From ed8167b28c0e788283ccb6135a9eae8a553473dc Mon Sep 17 00:00:00 2001 From: Andrew McCluskey Date: Mon, 4 May 2020 09:44:46 +0100 Subject: [PATCH] formatting fix --- paper/paper.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 5070f64..da0885a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -27,8 +27,7 @@ bibliography: paper.bib # Summary -`uravu` offers an easy to use interface to Bayesian modelling for scientific analysis in the Python programming language, making Bayesian modelling as easy to use as the `scipy.optimize.curve_fit()` method. -This software acts to lower the barrier of entry to the use of packages such as: +`uravu` offers an easy to use interface to Bayesian modelling for scientific analysis in the Python programming language, making Bayesian modelling as easy to use as the `scipy.optimize.curve_fit()` method. This software acts to lower the barrier of entry to the use of packages such as: - `scipy`: for maximum likelihood estimation [@virtanen_scipy_2020] - `emcee`: for Markov chain Monte Carlo investigation of posterior probabilities [@foremanmackey_emcee_2019] @@ -36,7 +35,7 @@ This software acts to lower the barrier of entry to the use of packages such as: In addition to lowering the entry barrier uravu also adds additional utility, such as the inclusion of measurement units (important for scientific analysis) with the `pint` package, and publication-quality plots of relationships, data, and distributions with `matplotlib` [@hunter_matplotlib_2007] and `corner` [@foremanmackey_corner_2019]. -In addition to the straightforward interface, the `uravu` documentation offers brief tutorials (uravu.rtfd.io/en/latest/tutorials.html) in all aspects of the package. +Alongside API information, the `uravu` documentation offers brief tutorials (uravu.rtfd.io/en/latest/tutorials.html) in all aspects of the package. This enables those unfamiliar with Bayesian modelling to get to grips with these important tools for data analysis. `uravu` is being actively applied to scientific problems, such as data reduction at large scale scientific facilities and the modelling of diffusion in battery materials.