cerrado_2classes        Samples of classes Cerrado and Pasture
hist.probs_cube         histogram of prob cubes
hist.raster_cube        histogram of data cubes
hist.sits               Histogram
hist.uncertainty_cube   Histogram uncertainty cubes
impute_linear           Replace NA values by linear interpolation
impute_mean             Remove NA using mean
impute_mean_window      Remove NA using weighted moving average
impute_median           Remove NA using median
plot                    Plot time series and data cubes
plot.class_cube         Plot classified images
plot.class_vector_cube
                        Plot Segments
plot.dem_cube           Plot DEM cubes
plot.geo_distances      Make a kernel density plot of samples
                        distances.
plot.patterns           Plot patterns that describe classes
plot.predicted          Plot time series predictions
plot.probs_cube         Plot probability cubes
plot.probs_vector_cube
                        Plot probability vector cubes
plot.raster_cube        Plot RGB data cubes
plot.rfor_model         Plot Random Forest model
plot.sar_cube           Plot SAR data cubes
plot.sits_accuracy      Plot confusion matrix
plot.sits_cluster       Plot a dendrogram cluster
plot.sits_model         Message for models whose plots are not
                        available
plot.som_clean_samples
                        Plot SOM samples evaluated
plot.som_evaluate_cluster
                        Plot confusion between clusters
plot.som_map            Plot a SOM map
plot.torch_model        Plot Torch (deep learning) model
plot.uncertainty_cube   Plot uncertainty cubes
plot.uncertainty_vector_cube
                        Plot uncertainty vector cubes
plot.variance_cube      Plot variance cubes
plot.vector_cube        Plot RGB vector data cubes
plot.xgb_model          Plot XGB model
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_ndvi      Samples of nine classes for the state of Mato
                        Grosso
sits-package            sits
sits_accuracy           Assess classification accuracy
sits_add_base_cube      Add base maps to a time series data cube
sits_apply              Apply a function on a set of time series
sits_as_sf              Return a sits_tibble or raster_cube as an sf
                        object.
sits_as_stars           Convert a data cube into a stars object
sits_as_terra           Convert a data cube into a Spatial Raster
                        object from terra
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_classify.raster_cube
                        Classify a regular raster cube
sits_classify.sits      Classify a set of time series
sits_classify.vector_cube
                        Classify a segmented data cube
sits_clean              Cleans a classified map using a local window
sits_cluster_clean      Removes labels that are minority in each
                        cluster.
sits_cluster_dendro     Find clusters in time series samples
sits_cluster_frequency
                        Show label frequency in each cluster produced
                        by dendrogram analysis
sits_colors             Function to retrieve sits color table
sits_colors_qgis        Function to save color table as QML style for
                        data cube
sits_colors_reset       Function to reset sits color table
sits_colors_set         Function to set sits color table
sits_colors_show        Function to show colors in SITS
sits_combine_predictions
                        Estimate ensemble prediction based on list of
                        probs cubes
sits_confidence_sampling
                        Suggest high confidence samples to increase the
                        training set.
sits_config             Configure parameters for sits package
sits_config_show        Show current sits configuration
sits_config_user_file   Create a user configuration file.
sits_cube               Create data cubes from image collections
sits_cube.local_cube    Create sits cubes from cubes in flat files in a
                        local
sits_cube.results_cube
                        Create a results cube from local files
sits_cube.stac_cube     Create data cubes from image collections
                        accessible by STAC
sits_cube.vector_cube   Create a vector cube from local files
sits_cube_copy          Copy the images of a cube to a local directory
sits_factory_function   Create a closure for calling functions with and
                        without data
sits_filter             Filter time series with smoothing filter
sits_formula_linear     Define a linear formula for classification
                        models
sits_formula_logref     Define a loglinear formula for classification
                        models
sits_geo_dist           Compute the minimum distances among samples and
                        prediction points.
sits_get_class          Get values from classified maps
sits_get_data           Get time series from data cubes and cloud
                        services
sits_get_data.csv       Get time series using CSV files
sits_get_data.data.frame
                        Get time series using sits objects
sits_get_data.sf        Get time series using sf objects
sits_get_data.shp       Get time series using shapefiles
sits_get_data.sits      Get time series using sits objects
sits_get_probs          Get values from probability maps
sits_impute             Replace NA values in time series with
                        imputation function
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<-           Change the labels of a set of time series
sits_labels<-.class_cube
                        Change the labels of a classified raster cube
sits_labels<-.class_vector_cube
                        Change the labels of a class vector data cube
sits_labels<-.default   Change the labels of other data structures
sits_labels<-.probs_cube
                        Change the labels of a probs raster cube
sits_labels<-.probs_vector_cube
                        Change the labels of a probs vector data cube
sits_labels<-.sits      Change the labels of a set of time series
sits_labels_summary     Inform label distribution of a set of time
                        series
sits_lightgbm           Train light gradient boosting model
sits_lighttae           Train a model using Lightweight Temporal
                        Self-Attention Encoder
sits_list_collections   List the cloud collections supported by sits
sits_lstm_fcn           Train a Long Short Term Memory Fully
                        Convolutional Network
sits_merge              Merge two data sets (time series or cubes)
sits_mgrs_to_roi        Convert MGRS tile information to ROI in WGS84
sits_mixture_model      Multiple endmember spectral mixture analysis
sits_mlp                Train multi-layer perceptron models using torch
sits_model_export       Export classification models
sits_mosaic             Mosaic classified cubes
sits_patterns           Find temporal patterns associated to a set of
                        time series
sits_pred_features      Obtain numerical values of predictors for time
                        series samples
sits_pred_normalize     Normalize predictor values
sits_pred_references    Obtain categorical id and predictor labels for
                        time series samples
sits_pred_sample        Obtain a fraction of the predictors data frame
sits_predictors         Obtain predictors for time series samples
sits_reclassify         Reclassify a classified cube
sits_reduce             Reduces a cube or samples from a summarization
                        function
sits_reduce_imbalance   Reduce imbalance in a set of samples
sits_regularize         Build a regular data cube from an irregular one
sits_resnet             Train ResNet classification models
sits_rfor               Train random forest models
sits_roi_to_mgrs        Given a ROI, find MGRS tiles intersecting it.
sits_roi_to_tiles       Find tiles of a given ROI and Grid System
sits_run_examples       Informs if sits examples should run
sits_run_tests          Informs if sits tests should run
sits_sample             Sample a percentage of a time series
sits_sampling_design    Allocation of sample size to strata
sits_segment            Segment an image
sits_select             Filter a data set (tibble or cube) for bands,
                        tiles, and dates
sits_sgolay             Filter time series with Savitzky-Golay filter
sits_slic               Segment an image using SLIC
sits_smooth             Smooth probability cubes with spatial
                        predictors
sits_snic               Segment an image using SNIC
sits_som_clean_samples
                        Cleans the samples based on SOM map information
sits_som_evaluate_cluster
                        Evaluate cluster
sits_som_map            Build a SOM for quality analysis of time series
                        samples
sits_som_remove_samples
                        Evaluate cluster
sits_stats              Obtain statistics for all sample bands
sits_stratified_sampling
                        Allocation of sample size to strata
sits_svm                Train support vector machine models
sits_tae                Train a model using Temporal Self-Attention
                        Encoder
sits_tempcnn            Train temporal convolutional neural network
                        models
sits_texture            Apply a set of texture measures on a data cube.
sits_tiles_to_roi       Convert MGRS tile information to ROI in WGS84
sits_timeline           Get timeline of a cube or a set of time series
sits_timeseries_to_csv
                        Export a a full sits tibble to the CSV format
sits_to_csv             Export a sits tibble metadata to the CSV format
sits_to_xlsx            Save accuracy assessments as Excel files
sits_train              Train classification models
sits_tuning             Tuning machine learning models hyper-parameters
sits_tuning_hparams     Tuning machine learning models hyper-parameters
sits_uncertainty        Estimate classification uncertainty based on
                        probs cube
sits_uncertainty_sampling
                        Suggest samples for enhancing classification
                        accuracy
sits_validate           Validate time series samples
sits_variance           Calculate the variance of a probability cube
sits_view               View data cubes and samples in leaflet
sits_whittaker          Filter time series with whittaker filter
sits_xgboost            Train extreme gradient boosting models
summary.class_cube      Summarize data cubes
summary.raster_cube     Summarize data cubes
summary.sits            Summarize sits
summary.sits_accuracy   Summarize accuracy matrix for training data
summary.sits_area_accuracy
                        Summarize accuracy matrix for area data
summary.variance_cube   Summarize variance cubes
