R: Leveraging Experiment Lines to Data Analytics

# Leveraging Experiment Lines to Data Analytics

## Documentation for package ‘daltoolbox’ version 1.3.727

- DESCRIPTION file
- Package README

## Help Pages

A B C D E F H I K M N O P R S T Z

## -- A --

action | Action
action.dal_transform | Action implementation for transform
adjust_class_label | Adjust categorical mapping
adjust_data.frame | Adjust to data frame
adjust_factor | Adjust factors
adjust_matrix | Adjust to matrix
aggregation | Aggregation by groups
autoenc_base_e | Autoencoder base (encoder)
autoenc_base_ed | Autoencoder base (encoder + decoder)

## -- B --

bal_oversampling | Random or SMOTE-based class oversampling
bal_subsampling | Random class undersampling
Boston | Boston Housing Data (Regression)

## -- C --

categ_mapping | Categorical mapping (one‑hot encoding)
classification | Classification base class
cla_bagging | Bagging (ipred)
cla_boosting | Boosting (adabag)
cla_dtree | Decision Tree for classification
cla_glm | Logistic regression (GLM)
cla_glmnet | LASSO logistic regression (glmnet)
cla_knn | K-Nearest Neighbors (KNN) Classification
cla_majority | Majority baseline classifier
cla_mlp | MLP for classification
cla_multinom | Multinomial logistic regression
cla_nb | Naive Bayes Classifier
cla_rf | Random Forest for classification
cla_rpart | CART (rpart)
cla_svm | SVM for classification
cla_tune | Classification tuning (k-fold CV)
cla_xgboost | XGBoost
cluster | Cluster
clusterer | Clusterer
cluster_cmeans | Fuzzy c-means
cluster_dbscan | DBSCAN
cluster_gmm | Gaussian mixture model clustering (GMM)
cluster_hclust | Hierarchical clustering
cluster_kmeans | k-means
cluster_louvain_graph | Louvain community detection
cluster_pam | PAM (Partitioning Around Medoids)
clu_tune | Clustering tuning (intrinsic metric)

## -- D --

dal_base | Class dal_base
dal_graphics | Graphics utilities
dal_learner | DAL Learner (base class)
dal_transform | DAL Transform
dal_tune | DAL Tune (base for hyperparameter search)
data_sample | Data sampling abstractions
discover | Discover
dt_pca | PCA

## -- E --

evaluate | Evaluate

## -- F --

feature_generation | Feature generation
feature_selection_corr | Feature selection by correlation
feature_selection_fss | Feature selection by forward stepwise search
feature_selection_info_gain | Feature selection by information gain
feature_selection_lasso | Feature selection by lasso
feature_selection_relief | Feature selection by RELIEF
feature_selection_stepwise | Feature selection by stepwise model selection
fit | Fit
fit.cla_tune | tune hyperparameters of ml model
fit.cluster_dbscan | fit dbscan model
fit_curvature_max | Maximum curvature analysis (elbow detection)
fit_curvature_min | Minimum curvature analysis (elbow detection)

## -- H --

hierarchy_cut | Hierarchy mapping by cut

## -- I --

imputation_simple | Simple imputation
inverse_transform | Inverse Transform

## -- K --

k_fold | K-fold sampling

## -- M --

minmax | Min-max normalization

## -- N --

na_removal | Missing value removal

## -- O --

outliers_boxplot | Outlier removal by boxplot (IQR rule)
outliers_gaussian | Outlier removal by Gaussian 3-sigma rule

## -- P --

pattern_miner | Pattern miner
pat_apriori | Apriori rules
pat_cspade | cSPADE sequences
pat_eclat | ECLAT itemsets
plot_bar | Plot bar graph
plot_boxplot | Plot boxplot
plot_boxplot_class | Boxplot per class
plot_correlation | Plot correlation
plot_dendrogram | Plot dendrogram
plot_density | Plot density
plot_density_class | Plot density per class
plot_groupedbar | Plot grouped bar
plot_hist | Plot histogram
plot_lollipop | Plot lollipop
plot_pair | Plot scatter matrix
plot_pair_adv | Plot advanced scatter matrix
plot_parallel | Plot parallel coordinates
plot_pieplot | Plot pie
plot_pixel | Plot pixel visualization
plot_points | Plot points
plot_radar | Plot radar
plot_scatter | Scatter graph
plot_series | Plot series
plot_stackedbar | Plot stacked bar
plot_ts | Plot time series chart
plot_ts_pred | Plot time series with predictions
predictor | Predictor (base for classification/regression)

## -- R --

regression | Regression base class
reg_dtree | Decision Tree for regression
reg_knn | K-Nearest Neighbors (KNN) Regression
reg_lm | Linear regression (lm)
reg_mlp | MLP for regression
reg_rf | Random Forest for regression
reg_svm | SVM for regression
reg_tune | Regression tuning (k-fold CV)

## -- S --

sample_balance | Class balancing (up/down sampling)
sample_cluster | Cluster sampling
sample_random | Random sampling
sample_simple | Simple sampling
sample_stratified | Stratified sampling
select_hyper | Selection of hyperparameters
select_hyper.cla_tune | selection of hyperparameters
set_params | Assign parameters
set_params.default | Default Assign parameters
smoothing | Smoothing (binning/quantization)
smoothing_cluster | Smoothing by clustering (k-means)
smoothing_freq | Smoothing by equal frequency
smoothing_inter | Smoothing by equal interval

## -- T --

train_test | Train-Test Partition
train_test_from_folds | k-fold training and test partition object
transform | Transform

## -- Z --

zscore | Z-score normalization