Digital Elevation Models using the 'CopernicusDEM' R package

In this blog post I’ll explain how to use the CopernicusDEM R package based on a use case of the Movebank animal tracking data. I picked animal tracking data because there is an abundance in the Movebank archive from all over the world. In this specific vignette I’ll use data of Wolves from the northeastern Alberta and Caribou from the British Columbia (see the reference papers at the end of the blog post for more information).

Fitbit Visualizations using the 'fitbitViz' R package

2026-04-27:

• Breaking change: Fitbit Web API dependency removed from the fitbitViz R package. All functions that previously required a user_id and token have been refactored to accept pre-downloaded data directly, eliminating the dependency on httr, jsonlite, and base64enc. This change was driven by recurring Fitbit API policy changes (GitHub issues #4, #5, #7, #8, #11).
• Users who need the previous API-based version can install it with remotes::install_github("mlampros/fitbitViz@v1.0.7") or from the CRAN archive at https://cran.r-project.org/src/contrib/Archive/fitbitViz/fitbitViz_1.0.7.tar.gz (the version 1.0.7 is no longer maintained)
• This blog post is based on version 1.0.7 (or earlier)

Language Identification using the 'fastText' package (a Benchmark)

In this blog post, I’ll explain how to perform Language Identification with the fastText R package and I’ll create a benchmark by including other language identification R packages, i.e.

• cld2, R Wrapper for Google’s Compact Language Detector 2
• cld3, Bindings to Google’s Compact Language Detector 3
• textcat, N-Gram Based Text Categorization
• franc, Port of the JavaScript ‘franc’, Detect the Language of Text

fastText ( updated version )

In this blog post, I’ll explain the updated version of the fastText R package. This R package is an interface to the fasttext library for efficient learning of word representations and sentence classification. Back in 2016 I ported for the first time the fasttext library but it had restricted functionality. This updated version allows the user to take advantage of all current features and it adds new ones such as plotting of loss, learning-rate and word-counts.

Image segmentation based on Superpixels and Clustering

In this blog post, I’ll explain the new functionality of the OpenImageR package, SLIC and SLICO superpixels (Simple Linear Iterative Clustering) and their applicability based on an IJSR article. The author of the article uses superpixel (SLIC) and Clustering (Affinity Propagation) to perform image segmentation. The article was reproduced (and extended with Kmeans) using the latest versions of the OpenImageR and ClusterR packages.