia - Data for Classification
indian_liver_patient_1vs2 - Data for Classification
indian_liver_patient_FvsM - Data for Classification
iris_setosavsversicolor - Data for Classification
iris_setosavsvirginica - Data for Classification
iris_versicolorvsvirginica - Data for Classification
irish_ed_MvsF - Data for Classification
kidney - Data for Classification
medflies - Relationship of Age Patterns of Fecundity to Mortality for Female Medflies.
pima - Data for Classification
plasma_retinol_MvsF - Data for Classification
population - World Historical Population-by-Country Dataset
population2010 - World Historical Population-by-Country Dataset
segmentation - Data for Classification
socmob_IvsNI - Data for Classification
socmob_WvsB - Data for Classification
tae - Data for Classification
tecator - Functional Data Set Spectrometric Data
tennis_MvsF - Data for Classification
tips_DvsN - Data for Classification
tips_MvsF - Data for Classification
uscrime_SvsN - Data for Classification
vertebral_column - Data for Classification
veteran_lung_cancer - Data for Classification
vowel_MvsF - Data for Classification
wine_1vs2 - Data for Classification
wine_1vs3 - Data for Classification
wine_2vs3 - Data for Classification

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

4.84 score 2 stars 7 packages 214 scripts 3.9k downloads 76 exports 13 dependencies

Last updated 1 months agofrom:36b14b5716. Checks:OK: 3 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 31 2024
R-4.5-win-x86_64	OK	Oct 31 2024
R-4.5-linux-x86_64	OK	Oct 31 2024
R-4.4-win-x86_64	NOTE	Oct 31 2024
R-4.4-mac-x86_64	NOTE	Oct 31 2024
R-4.4-mac-aarch64	NOTE	Oct 31 2024
R-4.3-win-x86_64	NOTE	Oct 31 2024
R-4.3-mac-x86_64	NOTE	Oct 31 2024
R-4.3-mac-aarch64	NOTE	Oct 31 2024

Exports:Cmetric compclassf.classify compclassf.train dataf dataf.geneexp dataf.growth dataf.medflies dataf.population dataf.population2010 dataf.sim.1.CFF07 dataf.sim.2.CFF07 dataf.tecator dataf2rawfd ddalpha.classify ddalpha.getErrorRateCV ddalpha.getErrorRatePart ddalpha.test ddalpha.train ddalphaf.classify ddalphaf.getErrorRateCV ddalphaf.getErrorRatePart ddalphaf.test ddalphaf.train depth.depth.betaSkeleton depth.contours depth.contours.ddalpha depth.graph depth.halfspace depth.L2 depth.Mahalanobis depth.potential depth.projection depth.qhpeeling depth.sample depth.simplicial depth.simplicialVolume depth.space.depth.space.halfspace depth.space.Mahalanobis depth.space.potential depth.space.projection depth.space.simplicial depth.space.simplicialVolume depth.space.spatial depth.space.zonoid depth.spatial depth.zonoid depthf.depthf.ABD depthf.BD depthf.fd1 depthf.fd2 depthf.hM depthf.hM2 depthf.HR depthf.RP1 depthf.RP2 depthf.simplicialBand derivatives.est dknn.classify dknn.classify.trained dknn.train draw.ddplot FKS getdata infimalRank is.in.convex L2metric lines.functional plot.functional points.functional rawfd2dataf resetPar shape.fd.analysis shape.fd.outliers

Dependencies:abind BH class DEoptimR geometry linprog lpSolve magic MASS Rcpp RcppProgress robustbase sfsmisc

Help page	Topics
Depth-Based Classification and Calculation of Data Depth	ddalpha-package ddalpha
Fast Computation of the Uniform Metric for Sets of Functional Data	Cmetric
Classify using Functional Componentwise Classifier	compclassf.classify predict.compclassf
Functional Componentwise Classifier	compclassf.train
Using Custom Depth Functions and Classifiers	Custom Methods
Converts data from fdata class to the functional class.	dataf
Functional Data Sets
Gene Expression Profile Data	dataf.geneexp geneexp
Berkeley Growth Study Data	dataf.growth growth
Relationship of Age Patterns of Fecundity to Mortality for Female Medflies.	dataf.medflies medflies
World Historical Population-by-Country Dataset	dataf.population population
World Historical Population-by-Country Dataset (2010 Revision)	dataf.population2010 population2010
Model 1 from Cuevas et al. (2007)	dataf.sim.1.CFF07
Model 2 from Cuevas et al. (2007)	dataf.sim.2.CFF07
Functional Data Set Spectrometric Data (Tecator)	dataf.tecator tecator
Transform a 'dataf' Object to Raw Functional Data	dataf2rawfd
Classify using DD-Classifier	ddalpha.classify predict.ddalpha
Test DD-Classifier	ddalpha.getErrorRateCV
Test DD-Classifier	ddalpha.getErrorRatePart
Test DD-Classifier	ddalpha.test
Train DD-Classifier	alpha ddalpha.train knnlm maxD outsiders polynomial
Classify using Functional DD-Classifier	ddalphaf.classify predict.ddalphaf
Test Functional DD-Classifier	ddalphaf.getErrorRateCV
Test Functional DD-Classifier	ddalphaf.getErrorRatePart
Test Functional DD-Classifier	ddalphaf.test
Functional DD-Classifier	ddalphaf.train
Calculate Depth
Calculate Beta-Skeleton Depth	depth.betaSkeleton
Depth Contours	depth.contours
Depth Contours	depth.contours.ddalpha
Depth Graph	depth.graph
Calculate Halfspace Depth	depth.halfspace
Calculate L2-Depth	depth.L2
Calculate Mahalanobis Depth	depth.Mahalanobis
Calculate Potential of the Data	depth.potential
Calculate Projection Depth	depth.projection
Calculate Convex Hull Peeling Depth	depth.qhpeeling
Fast Depth Computation for Univariate and Bivariate Random Samples	depth.sample
Calculate Simplicial Depth	depth.simplicial
Calculate Simplicial Volume Depth	depth.simplicialVolume
Calculate Depth Space using the Given Depth
Calculate Depth Space using Halfspace Depth	depth.space.halfspace
Calculate Depth Space using Mahalanobis Depth	depth.space.Mahalanobis
Calculate Potential Space	depth.space.potential
Calculate Depth Space using Projection Depth	depth.space.projection
Calculate Depth Space using Simplicial Depth	depth.space.simplicial
Calculate Depth Space using Simplicial Volume Depth	depth.space.simplicialVolume
Calculate Depth Space using Spatial Depth	depth.space.spatial
Calculate Depth Space using Zonoid Depth	depth.space.zonoid
Calculate Spatial Depth	depth.spatial
Calculate Zonoid Depth	depth.zonoid
Calculate Functional Depth
Adjusted Band Depth for Functional Data	depthf.ABD
Band Depth for Functional Data	depthf.BD
Univariate Integrated and Infimal Depth for Functional Data	depthf.fd1
Bivariate Integrated and Infimal Depth for Functional Data	depthf.fd2
h-Mode Depth for Functional Data	depthf.hM
Bivariate h-Mode Depth for Functional Data Based on the L^2 Metric	depthf.hM2
Half-Region Depth for Functional Data	depthf.HR
Univariate Random Projection Depths for Functional Data	depthf.RP1
Bivariate Random Projection Depths for Functional Data	depthf.RP2
Calculate Simplicial Band Depth	depthf.simplicialBand
Estimation of the First Two Derivatives for Functional Data	derivatives.est
Depth-Based kNN	dknn.classify
Depth-Based kNN	dknn.classify.trained
Depth-Based kNN	dknn.train
Draw _DD_-Plot	draw.ddplot
Fast Kernel Smoothing	FKS
Data for Classification	baby banknoten biomed bloodtransfusion breast_cancer_wisconsin bupa chemdiab_1vs2 chemdiab_1vs3 chemdiab_2vs3 cloud crabB_MvsF crabF_BvsO crabM_BvsO crabO_MvsF crab_BvsO crab_MvsF cricket_CvsP data diabetes ecoli_cpvsim ecoli_cpvspp ecoli_imvspp gemsen_MvsF getdata glass groessen_MvsF haberman heart hemophilia indian_liver_patient_1vs2 indian_liver_patient_FvsM irish_ed_MvsF iris_setosavsversicolor iris_setosavsvirginica iris_versicolorvsvirginica kidney pima plasma_retinol_MvsF segmentation socmob_IvsNI socmob_WvsB tae tennis_MvsF tips_DvsN tips_MvsF uscrime_SvsN vertebral_column veteran_lung_cancer vowel_MvsF wine_1vs2 wine_1vs3 wine_2vs3
Adjusted Ranking of Functional Data Based on the Infimal Depth	infimalRank
Check Outsiderness	is.in.convex
Fast Computation of the L^2 Metric for Sets of Functional Data	L2metric
Plots for the "ddalpha" Class	plot.ddalpha
Plots for the "ddalphaf" Class	plot.ddalphaf
Plot functions for the Functional Data	lines.functional plot.functional points.functional
Transform Raw Functional Data to a 'dataf' Object	rawfd2dataf
Reset Graphical Parameters	resetPar
Diagnostic Plot for First and Second Order Integrated and Infimal Depths	shape.fd.analysis
Functional Depth-Based Shape Outlier Detection	shape.fd.outliers

Package: ddalpha 1.3.16

ddalpha: Depth-Based Classification and Calculation of Data Depth

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