Some docs touchups

NEWS.md

@@ -1,5 +1,12 @@
 # History of Measurements.jl
 
+## v2.11.0 (????)
+
+### New features
+
+* Support for [symbolic variables](https://symbolics.juliasymbolics.org/stable/manual/variables/),
+   as provided by `Symbolics.jl` package.
+
 ## v2.10.0 (2023-09-21)
 
 ### New features

README.md

@@ -52,6 +52,9 @@ easy-to-use calculator.
   [arbitrary precision](https://docs.julialang.org/en/v1/manual/integers-and-floating-point-numbers/#Arbitrary-Precision-Arithmetic-1)
   (also called multiple precision) numbers with uncertainties.  This is useful
   for measurements with very low relative error
+* Support for numbers with uncertainties of
+  [symbolic variable](https://symbolics.juliasymbolics.org/stable/manual/variables/)
+  type, as provided by `Symbolics.jl` package.
 * Define arrays of measurements and perform calculations with them.  Some linear
   algebra functions work out-of-the-box
 * Propagate uncertainty for any function of real arguments (including functions

docs/src/examples.md

@@ -272,6 +272,33 @@ hypot(a, b)
 log(2a) ^ b
 ```
 
+Symbolic Variables
+------------------
+
+You can perform any mathematical operation supported in `Measurements.jl` using
+[symbolic variables](https://symbolics.juliasymbolics.org/stable/manual/variables/),
+as provided by `Symbolics.jl` package:
+
+``` julia
+julia> using Measurements, Symbolics
+
+julia> @variables x_val, x_err, y_val, y_err
+4-element Vector{Num}:
+ x_val x_err y_val y_err
+
+julia> x = x_val ± x_err
+x_val ± x_err
+
+julia> y = y_val ± y_err
+y_val ± y_err
+
+julia> x + y
+x_val + y_val ± sqrt(abs2(x_err) + abs2(y_err))
+
+julia> x - x
+0 ± 0.0
+```
+
 Operations with Arrays and Linear Algebra
 -----------------------------------------
 

docs/src/index.md

@@ -48,6 +48,9 @@ The main features of the package are:
   precision](https://docs.julialang.org/en/v1/manual/integers-and-floating-point-numbers/#Arbitrary-Precision-Arithmetic-1)
   (also called multiple precision) numbers with uncertainties. This is useful
   for measurements with very low relative error
+- Support for numbers with uncertainties of
+  [symbolic variable](https://symbolics.juliasymbolics.org/stable/manual/variables/)
+  type, as provided by `Symbolics.jl` package.
 - Define arrays of measurements and perform calculations with them.  Some linear
   algebra functions work out-of-the-box
 - Propagate uncertainty for any function of real arguments (including functions

docs/src/usage.md

@@ -20,11 +20,12 @@ where
 - `err` is its uncertainty, assumed to be a [standard
   deviation](https://en.wikipedia.org/wiki/Standard_deviation).
 
-They are both subtype of `AbstractFloat`. Some keyboard layouts provide an easy
+They are both subtype of `Real`. Some keyboard layouts provide an easy
 way to type the `±` sign, if your does not, remember you can insert it in Julia
 REPL with `\pm` followed by `TAB` key. You can provide `val` and `err` of any
-subtype of `Real` that can be converted to `AbstractFloat`. Thus,
-`measurement(42, 33//12)` and `pi ± 0.1` are valid.
+subtype of `Real` that can be converted to `AbstractFloat`, or are
+[symbolic variables](https://symbolics.juliasymbolics.org/stable/manual/variables/).
+Thus, `measurement(42, 33//12)` and `pi ± 0.1` are valid.
 
 `measurement(value)` creates a `Measurement` object with zero uncertainty, like
 mathematical constants. See below for further examples.