Remove trailing whitespace

Author: mhauru

ext/DynamicPPLMCMCChainsExt.jl

@@ -48,18 +48,18 @@ end
 Sample from the posterior predictive distribution by executing `model` with parameters fixed to each sample
 in `chain`, and return the resulting `Chains`.
 
-The `model` passed to `predict` is often different from the one used to generate `chain`. 
-Typically, the model from which `chain` originated treats certain variables as observed (i.e., 
-data points), while the model you pass to `predict` may mark these same variables as missing 
-or unobserved. Calling `predict` then leverages the previously inferred parameter values to 
+The `model` passed to `predict` is often different from the one used to generate `chain`.
+Typically, the model from which `chain` originated treats certain variables as observed (i.e.,
+data points), while the model you pass to `predict` may mark these same variables as missing
+or unobserved. Calling `predict` then leverages the previously inferred parameter values to
 simulate what new, unobserved data might look like, given your posterior beliefs.
 
 For each parameter configuration in `chain`:
 1. All random variables present in `chain` are fixed to their sampled values.
 2. Any variables not included in `chain` are sampled from their prior distributions.
 
 If `include_all` is `false`, the returned `Chains` will contain only those variables that were not fixed by
-the samples in `chain`. This is useful when you want to sample only new variables from the posterior 
+the samples in `chain`. This is useful when you want to sample only new variables from the posterior
 predictive distribution.
 
 # Examples

src/logdensityfunction.jl

@@ -175,7 +175,7 @@ end
 Evaluate the log density of the given `model` at the given parameter values `x`,
 using the given `varinfo` and `context`. Note that the `varinfo` argument is provided
 only for its structure, in the sense that the parameters from the vector `x` are inserted into
-it, and its own parameters are discarded. 
+it, and its own parameters are discarded.
 """
 function logdensity_at(
     x::AbstractVector, model::Model, varinfo::AbstractVarInfo, context::AbstractContext