Clean up NEWS

NEWS.md

@@ -1,44 +1,34 @@
-# yardstick development
+# yardstick (development version)
 
 ## New metrics and functionality
 
-* `iic()` is a numeric metric for computing the index of ideality of correlation. It can be seen as a potential alternative to the traditional correlation coefficient, and has been used in QSAR models (@jyuu, #115).
+* `iic()` is a new numeric metric for computing the index of ideality of correlation. It can be seen as a potential alternative to the traditional correlation coefficient, and has been used in QSAR models (@jyuu, #115).
 
-* `average_precision()` is a probability metric that can be used as an alternative to `pr_auc()`. It has the benefit of avoiding any issues of ambiguity in the case where `recall == 0` and the current number of false positives is `0`.
+* `average_precision()` is a new probability metric that can be used as an alternative to `pr_auc()`. It has the benefit of avoiding any issues of ambiguity in the case where `recall == 0` and the current number of false positives is `0`.
 
 ## Other improvements
 
 * Each metric function now has a `direction` attribute attached to it, specifying whether to minimize or maximize the metric.
 
-* Classification metrics that can potentially have a `0` value denominator now throw an informative warning when this case occurs. These directly include `recall()`, `precision()`, `sens()`, and `spec()` (#98).
+* Classification metrics that can potentially have a `0` value denominator now throw an informative warning when this case occurs. These include `recall()`, `precision()`, `sens()`, and `spec()` (#98).
 
-* The `autoplot()` method for `pr_curve()` has been improved to always set the
-axis limits to `c(0, 1)`.
+* The `autoplot()` method for `pr_curve()` has been improved to always set the axis limits to `c(0, 1)`.
 
-* All valid arguments to `pROC::roc()` are now utilized, including those passed
-on to `pROC::auc()`.
+* All valid arguments to `pROC::roc()` are now utilized, including those passed on to `pROC::auc()`.
 
-* Documentation for class probability metrics has been improved with
-more informative examples (@rudeboybert, #100).
+* Documentation for class probability metrics has been improved with more informative examples (@rudeboybert, #100).
 
 ## Bug fixes
 
 * `mn_log_loss()` now uses the min/max rule before computing the log of the estimated probabilities to avoid problematic undefined log values (#103).
 
-* `pr_curve()` now places a `1` as the first precision value, rather than a
-`NA`. While `NA` is technically correct as precision is undefined here, `1` is
-practically more correct because it generates a correct PR Curve graph and, 
-more importantly, allows `pr_auc()` to compute the correct AUC.
+* `pr_curve()` now places a `1` as the first precision value, rather than `NA`. While `NA` is technically correct as precision is undefined here, `1` is practically more correct because it generates a correct PR Curve graph and, more importantly, allows `pr_auc()` to compute the correct AUC.
 
-* `pr_curve()` could generate the wrong results in the somewhat rare case 
-when two class probability estimates were the same, but had different 
-truth values.
+* `pr_curve()` could generate the wrong results in the somewhat rare case when two class probability estimates were the same, but had different truth values.
 
-* `pr_curve()` (and subsequently `pr_auc()`) now generates the correct curve
-when there are duplicate class probability values (reported by @dariyasydykova, #93).
+* `pr_curve()` (and subsequently `pr_auc()`) now generates the correct curve when there are duplicate class probability values (reported by @dariyasydykova, #93).
 
-* Binary `mcc()` now avoids integer overflow when the confusion matrix 
-elements are large (#108).
+* Binary `mcc()` now avoids integer overflow when the confusion matrix elements are large (#108).
 
 # yardstick 0.0.3