This package is considered a duplicate. The official version of this package is found at:https://cefet-rj-dal.r-universe.dev/daltoolbox

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:Eduardo Ogasawara [aut, ths, cre], Antonio Castro [aut, ctb], Heraldo Borges [aut, ths], Janio Lima [aut, ths], Lucas Tavares [aut, ths], Diego Carvalho [aut, ths], Eduardo Bezerra [aut, ths], Joel Santos [aut, ths], Rafaelli Coutinho [aut, ths], Federal Center for Technological Education of Rio de Janeiro [cph]

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

Bug tracker:https://github.com/cefet-rj-dal/daltoolbox/issues

Datasets:

Boston - Boston Housing Data
sin_data - Time series example dataset

4.80 score 4 packages 264 scripts 665 downloads 113 exports 108 dependencies

Last updated 2 days agofrom:b89c55d3dd. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 25 2024
R-4.5-linux	OK	Nov 25 2024

Exports:aae_encode aae_encode_decode action adjust_class_label adjust_data.frame adjust_factor adjust_matrix adjust_ts_data autoenc_encode autoenc_encode_decode cae_encode cae_encode_decode cae2d_encode_decode cae2den_encode cae2den_encode_decode categ_mapping cla_dtree cla_knn cla_majority cla_mlp cla_nb cla_rf cla_svm cla_tune classification clu_tune cluster cluster_dbscan cluster_kmeans cluster_pam clusterer dal_base dal_learner dal_transform dal_tune data_sample dns_encode_decode do_fit do_predict dt_pca evaluate fit fit_curvature_max fit_curvature_min inverse_transform k_fold lae_encode lae_encode_decode minmax MSE.ts outliers plot_bar plot_boxplot plot_boxplot_class plot_density plot_density_class plot_groupedbar plot_hist plot_lollipop plot_pieplot plot_points plot_radar plot_scatter plot_series plot_stackedbar plot_ts plot_ts_pred predictor R2.ts reg_dtree reg_knn reg_mlp reg_rf reg_svm reg_tune regression sae_encode sae_encode_decode sample_random sample_stratified select_hyper set_params sMAPE.ts smoothing smoothing_cluster smoothing_freq smoothing_inter train_test train_test_from_folds transform ts_arima ts_conv1d ts_data ts_elm ts_head ts_knn ts_lstm ts_mlp ts_norm_an ts_norm_diff ts_norm_ean ts_norm_gminmax ts_norm_swminmax ts_projection ts_reg ts_regsw ts_rf ts_sample ts_svm ts_tune varae_encode varae_encode_decode zscore

Dependencies:bitops caret caTools class cli clock cluster codetools colorspace cpp11 curl data.table dbscan diagram digest dplyr e1071 elmNNRcpp fansi farver FNN foreach forecast fracdiff future future.apply generics ggplot2 globals glue gower gplots gtable gtools hardhat here ipred isoband iterators jsonlite KernelKnn KernSmooth labeling lattice lava lifecycle listenv lmtest lubridate magrittr MASS Matrix mgcv MLmetrics ModelMetrics munsell nlme nnet numDeriv parallelly pillar pkgconfig plyr png pROC prodlim progressr proxy purrr quadprog quantmod R6 randomForest rappdirs RColorBrewer Rcpp RcppArmadillo RcppTOML recipes reshape reshape2 reticulate rlang ROCR rpart rprojroot scales shape SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tree tseries TTR tzdb urca utf8 vctrs viridisLite withr xts zoo

Help page	Topics
Subset Extraction for Time Series Data	[.ts_data
Adversarial Autoencoder - Encode	aae_encode
Adversarial Autoencoder - Encode	aae_encode_decode
Action	action
Action implementation for transform	action.dal_transform
Adjust categorical mapping	adjust_class_label
Adjust to data frame	adjust_data.frame
Adjust factors	adjust_factor
Adjust to matrix	adjust_matrix
Adjust 'ts_data'	adjust_ts_data
Autoencoder - Encode	autoenc_encode
Autoencoder - Encode-decode	autoenc_encode_decode
Boston Housing Data (Regression)	Boston
Convolutional Autoencoder - Encode	cae_encode
Convolutional Autoencoder - Encode	cae_encode_decode
Convolutional 2d Autoencoder - Encode	cae2d_encode_decode
Convolutional 2d Denoising Autoencoder - Encode	cae2den_encode
Convolutional 2d Denoising Autoencoder - Encode	cae2den_encode_decode
Categorical mapping	categ_mapping
Decision Tree for classification	cla_dtree
K Nearest Neighbor Classification	cla_knn
Majority Classification	cla_majority
MLP for classification	cla_mlp
Naive Bayes Classifier	cla_nb
Random Forest for classification	cla_rf
SVM for classification	cla_svm
Classification Tune	cla_tune
classification	classification
Clustering Tune	clu_tune
Cluster	cluster
DBSCAN	cluster_dbscan
k-means	cluster_kmeans
PAM	cluster_pam
Clusterer	clusterer
Class dal_base	dal_base
DAL Learner	dal_learner
DAL Transform	dal_transform
DAL Tune	dal_tune
Data Sample	data_sample
Denoising Autoencoder - Encode	dns_encode
Denoising Autoencoder - Encode	dns_encode_decode
Fit Time Series Model	do_fit
Predict Time Series Model	do_predict
PCA	dt_pca
Evaluate	evaluate
Fit	fit
maximum curvature analysis	fit_curvature_max
minimum curvature analysis	fit_curvature_min
tune hyperparameters of ml model	fit.cla_tune
fit dbscan model	fit.cluster_dbscan
Inverse Transform	inverse_transform
K-fold sampling	k_fold
LSTM Autoencoder - Encode	lae_encode
LSTM Autoencoder - Decode	lae_encode_decode
Min-max normalization	minmax
MSE	MSE.ts
Outliers	outliers
Plot bar graph	plot_bar
Plot boxplot	plot_boxplot
Boxplot per class	plot_boxplot_class
Plot density	plot_density
Plot density per class	plot_density_class
Plot grouped bar	plot_groupedbar
Plot histogram	plot_hist
Plot lollipop	plot_lollipop
Plot pie	plot_pieplot
Plot points	plot_points
Plot radar	plot_radar
Scatter graph	plot_scatter
Plot series	plot_series
Plot stacked bar	plot_stackedbar
Plot time series chart	plot_ts
Plot a time series chart with predictions	plot_ts_pred
DAL Predict	predictor
R2	R2.ts
Decision Tree for regression	reg_dtree
knn regression	reg_knn
MLP for regression	reg_mlp
Random Forest for regression	reg_rf
SVM for regression	reg_svm
Regression Tune	reg_tune
Regression	regression
Stacked Autoencoder - Encode	sae_encode
Stacked Autoencoder - Encode	sae_encode_decode
Sample Random	sample_random
Stratified Random Sampling	sample_stratified
Selection hyper parameters	select_hyper
selection of hyperparameters	select_hyper.cla_tune
Select Optimal Hyperparameters for Time Series Models	select_hyper.ts_tune
Assign parameters	set_params
Default Assign parameters	set_params.default
Time series example dataset	sin_data
sMAPE	sMAPE.ts
Smoothing	smoothing
Smoothing by cluster	smoothing_cluster
Smoothing by Freq	smoothing_freq
Smoothing by interval	smoothing_inter
Train-Test Partition	train_test
k-fold training and test partition object	train_test_from_folds
Transform	transform
ARIMA	ts_arima
Conv1D	ts_conv1d
ts_data	ts_data
ELM	ts_elm
Extract the First Observations from a 'ts_data' Object	ts_head
KNN time series prediction	ts_knn
LSTM	ts_lstm
MLP	ts_mlp
Time Series Adaptive Normalization	ts_norm_an
Time Series Diff	ts_norm_diff
Time Series Adaptive Normalization (Exponential Moving Average - EMA)	ts_norm_ean
Time Series Global Min-Max	ts_norm_gminmax
Time Series Sliding Window Min-Max	ts_norm_swminmax
Time Series Projection	ts_projection
TSReg	ts_reg
TSRegSW	ts_regsw
Random Forest	ts_rf
Time Series Sample	ts_sample
SVM	ts_svm
Time Series Tune	ts_tune
Variational Autoencoder - Encode	varae_encode
Variational Autoencoder - Encode	varae_encode_decode
Z-score normalization	zscore

Package: daltoolbox 1.1.727

daltoolbox: Leveraging Experiment Lines to Data Analytics

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)