%>%                     Pipe
.raster_gdal_datatypes
                        Match sits data types to GDAL data types
:=                      Set by reference in data.table
`sits_labels<-`         Change the labels of a set of time series
cerrado_2classes        Samples of classes Cerrado and Pasture
plot                    Plot time series
plot.classified_image   Plot classified images
plot.keras_model        Plot Keras (deep learning) model
plot.patterns           Plot patterns that describe classes
plot.predicted          Plot time series predictions
plot.probs_cube         Plot probability cubes
plot.raster_cube        Plot RGB data cubes
plot.som_evaluate_cluster
                        Plot confusion between clusters
plot.som_map            Plot a SOM map
plot.uncertainty_cube   Plot uncertainty cubes
point_mt_6bands         A time series sample with data from 2000 to
                        2016
samples_l8_rondonia_2bands
                        Samples of Amazon tropical forest biome for
                        deforestation analysis
samples_modis_4bands    Samples of nine classes for the state of Mato
                        Grosso
sits-package            sits
sits_ResNet             Train ResNet classification models
sits_TempCNN            Train temporal convolutional neural network
                        models
sits_accuracy           Assess classification accuracy (area-weighted
                        method)
sits_bands              Get the names of the bands
sits_bbox               Get the bounding box of the data
sits_classify           Classify time series or data cubes
sits_clustering         Find clusters in time series samples
sits_configuration      Configure parameters for sits package
sits_cube               Create data cubes from image collections
sits_filters            Filter time series and data cubes
sits_formula_linear     Define a linear formula for classification
                        models
sits_formula_logref     Define a loglinear formula for classification
                        models
sits_get_data           Get time series from data cubes and cloud
                        services
sits_impute_linear      Replace NA values with linear interpolation
sits_kfold_validate     Cross-validate time series samples
sits_label_classification
                        Build a labelled image from a probability cube
sits_labels             Get labels associated to a data set
sits_labels_summary     Inform label distribution of a set of time
                        series
sits_merge              Merge two data sets (time series or cubes)
sits_metadata_to_csv    Export a sits tibble metadata to the CSV format
sits_mlp                Train multi-layer perceptron models
sits_mlr                Train multinomial log-linear models
sits_patterns           Find temporal patterns associated to a set of
                        time series
sits_regularize         Build a regular data cube from an irregular one
sits_rfor               Train random forest models
sits_sample             Sample a percentage of a time series
sits_select             Filter bands on a data set (tibble or cube)
sits_smooth             Smooth probability cubes with spatial
                        predictors
sits_som                Use SOM for quality analysis of time series
                        samples
sits_svm                Train support vector machine models
sits_time_series        Get the time series for a row of a sits tibble
sits_timeline           Get timeline of a cube or a set of time series
sits_to_xlsx            Save accuracy assessments as Excel files
sits_to_zoo             Export time series to zoo format
sits_train              Train classification models
sits_twdtw_classify     Find matches between patterns and time series
                        using TWDTW
sits_uncertainty        Estimate classification uncertainty based on
                        probs cube
sits_values             Return the values of a set of time series
sits_view               View data cubes and samples in leaflet
sits_xgboost            Train extreme gradient boosting models
