Package: daltoolbox 1.1.727
Eduardo Ogasawara
daltoolbox: Leveraging Experiment Lines to Data Analytics
The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.
Authors:
daltoolbox_1.1.727.tar.gz
daltoolbox_1.1.727.tar.gz(r-4.5-noble)daltoolbox_1.1.727.tar.gz(r-4.4-noble)
daltoolbox_1.1.727.tgz(r-4.4-emscripten)daltoolbox_1.1.727.tgz(r-4.3-emscripten)
daltoolbox.pdf |daltoolbox.html✨
daltoolbox/json (API)
| # Install 'daltoolbox' in R: |
| install.packages('daltoolbox', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/cefet-rj-dal/daltoolbox/issues0 issues
Last updated 4 months agofrom:b89c55d3dd. Checks:2 OK, 1 NOTE. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 25 2025 |
| R-4.5-linux | NOTE | Mar 25 2025 |
| R-4.4-linux | OK | Mar 25 2025 |
Exports:aae_encodeaae_encode_decodeactionadjust_class_labeladjust_data.frameadjust_factoradjust_matrixadjust_ts_dataautoenc_encodeautoenc_encode_decodecae_encodecae_encode_decodecae2d_encode_decodecae2den_encodecae2den_encode_decodecateg_mappingcla_dtreecla_knncla_majoritycla_mlpcla_nbcla_rfcla_svmcla_tuneclassificationclu_tuneclustercluster_dbscancluster_kmeanscluster_pamclustererdal_basedal_learnerdal_transformdal_tunedata_sampledns_encode_decodedo_fitdo_predictdt_pcaevaluatefitfit_curvature_maxfit_curvature_mininverse_transformk_foldlae_encodelae_encode_decodeminmaxMSE.tsoutliersplot_barplot_boxplotplot_boxplot_classplot_densityplot_density_classplot_groupedbarplot_histplot_lollipopplot_pieplotplot_pointsplot_radarplot_scatterplot_seriesplot_stackedbarplot_tsplot_ts_predpredictorR2.tsreg_dtreereg_knnreg_mlpreg_rfreg_svmreg_tuneregressionsae_encodesae_encode_decodesample_randomsample_stratifiedselect_hyperset_paramssMAPE.tssmoothingsmoothing_clustersmoothing_freqsmoothing_intertrain_testtrain_test_from_foldstransformts_arimats_conv1dts_datats_elmts_headts_knnts_lstmts_mlpts_norm_ants_norm_diffts_norm_eants_norm_gminmaxts_norm_swminmaxts_projectionts_regts_regswts_rfts_samplets_svmts_tunevarae_encodevarae_encode_decodezscore
Dependencies:bitopscaretcaToolsclasscliclockclustercodetoolscolorspacecpp11curldata.tabledbscandiagramdigestdplyre1071elmNNRcppfansifarverFNNforeachforecastfracdifffuturefuture.applygenericsggplot2globalsgluegowergplotsgtablegtoolshardhathereipredisobanditeratorsjsonliteKernelKnnKernSmoothlabelinglatticelavalifecyclelistenvlmtestlubridatemagrittrMASSMatrixmgcvMLmetricsModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpngpROCprodlimprogressrproxypurrrquadprogquantmodR6randomForestrappdirsRColorBrewerRcppRcppArmadilloRcppTOMLrecipesreshapereshape2reticulaterlangROCRrpartrprojrootscalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetreetseriesTTRtzdburcautf8vctrsviridisLitewithrxtszoo
Citation
To cite package ‘daltoolbox’ in publications use:
Ogasawara E, Castro A, Borges H, Lima J, Tavares L, Carvalho D, Bezerra E, Santos J, Coutinho R (2024). daltoolbox: Leveraging Experiment Lines to Data Analytics. R package version 1.1.727, https://CRAN.R-project.org/package=daltoolbox.
Corresponding BibTeX entry:
@Manual{,
title = {daltoolbox: Leveraging Experiment Lines to Data
Analytics},
author = {Eduardo Ogasawara and Antonio Castro and Heraldo Borges
and Janio Lima and Lucas Tavares and Diego Carvalho and Eduardo
Bezerra and Joel Santos and Rafaelli Coutinho},
year = {2024},
note = {R package version 1.1.727},
url = {https://CRAN.R-project.org/package=daltoolbox},
}
Readme and manuals
DAL Toolbox
The goal of DAL Toolbox is to provide a series data analytics functions organized as a framework. It supports data preprocessing, classification, regression, clustering, and time series prediction functions.
Installation
The latest version of DAL Toolbox at CRAN is available at: https://CRAN.R-project.org/package=daltoolbox
You can install the stable version of DAL Toolbox from CRAN with:
install.packages("daltoolbox")
You can install the development version of DAL Toolbox from GitHub https://github.com/cefet-rj-dal/daltoolbox with:
library(devtools)
devtools::install_github("cefet-rj-dal/daltoolbox", force=TRUE, dependencies=FALSE, upgrade="never")
Examples
Classification: https://github.com/cefet-rj-dal/daltoolbox/tree/main/classification/
Clustering: https://github.com/cefet-rj-dal/daltoolbox/tree/main/clustering/
Graphics: https://github.com/cefet-rj-dal/daltoolbox/tree/main/graphics/
Regression: https://github.com/cefet-rj-dal/daltoolbox/tree/main/regression/
Time series: https://github.com/cefet-rj-dal/daltoolbox/tree/main/timeseries/
Transformation: https://github.com/cefet-rj-dal/daltoolbox/tree/main/transf/
The examples are organized according to general (data preprocessing), clustering, classification, regression, and time series functions. This version has Python integration with Pytorch.
library(daltoolbox)
#> Registered S3 method overwritten by 'quantmod':
#> method from
#> as.zoo.data.frame zoo
#>
#> Attaching package: 'daltoolbox'
#> The following object is masked from 'package:base':
#>
#> transform
## loading DAL Toolbox
