Fix line length

src/gelmandiag.jl

@@ -54,9 +54,10 @@ end
 """
     gelmandiag(chains::AbstractArray{<:Real,3}; alpha::Real=0.95)
 
-Compute the Gelman, Rubin and Brooks diagnostics [^Gelman1992] [^Brooks1998].
-Values of the diagnostic’s potential scale reduction factor (PSRF) that are close to one suggest convergence.
-As a rule-of-thumb, convergence is rejected if the 97.5 percentile of a PSRF is greater than 1.2.
+Compute the Gelman, Rubin and Brooks diagnostics [^Gelman1992] [^Brooks1998].  Values of the
+diagnostic’s potential scale reduction factor (PSRF) that are close to one suggest
+convergence.  As a rule-of-thumb, convergence is rejected if the 97.5 percentile of a PSRF
+is greater than 1.2.
 
 [^Gelman1992]: Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 7(4), 457-472.
 

src/gewekediag.jl

@@ -1,13 +1,16 @@
 """
     gewekediag(x::AbstractVector{<:Real}; first::Real=0.1, last::Real=0.5, kwargs...)
 
-Compute the Geweke diagnostic [^Geweke1991] from the `first` and `last` proportion of samples `x`.
+Compute the Geweke diagnostic [^Geweke1991] from the `first` and `last` proportion of
+samples `x`.
 
-The diagnostic is designed to asses convergence of posterior means estimated with autocorrelated samples.
-It computes a normal-based test statistic comparing the sample means in two windows containing proportions of the first and last iterations.
-Users should ensure that there is sufficient separation between the two windows to assume that their samples are independent.
-A non-significant test p-value indicates convergence.
-Significant p-values indicate non-convergence and the possible need to discard initial samples as a burn-in sequence or to simulate additional samples.
+The diagnostic is designed to asses convergence of posterior means estimated with
+autocorrelated samples.  It computes a normal-based test statistic comparing the sample
+means in two windows containing proportions of the first and last iterations.  Users should
+ensure that there is sufficient separation between the two windows to assume that their
+samples are independent.  A non-significant test p-value indicates convergence.  Significant
+p-values indicate non-convergence and the possible need to discard initial samples as a
+burn-in sequence or to simulate additional samples.
 
 [^Geweke1991]: Geweke, J. F. (1991). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments (No. 148). Federal Reserve Bank of Minneapolis.
 """

src/heideldiag.jl

@@ -3,10 +3,11 @@
         x::AbstractVector{<:Real}; alpha::Real=0.05, eps::Real=0.1, start::Int=1, kwargs...
     )
 
-Compute the Heidelberger and Welch diagnostic [^Heidelberger1983].
-This diagnostic tests for non-convergence (non-stationarity) and whether ratios of estimation interval halfwidths to means are within a target ratio.
-Stationarity is rejected (0) for significant test p-values.
-Halfwidth tests are rejected (0) if observed ratios are greater than the target, as is the case for `s2` and `beta[1]`.
+Compute the Heidelberger and Welch diagnostic [^Heidelberger1983]. This diagnostic tests for
+non-convergence (non-stationarity) and whether ratios of estimation interval halfwidths to
+means are within a target ratio. Stationarity is rejected (0) for significant test p-values.
+Halfwidth tests are rejected (0) if observed ratios are greater than the target, as is the
+case for `s2` and `beta[1]`.
 
 [^Heidelberger1983]: Heidelberger, P., & Welch, P. D. (1983). Simulation run length control in the presence of an initial transient. Operations Research, 31(6), 1109-1144.
 """

src/rafterydiag.jl

@@ -1,23 +1,31 @@
 @doc raw"""
-    rafterydiag(x::AbstractVector{<:Real}; q=0.025, r=0.005, s=0.95, eps=0.001, range=1:length(x))
+    rafterydiag(
+        x::AbstractVector{<:Real}; q=0.025, r=0.005, s=0.95, eps=0.001, range=1:length(x))
 
-Compute the Raftery and Lewis diagnostic [^rafterydiag].
-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 $\Pr(q - r < \hat{P}_q < q + r) = s$.
-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).
+Compute the Raftery and Lewis diagnostic [^rafterydiag]. 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
+$\Pr(q - r < \hat{P}_q < q + r) = s$. 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).
 
-Furthermore, the argument `r` specifies the margin of error for estimated cumulative probabilities and `s` the probability for the margin of error.
-`eps` specifies the  tolerance within which the probabilities of transitioning from initial to retained iterations are within the equilibrium probabilities for the chain.
-This argument determines the number of samples to discard as a burn-in sequence and is typically left at its default value.
+Furthermore, the argument `r` specifies the margin of error for estimated cumulative
+probabilities and `s` the probability for the margin of error. `eps` specifies the tolerance
+within which the probabilities of transitioning from initial to retained iterations are
+within the equilibrium probabilities for the chain. This argument determines the number of
+samples to discard as a burn-in sequence and is typically left at its default value.
 
 [^rafterydiag]: A L Raftery and S Lewis. Bayesian Statistics, chapter How Many Iterations in the Gibbs Sampler? Volume 4. Oxford University Press, New York, 1992.
 """
 function rafterydiag(
-    x::AbstractVector{<:Real}; q=0.025, r=0.005, s=0.95, eps=0.001, range=1:length(x)
-)
+    x::AbstractVector{<:Real}; q=0.025, r=0.005, s=0.95, eps=0.001, range=1:length(x))
+
     nx = length(x)
     phi = sqrt(2.0) * SpecialFunctions.erfinv(s)
     nmin = ceil(Int, q * (1.0 - q) * (phi / r)^2)