From 68a09fb0fc19628b19a233be5cd391db1a90c562 Mon Sep 17 00:00:00 2001
From: sschmidt23 <sschmidt@physics.ucdavis.edu>
Date: Wed, 29 May 2024 12:53:01 -0700
Subject: [PATCH] typo swap master -> main in tutorials paths

---
 README.md          |  2 +-
 docs/tutorials.rst | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index fb152e9..4b39d0c 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,7 @@ Quantile parametrization for probability distribution functions.
 
 ## Motivation
 
-In a scientific inference we typically seek to characterize the posterior probability density function (PDF) for our parameter(s), which means we need to fund a suitable, calculable approximation to it. Popular choices include an ensemble of samples, a histogram estimator based on those samples, or (in 1 dimensional problems) a tabulation of the PDF on a regular parameter grid. qp is a python package that supports these approximations, as well as the “quantile parameterization” from which the package gets its name.
+In a scientific inference we typically seek to characterize the posterior probability density function (PDF) for our parameter(s), which means we need to find a suitable, calculable approximation to it. Popular choices include an ensemble of samples, a histogram estimator based on those samples, or (in 1 dimensional problems) a tabulation of the PDF on a regular parameter grid. qp is a python package that supports these approximations, as well as the “quantile parameterization” from which the package gets its name.
 
 The [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html) package provides an interface to many probability distributions for parameterized analytic forms (e.g., Gaussians, LogNormal Distributions, etc...)  `qp` extends that functionality to numerically-evaluated forms, such as the histograms and interpolated grids mentioned above.
 
diff --git a/docs/tutorials.rst b/docs/tutorials.rst
index 7bab6e0..91ccaab 100644
--- a/docs/tutorials.rst
+++ b/docs/tutorials.rst
@@ -5,9 +5,9 @@ Tutorials
 
 See the following IPython Notebooks for some examples of using `qp`:
 
-* `Basic Demo <http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/docs/demo.html>`_
-* `Practical Example <http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/docs/practical_example.html>`_
-* `Using Metrics <http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/master/docs/metrics_examples.htm>`_
-* `Using iterarors <http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/master/docs/iterator_demo.html>`_
-* `Quantile parameterization] <http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/master/docs/quantile_parameterization_demo.html>`_
+* `Basic Demo <http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/main/docs/demo.html>`_
+* `Practical Example <http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/main/docs/practical_example.html>`_
+* `Using Metrics <http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/main/docs/metrics_examples.htm>`_
+* `Using iterarors <http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/main/docs/iterator_demo.html>`_
+* `Quantile parameterization] <http://htmlpreview.github.io/?https://github.com/LSSTDESC/qp/blob/main/docs/quantile_parameterization_demo.html>`_