update DESCRIPTION

Author: gilbertocamara

DESCRIPTION

@@ -27,7 +27,7 @@ Description: An end-to-end toolkit for land use and land cover classification
     random forests, extreme gradient boosting, multi-layer perceptrons,
     temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, 
     residual networks by Fawaz et al (2019) <doi:10.1007/s10618-019-00619-1>, and temporal attention encoders
-    by Garnot and Landrieu (2020) <arXiv:2007.00586>.
+    by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>.
     Performs efficient classification of big Earth observation data cubes and includes 
     functions for post-classification smoothing based on Bayesian inference, and 
     methods for uncertainty assessment. Enables best

R/api_kohonen.R

@@ -77,7 +77,6 @@
     for (i in 1:nclasses) {
         outmat[which(as.integer(yvec) == i), i] <- 1
     }
-
     outmat
 }
 

contributing.md

@@ -1,10 +1,6 @@
 # Contributing to the sits R package
 
-- We welcome all contributors to sits package! 
-
-Please submit questions, bug reports, and requests in the [issues tracker](https://github.com/e-sensing/sits/issues). 
-
-If you plan to contribute code, go ahead! Fork the repo and submit a pull request. A few notes:
+We welcome all contributors to sits package! Please submit questions, bug reports, and requests in the [issues tracker](https://github.com/e-sensing/sits/issues). If you plan to contribute code, go ahead! Fork the repo and submit a pull request. A few notes:
 
 -   This package is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms. 
 -   If you have large changes, please open an issue first to discuss.
@@ -13,14 +9,13 @@ their permission) for contributions that go beyond small typos in code or docume
 -   This package generally uses the [rOpenSci packaging guidelines](https://github.com/ropensci/onboarding/blob/master/packaging_guide.md) for style and structure.
 -   Documentation is generated by **roxygen2**. Please write documentation in code files and let it auto-generate documentation files.  
 -  For more substantial contributions, consider adding a new section to one of the chapters of the SITS book (https://e-sensing.github.io/sitsbook/), which has been written in R markdown and whose source is available in the [sitsbook repository](https://github.com/e-sensing/sitsbook).
--  We aim for testing that has high coverage and is robust.  Include tests with
-   any major contribution to code. 
+-  We aim for testing that has high coverage and is robust.  Include tests with any major contribution to code. 
 - We particularly welcome additions in two areas: new STAC-based image repositories and new raster machine learning/deep learning algorithms. Please see more details below. 
 
 
 ## General structure of sits code.
 
-- New functions that build on the `sits` API should follow the general principles below.
+New functions that build on the `sits` API should follow the general principles below.
 
 ### API design 
 
@@ -42,7 +37,6 @@ their permission) for contributions that go beyond small typos in code or docume
 
 The sits `code` relies on the packages of the `tidyverse` to work with tables and list. We use `dplyr` and `tidyr` for data selection and wrangling, `purrr` and `slider` for loops on lists and table, `lubridate` to handle dates and times. 
 
-
 ### Adherence to the `sits` data types
 
 - The `sits` package in built on top of three data types: time series tibble, data cubes and models. Most `sits` functions have one or more of these types as inputs and one of them as return values.
@@ -73,8 +67,6 @@ The sits `code` relies on the packages of the `tidyverse` to work with tables an
 
 - Each STAC catalogue is different. The STAC specification allows providers to implement their data descriptions with specific information. For this reason, the generic API described in `api_source.R` needs to be specialized for each provider. Whenever a provider needs specific implementations of parts of the STAC protocol, we include them in separate files. For example, `api_source_mpc.R` implements specific quirks of the MPC platform. Similarly, specific support for CDSE (Copernicus Data Space Environment) is available in `api_source_cdse.R`. 
 
-- In summary, before including a new data provider, study the code that has been implemented. 
-
 ### Supporting new Machine Learning and Deep Learning algorithms
 
 - In general terms, ML/DL algorithms in `sits` are encapsulated as closures which are the output of the `sits_train()` function. In line with the established practices in **R**, each closure  contains a function that classifies input values, as well as information on the samples used to train the model.