We’ve made two major updates for this release.

The centroid functions have gained a keyword argument, `squared`

. The final step in computing distance to group centroids involves taking a square root. Sometimes, we end up with a negative number inside the square root. Normally, this produces `NaN`

as a result. However, if `squared`

is set to `TRUE`

, we don’t take the square root and the result is always a real number. The default setting is `squared = FALSE`

, which gives the distance as you’d expect. Thanks to Sam Ross for helpful advice on this topic.

We added a new function, `pivot_to_numeric_matrix()`

. This function takes a data frame in long format and converts to a matrix suitable for distance calculations. Long-format data frames are commonly used with functions in the `tidyverse`

, and proper conversion to a matrix requires a few non-obvious steps. The packages `dplyr`

, `tidyr`

, and `tibble`

are needed to run the function, and have been added as suggested packages for `usedist`

.

During development, we had implemented an additional function to create a distance matrix directly from a data frame in long format. However, we found that it was nearly as convenient to use `pivot_to_numeric_matrix()`

and `dist_make()`

together to achieve the same result. We added an example to the README file to illustrate this.

Initial release!

For the first release, we’ve moved over all the distance matrix-related functions from the `qiimer`

package. Utility functions `dist_setNames`

and `dist_make`

were added to the existing functions `dist_subset`

, `dist_get`

, and `dist_groups`

.

The other area of focus for our initial release was a set of distance-to-centroid functions. These functions operate on a set of items that are placed into several groups, and they apply algebraic formulas to find the group centroid distances based on the distance matrix. If you had a set of points in normal Euclidean space, you could easily find the centroid positions and compute the distances from there. The centroid functions included in the package are more useful for non-Euclidean distances, where it is less straightforward to locate the centroids in space. The centroid functions were inspired by work in microbial ecology, where it is typical to work with non-Euclidean distances between samples.