Access OpenDota Services in R (ROpenDota)
Provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see <https://…/ROpenDota> for more information.

Datasets from the Datasaurus Dozen (datasauRus)
The Datasaurus Dozen is a set of datasets with the same summary statistics. They retain the same summary statistics despite having radically different distributions. The datasets represent a larger and quirkier object lesson that is typically taught via Anscombe’s Quartet (available in the ‘datasets’ package). Anscombe’s Quartet contains four very different distributions with the same summary statistics and as such highlights the value of visualisation in understanding data, over and above summary statistics. As well as being an engaging variant on the Quartet, the data is generated in a novel way. The simulated annealing process used to derive datasets from the original Datasaurus is detailed in ‘Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing’ <http://…/3025453.3025912>.

Run ‘roxygen2’ on (Chunks of) Single Code Files (document)
Have you ever been tempted to create ‘roxygen2’-style documentation comments for one of your functions that was not part of one of your packages (yet)? This is exactly what this package is about: running ‘roxygen2’ on (chunks of) a single code file.

Tools for Teaching and Learning OLS Regression (olsrr)
Tools for teaching and learning ordinary least squares regression. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.

Penalized Adaptive Weighted Least Squares Regression (pawls)
Efficient algorithms for fitting weighted least squares regression with \eqn{L_{1}}{L1} regularization on both the coefficients and weight vectors, which is able to perform simultaneous variable selection and outliers detection efficiently.