remove references to ResNet

Author: gilbertocamara

R/sits_classify.R

@@ -13,8 +13,7 @@
 #' (c) extreme gradient boosting: \code{\link[sits]{sits_xgboost}};
 #' (d) multi-layer perceptrons: \code{\link[sits]{sits_mlp}};
 #' (e) 1D CNN: \code{\link[sits]{sits_tempcnn}};
-#' (f) deep residual networks: \code{\link[sits]{sits_resnet}};
-#' (g) self-attention encoders: \code{\link[sits]{sits_lighttae}}.
+#' (f) self-attention encoders: \code{\link[sits]{sits_lighttae}}.
 #'
 #' @param  data              Data cube (tibble of class "raster_cube")
 #' @param  ml_model          R model trained by \code{\link[sits]{sits_train}}

R/sits_train.R

@@ -13,7 +13,6 @@
 #' and different deep learning functions, including multi-layer perceptrons
 #' (see \code{\link[sits]{sits_mlp}}), 1D convolution neural
 #' networks \code{\link[sits]{sits_tempcnn}},
-#' deep residual networks \code{\link[sits]{sits_resnet}} and
 #' self-attention encoders \code{\link[sits]{sits_lighttae}}
 #'
 #' @param  samples          Time series with the training samples.

R/sits_variance.R

@@ -27,7 +27,7 @@
 #' <https://e-sensing.github.io/sitsbook/> for detailed examples.
 #' @examples
 #' if (sits_run_examples()) {
-#'     # create a ResNet model
+#'     # create a random forest model
 #'     rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
 #'     # create a data cube from local files
 #'     data_dir <- system.file("extdata/raster/mod13q1", package = "sits")