Learn R Programming

⚠️There's a newer version (1.1.737) of this package.Take me there.

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

Bugs and new features request

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

Copy Link

Version

Install

install.packages('daltoolbox')

Monthly Downloads

337

Version

1.1.727

License

MIT + file LICENSE

Issues

Pull Requests

Stars

Forks

Maintainer

Eduardo Ogasawara

Last Published

November 25th, 2024

Functions in daltoolbox (1.1.727)

classification

classification
adjust_data.frame

Adjust to data frame
adjust_factor

Adjust factors
cae_encode_decode

Convolutional Autoencoder - Encode
cla_mlp

MLP for classification
cla_majority

Majority Classification
cluster

Cluster
categ_mapping

Categorical mapping
dns_encode

Denoising Autoencoder - Encode
cluster_dbscan

DBSCAN
R2.ts

R2
cae2den_encode_decode

Convolutional 2d Denoising Autoencoder - Encode
dal_transform

DAL Transform
dal_learner

DAL Learner
cla_svm

SVM for classification
aae_encode

Adversarial Autoencoder - Encode
cae_encode

Convolutional Autoencoder - Encode
cla_tune

Classification Tune
dns_encode_decode

Denoising Autoencoder - Encode
cae2den_encode

Convolutional 2d Denoising Autoencoder - Encode
plot_boxplot

Plot boxplot
outliers

Outliers
clu_tune

Clustering Tune
plot_bar

Plot bar graph
clusterer

Clusterer
dal_tune

DAL Tune
plot_radar

Plot radar
reg_rf

Random Forest for regression
reg_mlp

MLP for regression
plot_points

Plot points
fit

Fit
data_sample

Data Sample
dal_base

Class dal_base
dt_pca

PCA
plot_ts_pred

Plot a time series chart with predictions
evaluate

Evaluate
plot_boxplot_class

Boxplot per class
plot_stackedbar

Plot stacked bar
plot_series

Plot series
fit.cluster_dbscan

fit dbscan model
cluster_kmeans

k-means
cla_nb

Naive Bayes Classifier
plot_scatter

Scatter graph
cluster_pam

PAM
cla_rf

Random Forest for classification
regression

Regression
fit_curvature_max

maximum curvature analysis
lae_encode_decode

LSTM Autoencoder - Decode
sMAPE.ts

sMAPE
lae_encode

LSTM Autoencoder - Encode
smoothing_freq

Smoothing by Freq
plot_ts

Plot time series chart
plot_density

Plot density
inverse_transform

Inverse Transform
fit_curvature_min

minimum curvature analysis
fit.cla_tune

tune hyperparameters of ml model
plot_density_class

Plot density per class
sae_encode

Stacked Autoencoder - Encode
plot_pieplot

Plot pie
plot_lollipop

Plot lollipop
reg_svm

SVM for regression
minmax

Min-max normalization
plot_groupedbar

Plot grouped bar
smoothing_inter

Smoothing by interval
ts_head

Extract the First Observations from a ts_data Object
reg_tune

Regression Tune
plot_hist

Plot histogram
ts_knn

KNN time series prediction
transform

Transform
train_test_from_folds

k-fold training and test partition object
set_params

Assign parameters
select_hyper.ts_tune

Select Optimal Hyperparameters for Time Series Models
predictor

DAL Predict
ts_rf

Random Forest
ts_sample

Time Series Sample
zscore

Z-score normalization
ts_tune

Time Series Tune
ts_lstm

LSTM
ts_svm

SVM
ts_mlp

MLP
reg_dtree

Decision Tree for regression
select_hyper

Selection hyper parameters
reg_knn

knn regression
select_hyper.cla_tune

selection of hyperparameters
[.ts_data

Subset Extraction for Time Series Data
sae_encode_decode

Stacked Autoencoder - Encode
set_params.default

Default Assign parameters
ts_data

ts_data
ts_elm

ELM
sin_data

Time series example dataset
sample_stratified

Stratified Random Sampling
train_test

Train-Test Partition
sample_random

Sample Random
ts_reg

TSReg
ts_regsw

TSRegSW
ts_norm_an

Time Series Adaptive Normalization
ts_projection

Time Series Projection
ts_norm_swminmax

Time Series Sliding Window Min-Max
ts_norm_diff

Time Series Diff
varae_encode_decode

Variational Autoencoder - Encode
varae_encode

Variational Autoencoder - Encode
smoothing

Smoothing
ts_norm_gminmax

Time Series Global Min-Max
ts_norm_ean

Time Series Adaptive Normalization (Exponential Moving Average - EMA)
ts_arima

ARIMA
ts_conv1d

Conv1D
smoothing_cluster

Smoothing by cluster
action

Action
action.dal_transform

Action implementation for transform
MSE.ts

MSE
aae_encode_decode

Adversarial Autoencoder - Encode
Boston

Boston Housing Data (Regression)
adjust_class_label

Adjust categorical mapping
adjust_matrix

Adjust to matrix
autoenc_encode

Autoencoder - Encode
autoenc_encode_decode

Autoencoder - Encode-decode
cae2d_encode_decode

Convolutional 2d Autoencoder - Encode
adjust_ts_data

Adjust ts_data
cla_knn

K Nearest Neighbor Classification
cla_dtree

Decision Tree for classification
do_fit

Fit Time Series Model
do_predict

Predict Time Series Model
k_fold

K-fold sampling