diff --git a/LICENSE b/LICENSE
index cb92340..eb243a2 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,10 +1,10 @@
 MIT License
 
-Copyright (c) 2021 Brian J Smith, David Widmann and contributors:
+Copyright (c) 2021 Brian J Smith, the Turing team and contributors:
 
 https://github.com/brian-j-smith/Mamba.jl/contributors
 
-https://github.com/devmotion/MCMCDiagnosticTools.jl/contributors
+https://turing.ml/dev/team/
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/src/rafterydiag.jl b/src/rafterydiag.jl
index e5d9db2..6a8fe72 100644
--- a/src/rafterydiag.jl
+++ b/src/rafterydiag.jl
@@ -3,8 +3,8 @@
 
 Compute the Raftery and Lewis diagnostic [^Raftery1992].
 This diagnostic is used to determine the number of iterations required to estimate a specified quantile `q` within a desired degree of accuracy.
-The diagnostic is designed to determine the number of autocorrelated samples required to estimate a specified quantile $\theta_q$, such that $\Pr(\theta \le \theta_q) = q$, within a desired degree of accuracy.
-In particular, if $\hat{\theta}_q$ is the estimand and $\Pr(\theta \le \hat{\theta}_q) = \hat{P}_q$ the estimated cumulative probability, then accuracy is specified in terms of $r$ and $s$, where $\Pr(q - r < \hat{P}_q < q + r) = s$.
+The diagnostic is designed to determine the number of autocorrelated samples required to estimate a specified quantile \$\theta_q\$, such that \$\\Pr(\\theta \\le \\theta_q) = q\$, within a desired degree of accuracy.
+In particular, if \$\\hat{\\theta}_q\$ is the estimand and \$\\Pr(\\theta \\le \\hat{\\theta}_q) = \\hat{P}_q\$ the estimated cumulative probability, then accuracy is specified in terms of `r` and `s`, where \$\\Pr(q - r < \\hat{P}_q < q + r) = s\$.
 Thinning may be employed in the calculation of the diagnostic to satisfy its underlying assumptions.
 However, users may not want to apply the same (or any) thinning when estimating posterior summary statistics because doing so results in a loss of information.
 Accordingly, sample sizes estimated by the diagnostic tend to be conservative (too large).