Merge pull request #1289 from e-sensing/dev

Pre-realease 1.5.2

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: sits
 Type: Package
-Version: 1.5.1
+Version: 1.5.2
 Title: Satellite Image Time Series Analysis for Earth Observation Data Cubes
 Authors@R: c(person('Rolf', 'Simoes', role = c('aut'), email = '[email protected]'),
              person('Gilberto', 'Camara', role = c('aut', 'cre', 'ths'), email = '[email protected]'),
@@ -11,6 +11,7 @@ Authors@R: c(person('Rolf', 'Simoes', role = c('aut'), email = 'rolf.simoes@inpe
              person('Charlotte', 'Pelletier', role = c('ctb'), email = '[email protected]'),
              person('Pedro', 'Andrade', role = c('ctb'), email = '[email protected]'),
              person('Alber', 'Sanchez', role = c('ctb'), email = '[email protected]'),
+             person('Estefania', 'Pizarro', role = c('ctb'), email = '[email protected]'),
              person('Gilberto', 'Queiroz', role = c('ctb'), email = '[email protected]')
              )
 Maintainer: Gilberto Camara <[email protected]>
@@ -26,14 +27,18 @@ Description: An end-to-end toolkit for land use and land cover classification
     smoothing filters for dealing with noisy time series.
     Includes functions for quality assessment of training samples using self-organized maps 
     as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. 
+    Includes methods to reduce training samples imbalance proposed by 
+    Chawla et al (2002) <doi:10.1613/jair.953>.
     Provides machine learning methods including support vector machines, 
     random forests, extreme gradient boosting, multi-layer perceptrons,
-    temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, 
+    temporal convolutional neural networks proposed 
+    by Pelletier et al (2019) <doi:10.3390/rs11050523>, 
     and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>.
     Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>.
     Performs efficient classification of big Earth observation data cubes and includes 
-    functions for post-classification smoothing based on Bayesian inference, and 
-    methods for active learning and uncertainty assessment. Supports object-based 
+    functions for post-classification smoothing based on Bayesian inference 
+    as described by Camara et al (2024) <doi:10.3390/rs16234572>, and 
+    methods for active learning and uncertainty assessment. Supports region-based 
     time series analysis using package supercells <https://jakubnowosad.com/supercells/>.
     Enables best practices for estimating area and assessing accuracy of land change as 
     recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>.
@@ -47,33 +52,33 @@ License: GPL-2
 ByteCompile: true
 LazyData: true
 Imports:
-    yaml,
-    dplyr (>= 1.0.0),
-    gdalUtilities,
+    yaml (>= 2.3.0),
+    dplyr (>= 1.1.0),
     grDevices,
     graphics,
+    leaflet (>= 2.2.2),
     lubridate,
-    magrittr, 
-    parallel (>= 4.0.5),
+    luz (>= 0.4.0),
+    parallel,
     purrr (>= 1.0.2),
-    Rcpp,
+    randomForest,
+    Rcpp (>= 1.0.13),
     rstac (>= 1.0.1),
-    sf (>= 1.0-12),
-    showtext,
-    sysfonts,
+    sf (>= 1.0-19),
     slider (>= 0.2.0),
     stats,
-    terra (>= 1.7-65),
+    terra (>= 1.8-5),
     tibble (>= 3.1),
-    tidyr (>= 1.2.0),
-    torch (>= 0.11.0),
+    tidyr (>= 1.3.0),
+    tmap (>= 4.0),
+    torch (>= 0.14.0),
     units,
     utils
 Suggests:
     aws.s3,
     caret,
     cli,
-    cols4all,
+    cols4all (>= 0.8.0),
     covr,
     dendextend,
     dtwclust,
@@ -82,31 +87,25 @@ Suggests:
     e1071,
     exactextractr,
     FNN,
-    future,
-    gdalcubes (>= 0.6.0),
+    gdalcubes (>= 0.7.0),
     geojsonsf,
     ggplot2,
-    httr2,
+    httr2 (>= 1.1.0),
     jsonlite,
     kohonen (>= 3.0.11),
-    leafem (>= 0.2.0),
-    leaflet (>= 2.2.0),
-    luz (>= 0.4.0),
     methods,
     mgcv,
     nnet,
     openxlsx,
-    randomForest,
+    proxy,
     randomForestExplainer,
     RColorBrewer,
     RcppArmadillo (>= 0.12),
     scales,
     spdep,
-    stars (>= 0.6-5),
     stringr,
     supercells (>= 1.0.0),
     testthat (>= 3.1.3),
-    tmap (>= 3.3),
     tools,
     xgboost
 Config/testthat/edition: 3
@@ -121,6 +120,7 @@ Collate:
     'api_accuracy.R'
     'api_apply.R'
     'api_band.R'
+    'api_bayts.R'
     'api_bbox.R'
     'api_block.R'
     'api_check.R'
@@ -137,16 +137,20 @@ Collate:
     'api_cube.R'
     'api_data.R'
     'api_debug.R'
+    'api_detect_change.R'
     'api_download.R'
+    'api_dtw.R'
     'api_environment.R'
     'api_factory.R'
     'api_file_info.R'
     'api_file.R'
     'api_gdal.R'
     'api_gdalcubes.R'
+    'api_grid.R'
     'api_jobs.R'
     'api_kohonen.R'
     'api_label_class.R'
+    'api_mask.R'
     'api_merge.R'
     'api_mixture_model.R'
     'api_ml_model.R'
@@ -160,16 +164,15 @@ Collate:
     'api_plot_vector.R'
     'api_point.R'
     'api_predictors.R'
+    'api_preconditions.R'
     'api_raster.R'
     'api_raster_sub_image.R'
-    'api_raster_terra.R'
     'api_reclassify.R'
     'api_reduce.R'
     'api_regularize.R'
     'api_request.R'
     'api_request_httr2.R'
     'api_roi.R'
-    'api_s2tile.R'
     'api_samples.R'
     'api_segments.R'
     'api_select.R'
@@ -200,13 +203,13 @@ Collate:
     'api_tile.R'
     'api_timeline.R'
     'api_tmap.R'
-    'api_tmap_v3.R'
     'api_torch.R'
     'api_torch_psetae.R'
     'api_ts.R'
     'api_tuning.R'
     'api_uncertainty.R'
     'api_utils.R'
+    'api_validate.R'
     'api_values.R'
     'api_variance.R'
     'api_vector.R'
@@ -218,8 +221,8 @@ Collate:
     'sits_add_base_cube.R'
     'sits_apply.R'
     'sits_accuracy.R'
-    'sits_active_learning.R'
     'sits_bands.R'
+    'sits_bayts.R'
     'sits_bbox.R'
     'sits_classify.R'
     'sits_colors.R'
@@ -230,10 +233,15 @@ Collate:
     'sits_cube_copy.R'
     'sits_clean.R'
     'sits_cluster.R'
+    'sits_detect_change.R'
+    'sits_detect_change_method.R'
+    'sits_dtw.R'
     'sits_factory.R'
     'sits_filters.R'
     'sits_geo_dist.R'
     'sits_get_data.R'
+    'sits_get_class.R'
+    'sits_get_probs.R'
     'sits_histogram.R'
     'sits_imputation.R'
     'sits_labels.R'
@@ -250,6 +258,7 @@ Collate:
     'sits_predictors.R'
     'sits_reclassify.R'
     'sits_reduce.R'
+    'sits_reduce_imbalance.R'
     'sits_regularize.R'
     'sits_sample_functions.R'
     'sits_segmentation.R'