From 273b3ba32f7c725f1cf23bafa40174ac84117195 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Wed, 2 Oct 2019 13:02:16 +0200
Subject: [PATCH] fix syntax

---
 paper.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/paper.md b/paper.md
index 4ee60ee..e175906 100755
--- a/paper.md
+++ b/paper.md
@@ -74,7 +74,7 @@ In all cases using Adaptive results in a higher fidelity plot.
 
 #### We provide a reference implementation, the Adaptive package, and demonstrate its performance.
 We provide a reference implementation, the open-source Python package called Adaptive [@Nijholt2019], which has previously been used in several scientific publications [@Vuik2018; @Laeven2019; @Bommer2019; @Melo2019].
-It has algorithms for $f \colon \R^N \to \R^M$, where $N, M \in \mathbb{Z}^+$ but which work best when $N$ is small; integration in $\R$; and the averaging of stochastic functions.
+It has algorithms for $f \colon R^N \to \R^M$, where $N, M \in \mathbb{Z}^+$ but which work best when $N$ is small; integration in $\R$; and the averaging of stochastic functions.
 Most of our algorithms allow for a customizable loss function with which one can adapt the sampling algorithm to work optimally for different classes of functions.
 It integrates with the Jupyter notebook environment as well as popular parallel computation frameworks such as `ipyparallel`, `mpi4py`, and `dask.distributed`.
 It provides auxiliary functionality such as live-plotting, inspecting the data as the calculation is in progress, and automatically saving and loading of the data.
-- 
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