istrans -> is_transformed

Author: penelopeysm

src/mcmc/gibbs.jl

@@ -551,22 +551,22 @@ variables, and one might need it to be linked while the other doesn't.
 """
 function match_linking!!(varinfo_local, prev_state_local, model)
     prev_varinfo_local = get_varinfo(prev_state_local)
-    was_linked = DynamicPPL.istrans(prev_varinfo_local)
-    is_linked = DynamicPPL.istrans(varinfo_local)
+    was_linked = DynamicPPL.is_transformed(prev_varinfo_local)
+    is_linked = DynamicPPL.is_transformed(varinfo_local)
     if was_linked && !is_linked
         varinfo_local = DynamicPPL.link!!(varinfo_local, model)
     elseif !was_linked && is_linked
         varinfo_local = DynamicPPL.invlink!!(varinfo_local, model)
     end
     # TODO(mhauru) The above might run into trouble if some variables are linked and others
-    # are not. `istrans(varinfo)` returns an `all` over the individual variables. This could
+    # are not. `is_transformed(varinfo)` returns an `all` over the individual variables. This could
     # especially be a problem with dynamic models, where new variables may get introduced,
     # but also in cases where component samplers have partial overlap in their target
     # variables. The below is how I would like to implement this, but DynamicPPL at this
     # time does not support linking individual variables selected by `VarName`. It soon
     # should though, so come back to this.
     # Issue ref: https://github.com/TuringLang/Turing.jl/issues/2401
-    # prev_links_dict = Dict(vn => DynamicPPL.istrans(prev_varinfo_local, vn) for vn in keys(prev_varinfo_local))
+    # prev_links_dict = Dict(vn => DynamicPPL.is_transformed(prev_varinfo_local, vn) for vn in keys(prev_varinfo_local))
     # any_linked = any(values(prev_links_dict))
     # for vn in keys(varinfo_local)
     #     was_linked = if haskey(prev_varinfo_local, vn)
@@ -576,7 +576,7 @@ function match_linking!!(varinfo_local, prev_state_local, model)
     #         # of the variables of the old state were linked.
     #         any_linked
     #     end
-    #     is_linked = DynamicPPL.istrans(varinfo_local, vn)
+    #     is_linked = DynamicPPL.is_transformed(varinfo_local, vn)
     #     if was_linked && !is_linked
     #         varinfo_local = DynamicPPL.invlink!!(varinfo_local, vn)
     #     elseif !was_linked && is_linked

src/optimisation/Optimisation.jl

@@ -274,7 +274,7 @@ function StatsBase.informationmatrix(
     # Convert the values to their unconstrained states to make sure the
     # Hessian is computed with respect to the untransformed parameters.
     old_ldf = m.f.ldf
-    linked = DynamicPPL.istrans(old_ldf.varinfo)
+    linked = DynamicPPL.is_transformed(old_ldf.varinfo)
     if linked
         new_vi = DynamicPPL.invlink!!(old_ldf.varinfo, old_ldf.model)
         new_f = OptimLogDensity(