| sits-package | sits |
| cerrado_2classes | Samples of classes Cerrado and Pasture |
| plot | Plot time series |
| plot.class_cube | Plot classified images |
| 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.raster_cube | Plot RGB data cubes |
| plot.rfor_model | Plot Random Forest model |
| plot.sits | Plot time series |
| plot.sits_accuracy | Plot confusion matrix |
| 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.variance_cube | Plot variance 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 | sits |
| sits_accuracy | Assess classification accuracy (area-weighted method) |
| sits_accuracy.class_cube | Assess classification accuracy (area-weighted method) |
| sits_accuracy.sits | Assess classification accuracy (area-weighted method) |
| sits_apply | Apply a function on a set of time series |
| sits_apply.raster_cube | Apply a function on a set of time series |
| sits_apply.sits | 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_sf.raster_cube | Return a sits_tibble or raster_cube as an sf object. |
| sits_as_sf.sits | Return a sits_tibble or raster_cube as an sf object. |
| sits_bands | Get the names of the bands |
| sits_bands.patterns | Get the names of the bands |
| sits_bands.raster_cube | Get the names of the bands |
| sits_bands.sits | Get the names of the bands |
| sits_bands.sits_model | Get the names of the bands |
| sits_bbox | Get the bounding box of the data |
| sits_bbox.raster_cube | Get the bounding box of the data |
| sits_bbox.sits | Get the bounding box of the data |
| sits_classify | Classify time series or data cubes |
| sits_classify.raster_cube | Classify time series or data cubes |
| sits_classify.sits | Classify time series or data cubes |
| sits_clustering | Find clusters in time series samples |
| 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_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_color_value | Function to retrieve sits color value |
| sits_combine_predictions | Estimate ensemble prediction based on list of probs cubes |
| sits_combine_predictions.average | Estimate ensemble prediction based on list of probs cubes |
| sits_combine_predictions.uncertainty | 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_configuration | Configure parameters for sits package |
| sits_config_show | Configure parameters for sits package |
| sits_cube | Create data cubes from image collections |
| sits_cube.local_cube | Create data cubes from image collections |
| sits_cube.stac_cube | Create data cubes from image collections |
| sits_cube_copy | Copy the images of a cube to a local directory |
| sits_filter | Filter time series and data cubes |
| 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_geo_dist | Compute the minimum distances among samples and prediction points. |
| sits_get_data | Get time series from data cubes and cloud services |
| sits_get_data.csv | Get time series from data cubes and cloud services |
| sits_get_data.data.frame | Get time series from data cubes and cloud services |
| sits_get_data.default | Get time series from data cubes and cloud services |
| sits_get_data.sf | Get time series from data cubes and cloud services |
| sits_get_data.shp | Get time series from data cubes and cloud services |
| sits_get_data.sits | 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_labels | Get labels associated to a data set |
| sits_labels.patterns | Get labels associated to a data set |
| sits_labels.raster_cube | Get labels associated to a data set |
| sits_labels.sits | Get labels associated to a data set |
| sits_labels.sits_model | 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 set of time series |
| sits_labels<-.probs_cube | Change the labels of a set of time series |
| 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_labels_summary.sits | Inform label distribution of a set of time series |
| sits_label_classification | Build a labelled image from a probability cube |
| sits_label_classification.probs_cube | Build a labelled image from a probability cube |
| sits_lighttae | Train a model using Lightweight Temporal Self-Attention Encoder |
| sits_list_collections | Configure parameters for sits package |
| sits_merge | Merge two data sets (time series or cubes) |
| sits_merge.raster_cube | Merge two data sets (time series or cubes) |
| sits_merge.sits | Merge two data sets (time series or cubes) |
| sits_mixture_model | Multiple endmember spectral mixture analysis |
| sits_mixture_model.raster_cube | Multiple endmember spectral mixture analysis |
| sits_mixture_model.sits | Multiple endmember spectral mixture analysis |
| sits_mlp | Train multi-layer perceptron models using torch |
| sits_model_export | Export classification models |
| sits_model_export.sits_model | Export classification models |
| sits_mosaic | Mosaic classified cubes |
| sits_patterns | Find temporal patterns associated to a set of time series |
| sits_reclassify | Reclassify a classified cube |
| sits_reclassify.class_cube | Reclassify a classified cube |
| 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_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_select | Filter bands on a data set (tibble or cube) |
| sits_select.patterns | Filter bands on a data set (tibble or cube) |
| sits_select.raster_cube | Filter bands on a data set (tibble or cube) |
| sits_select.sits | Filter bands on a data set (tibble or cube) |
| sits_sgolay | Filter time series and data cubes |
| sits_smooth | Smooth probability cubes with spatial predictors |
| sits_som | Use SOM for quality analysis of time series samples |
| sits_som_clean_samples | Cleans the samples based on SOM map information |
| sits_som_evaluate_cluster | Evaluate cluster |
| sits_som_map | Use SOM for quality analysis of time series samples |
| 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_timeline | Get timeline of a cube or a set of time series |
| 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.entropy | Estimate classification uncertainty based on probs cube |
| sits_uncertainty.least | Estimate classification uncertainty based on probs cube |
| sits_uncertainty.margin | Estimate classification uncertainty based on probs cube |
| sits_uncertainty_sampling | Suggest samples for enhancing classification accuracy |
| sits_validate | Validate time series samples |
| sits_values | Return the values of a set of time series |
| sits_variance | Calculate the variance of a probability cube |
| sits_view | View data cubes and samples in leaflet |
| sits_view.class_cube | View data cubes and samples in leaflet |
| sits_view.default | View data cubes and samples in leaflet |
| sits_view.probs_cube | View data cubes and samples in leaflet |
| sits_view.raster_cube | View data cubes and samples in leaflet |
| sits_view.sits | View data cubes and samples in leaflet |
| sits_view.som_map | View data cubes and samples in leaflet |
| sits_whittaker | Filter time series and data cubes |
| sits_xgboost | Train extreme gradient boosting models |
| %>% | Pipe |
| _PACKAGE | sits |
| `sits_labels<-` | Change the labels of a set of time series |