diff --git a/Project.toml b/Project.toml
index 9306ec49..2a76aaa4 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "ITensorNetworks"
 uuid = "2919e153-833c-4bdc-8836-1ea460a35fc7"
 authors = ["Matthew Fishman <mfishman@flatironinstitute.org> and contributors"]
-version = "0.5"
+version = "0.6"
 
 [deps]
 AbstractTrees = "1520ce14-60c1-5f80-bbc7-55ef81b5835c"
diff --git a/src/approx_itensornetwork/ttn_svd.jl b/src/approx_itensornetwork/ttn_svd.jl
index 958a80f9..59797c3e 100644
--- a/src/approx_itensornetwork/ttn_svd.jl
+++ b/src/approx_itensornetwork/ttn_svd.jl
@@ -2,7 +2,7 @@ using IterTools: partition
 """
 Approximate a `partition` into an output ITensorNetwork
 with the binary tree structure defined by `out_tree` by
-first transforming the partition into a TTN, then truncating
+first transforming the partition into a ttn, then truncating
 the ttn using a sequence of SVDs.
 """
 function _approx_itensornetwork_ttn_svd!(
@@ -22,7 +22,7 @@ function _approx_itensornetwork_ttn_svd!(
       contraction_sequence_kwargs=contraction_sequence_kwargs,
     )
   end
-  truncate_ttn = truncate(TTN(tn); cutoff=cutoff, maxdim=maxdim, root_vertex=root)
+  truncate_ttn = truncate(ttn(tn); cutoff=cutoff, maxdim=maxdim, root_vertex=root)
   out_tn = ITensorNetwork(truncate_ttn)
   root_tensor = out_tn[root]
   root_norm = norm(root_tensor)
diff --git a/src/solvers/contract.jl b/src/solvers/contract.jl
index 7a9fb2d9..cfc90fd6 100644
--- a/src/solvers/contract.jl
+++ b/src/solvers/contract.jl
@@ -22,7 +22,7 @@ function sum_contract(
   )
   any(ns .!= n) &&
     throw(DimensionMismatch("Number of sites in different operators ($n) do not match"))
-  # ToDo: Write test for single-vertex TTN, this implementation has not been tested.
+  # ToDo: Write test for single-vertex ttn, this implementation has not been tested.
   if n == 1
     res = 0
     for (tn1, tn2) in zip(tn1s, tn2s)
diff --git a/src/specialitensornetworks.jl b/src/specialitensornetworks.jl
index 967022b9..adb5f1f6 100644
--- a/src/specialitensornetworks.jl
+++ b/src/specialitensornetworks.jl
@@ -101,24 +101,24 @@ end
 """
 Build an ITensor network on a graph specified by the inds network s. Bond_dim is given by link_space and entries are randomised (normal distribution, mean 0 std 1)
 """
-function randomITensorNetwork(eltype::Type, s::IndsNetwork; link_space=nothing)
+function random_itensornetwork(eltype::Type, s::IndsNetwork; link_space=nothing)
   return ITensorNetwork(s; link_space) do v, inds...
     itensor(randn(eltype, dim(inds)...), inds...)
   end
 end
 
-function randomITensorNetwork(s::IndsNetwork; link_space=nothing)
-  return randomITensorNetwork(Float64, s; link_space)
+function random_itensornetwork(s::IndsNetwork; link_space=nothing)
+  return random_itensornetwork(Float64, s; link_space)
 end
 
-@traitfn function randomITensorNetwork(
+@traitfn function random_itensornetwork(
   eltype::Type, g::::IsUnderlyingGraph; link_space=nothing
 )
-  return randomITensorNetwork(eltype, IndsNetwork(g); link_space)
+  return random_itensornetwork(eltype, IndsNetwork(g); link_space)
 end
 
-@traitfn function randomITensorNetwork(g::::IsUnderlyingGraph; link_space=nothing)
-  return randomITensorNetwork(Float64, IndsNetwork(g); link_space)
+@traitfn function random_itensornetwork(g::::IsUnderlyingGraph; link_space=nothing)
+  return random_itensornetwork(Float64, IndsNetwork(g); link_space)
 end
 
 """
@@ -126,7 +126,7 @@ Build an ITensor network on a graph specified by the inds network s.
 Bond_dim is given by link_space and entries are randomized.
 The random distribution is based on the input argument `distribution`.
 """
-function randomITensorNetwork(
+function random_itensornetwork(
   distribution::Distribution, s::IndsNetwork; link_space=nothing
 )
   return ITensorNetwork(s; link_space) do v, inds...
@@ -134,8 +134,8 @@ function randomITensorNetwork(
   end
 end
 
-@traitfn function randomITensorNetwork(
+@traitfn function random_itensornetwork(
   distribution::Distribution, g::::IsUnderlyingGraph; link_space=nothing
 )
-  return randomITensorNetwork(distribution, IndsNetwork(g); link_space)
+  return random_itensornetwork(distribution, IndsNetwork(g); link_space)
 end
diff --git a/src/treetensornetworks/abstracttreetensornetwork.jl b/src/treetensornetworks/abstracttreetensornetwork.jl
index 2b71f5db..f835297b 100644
--- a/src/treetensornetworks/abstracttreetensornetwork.jl
+++ b/src/treetensornetworks/abstracttreetensornetwork.jl
@@ -295,7 +295,7 @@ function Base.:+(
   @assert all(ψ -> nv(first(ψs)) == nv(ψ), ψs)
 
   # Output state
-  ϕ = TTN(siteinds(ψs[1]))
+  ϕ = ttn(siteinds(ψs[1]))
 
   vs = post_order_dfs_vertices(ϕ, root_vertex)
   es = post_order_dfs_edges(ϕ, root_vertex)
diff --git a/src/treetensornetworks/opsum_to_ttn.jl b/src/treetensornetworks/opsum_to_ttn.jl
index d782e818..4ffd1743 100644
--- a/src/treetensornetworks/opsum_to_ttn.jl
+++ b/src/treetensornetworks/opsum_to_ttn.jl
@@ -252,7 +252,7 @@ function ttn_svd(
     link_space[e] = Index(qi...; tags=edge_tag(e), dir=linkdir_ref)
   end
 
-  H = TTN(sites0)   # initialize TTN without the dummy indices added
+  H = ttn(sites0)   # initialize TTN without the dummy indices added
   function qnblock(i::Index, q::QN)
     for b in 2:(nblocks(i) - 1)
       flux(i, Block(b)) == q && return b
@@ -496,12 +496,12 @@ function sorteachterm(os::OpSum, sites::IndsNetwork{V,<:Index}, root_vertex::V)
 end
 
 """
-    TTN(os::OpSum, sites::IndsNetwork{<:Index}; kwargs...)
-    TTN(eltype::Type{<:Number}, os::OpSum, sites::IndsNetwork{<:Index}; kwargs...)
+    ttn(os::OpSum, sites::IndsNetwork{<:Index}; kwargs...)
+    ttn(eltype::Type{<:Number}, os::OpSum, sites::IndsNetwork{<:Index}; kwargs...)
        
 Convert an OpSum object `os` to a TreeTensorNetwork, with indices given by `sites`.
 """
-function TTN(
+function ttn(
   os::OpSum,
   sites::IndsNetwork;
   root_vertex=default_root_vertex(sites),
@@ -530,37 +530,37 @@ function TTN(
 end
 
 function mpo(os::OpSum, external_inds::Vector; kwargs...)
-  return TTN(os, path_indsnetwork(external_inds); kwargs...)
+  return ttn(os, path_indsnetwork(external_inds); kwargs...)
 end
 
 # Conversion from other formats
-function TTN(o::Op, s::IndsNetwork; kwargs...)
-  return TTN(OpSum{Float64}() + o, s; kwargs...)
+function ttn(o::Op, s::IndsNetwork; kwargs...)
+  return ttn(OpSum{Float64}() + o, s; kwargs...)
 end
 
-function TTN(o::Scaled{C,Op}, s::IndsNetwork; kwargs...) where {C}
-  return TTN(OpSum{C}() + o, s; kwargs...)
+function ttn(o::Scaled{C,Op}, s::IndsNetwork; kwargs...) where {C}
+  return ttn(OpSum{C}() + o, s; kwargs...)
 end
 
-function TTN(o::Sum{Op}, s::IndsNetwork; kwargs...)
-  return TTN(OpSum{Float64}() + o, s; kwargs...)
+function ttn(o::Sum{Op}, s::IndsNetwork; kwargs...)
+  return ttn(OpSum{Float64}() + o, s; kwargs...)
 end
 
-function TTN(o::Prod{Op}, s::IndsNetwork; kwargs...)
-  return TTN(OpSum{Float64}() + o, s; kwargs...)
+function ttn(o::Prod{Op}, s::IndsNetwork; kwargs...)
+  return ttn(OpSum{Float64}() + o, s; kwargs...)
 end
 
-function TTN(o::Scaled{C,Prod{Op}}, s::IndsNetwork; kwargs...) where {C}
-  return TTN(OpSum{C}() + o, s; kwargs...)
+function ttn(o::Scaled{C,Prod{Op}}, s::IndsNetwork; kwargs...) where {C}
+  return ttn(OpSum{C}() + o, s; kwargs...)
 end
 
-function TTN(o::Sum{Scaled{C,Op}}, s::IndsNetwork; kwargs...) where {C}
-  return TTN(OpSum{C}() + o, s; kwargs...)
+function ttn(o::Sum{Scaled{C,Op}}, s::IndsNetwork; kwargs...) where {C}
+  return ttn(OpSum{C}() + o, s; kwargs...)
 end
 
 # Catch-all for leaf eltype specification
-function TTN(eltype::Type{<:Number}, os, sites::IndsNetwork; kwargs...)
-  return NDTensors.convert_scalartype(eltype, TTN(os, sites; kwargs...))
+function ttn(eltype::Type{<:Number}, os, sites::IndsNetwork; kwargs...)
+  return NDTensors.convert_scalartype(eltype, ttn(os, sites; kwargs...))
 end
 
 # 
diff --git a/src/treetensornetworks/ttn.jl b/src/treetensornetworks/ttn.jl
index 63148863..09364103 100644
--- a/src/treetensornetworks/ttn.jl
+++ b/src/treetensornetworks/ttn.jl
@@ -26,12 +26,16 @@ end
 
 const TTN = TreeTensorNetwork
 
+function ttn(itensor_network::ITensorNetwork, ortho_center::Vector)
+  return ttn(itensor_network, ortho_center)
+end
+
 function data_graph_type(G::Type{<:TTN})
   return data_graph_type(fieldtype(G, :itensor_network))
 end
 
 function Base.copy(ψ::TTN)
-  return TTN(copy(ψ.itensor_network), copy(ψ.ortho_center))
+  return ttn(copy(ψ.itensor_network), copy(ψ.ortho_center))
 end
 
 # Field access
@@ -44,36 +48,36 @@ data_graph(ψ::TTN) = data_graph(itensor_network(ψ))
 # Constructor
 # 
 
-TTN(tn::ITensorNetwork, args...) = TTN{vertextype(tn)}(tn, args...)
+ttn(tn::ITensorNetwork, args...) = TTN{vertextype(tn)}(tn, args...)
 
 # catch-all for default ElType
-function TTN(g::AbstractGraph, args...; kwargs...)
-  return TTN(Float64, g, args...; kwargs...)
+function ttn(g::AbstractGraph, args...; kwargs...)
+  return ttn(Float64, g, args...; kwargs...)
 end
 
-function TTN(eltype::Type{<:Number}, graph::AbstractGraph, args...; kwargs...)
+function ttn(eltype::Type{<:Number}, graph::AbstractGraph, args...; kwargs...)
   itensor_network = ITensorNetwork(eltype, graph; kwargs...)
-  return TTN(itensor_network, args...)
+  return ttn(itensor_network, args...)
 end
 
 # construct from given state (map)
-function TTN(::Type{ElT}, is::AbstractIndsNetwork, initstate, args...) where {ElT<:Number}
+function ttn(::Type{ElT}, is::AbstractIndsNetwork, initstate, args...) where {ElT<:Number}
   itensor_network = ITensorNetwork(ElT, is, initstate)
-  return TTN(itensor_network, args...)
+  return ttn(itensor_network, args...)
 end
 
 # Constructor from a collection of ITensors.
 # TODO: Support other collections like `Dictionary`,
 # interface for custom vertex names.
-function TTN(ts::ITensorCollection)
-  return TTN(ITensorNetwork(ts))
+function ttn(ts::ITensorCollection)
+  return ttn(ITensorNetwork(ts))
 end
 
 # TODO: Implement `random_circuit_ttn` for non-trivial
 # bond dimensions and correlations.
 # TODO: Implement random_ttn for QN-Index
 function random_ttn(args...; kwargs...)
-  T = TTN(args...; kwargs...)
+  T = ttn(args...; kwargs...)
   randn!.(vertex_data(T))
   normalize!.(vertex_data(T))
   return T
@@ -91,14 +95,14 @@ function random_mps(
   else
     randomMPS(external_inds, states; linkdims=internal_inds_space)
   end
-  return TTN([tn_mps[v] for v in eachindex(tn_mps)])
+  return ttn([tn_mps[v] for v in eachindex(tn_mps)])
 end
 
 #
 # Construction from operator (map)
 #
 
-function TTN(
+function ttn(
   ::Type{ElT},
   sites_map::Pair{<:AbstractIndsNetwork,<:AbstractIndsNetwork},
   ops::Dictionary;
@@ -110,7 +114,7 @@ function TTN(
   for v in vertices(sites)
     os *= Op(ops[v], v)
   end
-  T = TTN(ElT, os, sites; kwargs...)
+  T = ttn(ElT, os, sites; kwargs...)
   # see https://github.com/ITensor/ITensors.jl/issues/526
   lognormT = lognorm(T)
   T /= exp(lognormT / N) # TODO: fix broadcasting for in-place assignment
@@ -119,7 +123,7 @@ function TTN(
   return T
 end
 
-function TTN(
+function ttn(
   ::Type{ElT},
   sites_map::Pair{<:AbstractIndsNetwork,<:AbstractIndsNetwork},
   fops::Function;
@@ -127,10 +131,10 @@ function TTN(
 ) where {ElT<:Number}
   sites = first(sites_map) # TODO: Use the sites_map
   ops = Dictionary(vertices(sites), map(v -> fops(v), vertices(sites)))
-  return TTN(ElT, sites, ops; kwargs...)
+  return ttn(ElT, sites, ops; kwargs...)
 end
 
-function TTN(
+function ttn(
   ::Type{ElT},
   sites_map::Pair{<:AbstractIndsNetwork,<:AbstractIndsNetwork},
   op::String;
@@ -138,7 +142,7 @@ function TTN(
 ) where {ElT<:Number}
   sites = first(sites_map) # TODO: Use the sites_map
   ops = Dictionary(vertices(sites), fill(op, nv(sites)))
-  return TTN(ElT, sites, ops; kwargs...)
+  return ttn(ElT, sites, ops; kwargs...)
 end
 
 # Special constructors
@@ -156,25 +160,8 @@ function mps(external_inds::Vector{<:Vector{<:Index}}; states)
   tn = insert_missing_internal_inds(
     tn, edges(g); internal_inds_space=trivial_space(indtype(external_inds))
   )
-  return TTN(tn)
-end
-
-## function mps(external_inds::Vector{<:Index}; states)
-##   is = path_indsnetwork(external_inds)
-##   tn = TTN(underlying_graph(is))
-##   tn = insert_missing_internal_inds(tn, trivial_space(indtype(is)))
-##   for v in vertices(tn)
-##     @show v
-##     @show tn[v]
-##     tn[v] *= state(only(is[v]), states(v))
-##     @show tn[v]
-##   end
-##   return tn
-## end
-
-## function productTTN(args...; kwargs...)
-##   return TTN(args...; link_space=1, kwargs...)
-## end
+  return ttn(tn)
+end
 
 # 
 # Utility
diff --git a/test/test_additensornetworks.jl b/test/test_additensornetworks.jl
index c0c21d3f..f8bdde47 100644
--- a/test/test_additensornetworks.jl
+++ b/test/test_additensornetworks.jl
@@ -1,7 +1,7 @@
 @eval module $(gensym())
 using Graphs: rem_edge!, vertices
 using NamedGraphs: NamedEdge, hexagonal_lattice_graph, named_grid
-using ITensorNetworks: ITensorNetwork, inner_network, randomITensorNetwork, siteinds
+using ITensorNetworks: ITensorNetwork, inner_network, random_itensornetwork, siteinds
 using ITensors: ITensors, apply, op
 using Random: Random
 using Test: @test, @testset
@@ -32,8 +32,8 @@ using Test: @test, @testset
   rem_edge!(s2, NamedEdge((1, 1) => (1, 2)))
 
   v = rand(vertices(g))
-  ψ1 = randomITensorNetwork(s1; link_space=χ)
-  ψ2 = randomITensorNetwork(s2; link_space=χ)
+  ψ1 = random_itensornetwork(s1; link_space=χ)
+  ψ2 = random_itensornetwork(s2; link_space=χ)
 
   ψ12 = ψ1 + ψ2
 
diff --git a/test/test_apply.jl b/test/test_apply.jl
index f129d6d2..01a82b7c 100644
--- a/test/test_apply.jl
+++ b/test/test_apply.jl
@@ -9,7 +9,7 @@ using ITensorNetworks:
   contract_inner,
   environment,
   norm_network,
-  randomITensorNetwork,
+  random_itensornetwork,
   siteinds,
   update
 using ITensors: ITensors
@@ -25,7 +25,7 @@ using Test: @test, @testset
   g = named_grid(g_dims)
   s = siteinds("S=1/2", g)
   χ = 2
-  ψ = randomITensorNetwork(s; link_space=χ)
+  ψ = random_itensornetwork(s; link_space=χ)
   v1, v2 = (2, 2), (1, 2)
   ψψ = norm_network(ψ)
 
diff --git a/test/test_belief_propagation.jl b/test/test_belief_propagation.jl
index 2ba83ce1..124d2635 100644
--- a/test/test_belief_propagation.jl
+++ b/test/test_belief_propagation.jl
@@ -16,7 +16,7 @@ using ITensorNetworks:
   flatten_networks,
   ising_network,
   linkinds_combiners,
-  randomITensorNetwork,
+  random_itensornetwork,
   siteinds,
   split_index,
   tensornetwork,
@@ -40,7 +40,7 @@ ITensors.disable_warn_order()
   s = siteinds("S=1/2", g)
   χ = 4
   Random.seed!(1234)
-  ψ = randomITensorNetwork(s; link_space=χ)
+  ψ = random_itensornetwork(s; link_space=χ)
 
   ψψ = ψ ⊗ prime(dag(ψ); sites=[])
 
@@ -70,7 +70,7 @@ ITensors.disable_warn_order()
   s = siteinds("S=1/2", g)
   χ = 2
   Random.seed!(1564)
-  ψ = randomITensorNetwork(s; link_space=χ)
+  ψ = random_itensornetwork(s; link_space=χ)
 
   ψψ = ψ ⊗ prime(dag(ψ); sites=[])
 
@@ -117,7 +117,7 @@ ITensors.disable_warn_order()
   s = siteinds("S=1/2", g)
   vs = [(2, 2), (2, 3)]
   χ = 3
-  ψ = randomITensorNetwork(s; link_space=χ)
+  ψ = random_itensornetwork(s; link_space=χ)
   ψψ = ψ ⊗ prime(dag(ψ); sites=[])
 
   bpc = BeliefPropagationCache(ψψ, group(v -> v[1], vertices(ψψ)))
@@ -141,7 +141,7 @@ ITensors.disable_warn_order()
   g = named_grid(g_dims)
   s = siteinds("S=1/2", g)
   χ = 2
-  ψ = randomITensorNetwork(s; link_space=χ)
+  ψ = random_itensornetwork(s; link_space=χ)
   v = (2, 2)
 
   ψψ = flatten_networks(ψ, dag(ψ); combine_linkinds=false, map_bra_linkinds=prime)
diff --git a/test/test_binary_tree_partition.jl b/test/test_binary_tree_partition.jl
index 745072b8..5cc30f5d 100644
--- a/test/test_binary_tree_partition.jl
+++ b/test/test_binary_tree_partition.jl
@@ -16,7 +16,7 @@ using ITensorNetworks:
   ITensorNetwork,
   binary_tree_structure,
   path_graph_structure,
-  randomITensorNetwork
+  random_itensornetwork
 using NamedGraphs: NamedEdge, named_grid, post_order_dfs_vertices
 using OMEinsumContractionOrders: OMEinsumContractionOrders
 using Test: @test, @testset
@@ -56,7 +56,7 @@ end
 @testset "test _binary_tree_partition_inds of a 2D network" begin
   N = (3, 3, 3)
   linkdim = 2
-  network = randomITensorNetwork(IndsNetwork(named_grid(N)); link_space=linkdim)
+  network = random_itensornetwork(IndsNetwork(named_grid(N)); link_space=linkdim)
   tn = Array{ITensor,length(N)}(undef, N...)
   for v in vertices(network)
     tn[v...] = network[v...]
diff --git a/test/test_contract_deltas.jl b/test/test_contract_deltas.jl
index 36eddfe0..ebd6f6b2 100644
--- a/test/test_contract_deltas.jl
+++ b/test/test_contract_deltas.jl
@@ -11,7 +11,7 @@ using ITensorNetworks:
   IndsNetwork,
   ITensorNetwork,
   path_graph_structure,
-  randomITensorNetwork
+  random_itensornetwork
 using NamedGraphs: leaf_vertices, named_grid
 using Test: @test, @testset
 
@@ -38,7 +38,7 @@ end
 @testset "test _contract_deltas over partition" begin
   N = (3, 3, 3)
   linkdim = 2
-  network = randomITensorNetwork(IndsNetwork(named_grid(N)); link_space=linkdim)
+  network = random_itensornetwork(IndsNetwork(named_grid(N)); link_space=linkdim)
   tn = Array{ITensor,length(N)}(undef, N...)
   for v in vertices(network)
     tn[v...] = network[v...]
diff --git a/test/test_contraction_sequence.jl b/test/test_contraction_sequence.jl
index 26ff3e79..9e5d9d68 100644
--- a/test/test_contraction_sequence.jl
+++ b/test/test_contraction_sequence.jl
@@ -1,7 +1,7 @@
 @eval module $(gensym())
 using EinExprs: Exhaustive, Greedy, HyPar
 using ITensorNetworks:
-  contraction_sequence, norm_sqr_network, randomITensorNetwork, siteinds
+  contraction_sequence, norm_sqr_network, random_itensornetwork, siteinds
 using ITensors: ITensors, contract
 using NamedGraphs: named_grid
 using OMEinsumContractionOrders: OMEinsumContractionOrders
@@ -15,7 +15,7 @@ Random.seed!(1234)
     g = named_grid(dims)
     s = siteinds("S=1/2", g)
     χ = 10
-    ψ = randomITensorNetwork(s; link_space=χ)
+    ψ = random_itensornetwork(s; link_space=χ)
     tn = norm_sqr_network(ψ)
     seq_optimal = contraction_sequence(tn; alg="optimal")
     res_optimal = contract(tn; sequence=seq_optimal)[]
diff --git a/test/test_contraction_sequence_to_graph.jl b/test/test_contraction_sequence_to_graph.jl
index 2e21c2a4..cef57410 100644
--- a/test/test_contraction_sequence_to_graph.jl
+++ b/test/test_contraction_sequence_to_graph.jl
@@ -10,7 +10,7 @@ using ITensorNetworks:
   distance_to_leaf,
   flatten_networks,
   leaf_vertices,
-  randomITensorNetwork,
+  random_itensornetwork,
   siteinds
 using Test: @test, @testset
 using NamedGraphs: is_leaf, leaf_vertices, named_grid
@@ -21,7 +21,7 @@ using NamedGraphs: is_leaf, leaf_vertices, named_grid
   g = named_grid(dims)
   s = siteinds("S=1/2", g)
 
-  ψ = randomITensorNetwork(s; link_space=2)
+  ψ = random_itensornetwork(s; link_space=2)
   ψψ = flatten_networks(ψ, ψ)
 
   seq = contraction_sequence(ψψ)
diff --git a/test/test_forms.jl b/test/test_forms.jl
index 75dfd5e8..d30cde40 100644
--- a/test/test_forms.jl
+++ b/test/test_forms.jl
@@ -15,7 +15,7 @@ using ITensorNetworks:
   ket_network,
   ket_vertex,
   operator_network,
-  randomITensorNetwork,
+  random_itensornetwork,
   siteinds,
   tensornetwork,
   union_all_inds,
@@ -32,9 +32,9 @@ using Random: Random
   s_operator = union_all_inds(s_bra, s_ket)
   χ, D = 2, 3
   Random.seed!(1234)
-  ψket = randomITensorNetwork(s_ket; link_space=χ)
-  ψbra = randomITensorNetwork(s_bra; link_space=χ)
-  A = randomITensorNetwork(s_operator; link_space=D)
+  ψket = random_itensornetwork(s_ket; link_space=χ)
+  ψbra = random_itensornetwork(s_bra; link_space=χ)
+  A = random_itensornetwork(s_operator; link_space=D)
 
   blf = BilinearFormNetwork(A, ψbra, ψket)
   @test nv(blf) == nv(ψket) + nv(ψbra) + nv(A)
diff --git a/test/test_gauging.jl b/test/test_gauging.jl
index ce7c8867..97938847 100644
--- a/test/test_gauging.jl
+++ b/test/test_gauging.jl
@@ -7,7 +7,7 @@ using ITensorNetworks:
   contract_inner,
   gauge_error,
   messages,
-  randomITensorNetwork,
+  random_itensornetwork,
   siteinds,
   update
 using ITensors: diagITensor, inds
@@ -25,7 +25,7 @@ using Test: @test, @testset
   χ = 6
 
   Random.seed!(5467)
-  ψ = randomITensorNetwork(s; link_space=χ)
+  ψ = random_itensornetwork(s; link_space=χ)
 
   # Move directly to vidal gauge
   ψ_vidal = VidalITensorNetwork(ψ)
diff --git a/test/test_itensornetwork.jl b/test/test_itensornetwork.jl
index b4200e46..c6d56b84 100644
--- a/test/test_itensornetwork.jl
+++ b/test/test_itensornetwork.jl
@@ -41,7 +41,7 @@ using ITensorNetworks:
   internalinds,
   linkinds,
   orthogonalize,
-  randomITensorNetwork,
+  random_itensornetwork,
   siteinds
 using LinearAlgebra: factorize
 using NamedGraphs: NamedEdge, incident_edges, named_comb_tree, named_grid
@@ -150,7 +150,7 @@ using Test: @test, @test_broken, @testset
     @test has_vertex(tn, ((2, 2), 2))
   end
 
-  @testset "Custom element type" for eltype in (Float32, Float64, ComplexF32, ComplexF64),
+  @testset "Custom element type" for elt in (Float32, Float64, ComplexF32, ComplexF64),
     link_space in (nothing, 3),
     g in (
       grid((4,)),
@@ -160,33 +160,33 @@ using Test: @test, @test_broken, @testset
     )
 
     ψ = ITensorNetwork(g; link_space) do v, inds...
-      return itensor(randn(eltype, dims(inds)...), inds...)
+      return itensor(randn(elt, dims(inds)...), inds...)
     end
-    @test Base.eltype(ψ[first(vertices(ψ))]) == eltype
+    @test eltype(ψ[first(vertices(ψ))]) == elt
     ψ = ITensorNetwork(g; link_space) do v, inds...
       return itensor(randn(dims(inds)...), inds...)
     end
-    @test Base.eltype(ψ[first(vertices(ψ))]) == Float64
-    ψ = randomITensorNetwork(eltype, g; link_space)
-    @test Base.eltype(ψ[first(vertices(ψ))]) == eltype
-    ψ = randomITensorNetwork(g; link_space)
-    @test Base.eltype(ψ[first(vertices(ψ))]) == Float64
-    ψ = ITensorNetwork(eltype, undef, g; link_space)
-    @test Base.eltype(ψ[first(vertices(ψ))]) == eltype
+    @test eltype(ψ[first(vertices(ψ))]) == Float64
+    ψ = random_itensornetwork(elt, g; link_space)
+    @test eltype(ψ[first(vertices(ψ))]) == elt
+    ψ = random_itensornetwork(g; link_space)
+    @test elt(ψ[first(vertices(ψ))]) == Float64
+    ψ = ITensorNetwork(elt, undef, g; link_space)
+    @test eltype(ψ[first(vertices(ψ))]) == elt
     ψ = ITensorNetwork(undef, g)
-    @test Base.eltype(ψ[first(vertices(ψ))]) == Float64
+    @test eltype(ψ[first(vertices(ψ))]) == Float64
   end
 
-  @testset "randomITensorNetwork with custom distributions" begin
+  @testset "random_itensornetwork with custom distributions" begin
     distribution = Uniform(-1.0, 1.0)
-    tn = randomITensorNetwork(distribution, named_grid(4); link_space=2)
+    tn = random_itensornetwork(distribution, named_grid(4); link_space=2)
     # Note: distributions in package `Distributions` currently doesn't support customized
     # eltype, and all elements have type `Float64`
-    @test Base.eltype(tn[first(vertices(tn))]) == Float64
+    @test eltype(tn[first(vertices(tn))]) == Float64
   end
 
   @testset "orthogonalize" begin
-    tn = randomITensorNetwork(named_grid(4); link_space=2)
+    tn = random_itensornetwork(named_grid(4); link_space=2)
     Z = contract(inner_network(tn, tn))[]
 
     tn_ortho = factorize(tn, 4 => 3)
@@ -266,18 +266,18 @@ using Test: @test, @test_broken, @testset
     @test length(internalinds(ψ)) == length(edges(g))
   end
 
-  @testset "ElType conversion, $new_eltype" for new_eltype in (Float32, ComplexF64)
+  @testset "eltype conversion, $new_eltype" for new_eltype in (Float32, ComplexF64)
     dims = (2, 2)
     g = named_grid(dims)
     s = siteinds("S=1/2", g)
-    ψ = randomITensorNetwork(s; link_space=2)
+    ψ = random_itensornetwork(s; link_space=2)
     @test ITensors.scalartype(ψ) == Float64
 
     ϕ = ITensors.convert_leaf_eltype(new_eltype, ψ)
     @test ITensors.scalartype(ϕ) == new_eltype
   end
 
-  @testset "Construction from state map" for ElT in (Float32, ComplexF64)
+  @testset "Construction from state map" for elt in (Float32, ComplexF64)
     dims = (2, 2)
     g = named_grid(dims)
     s = siteinds("S=1/2", g)
@@ -291,13 +291,13 @@ using Test: @test, @test_broken, @testset
     @test abs(t[si => "↑", [b => end for b in bi]...]) == 1.0 # insert_links introduces extra signs through factorization...
     @test t[si => "↓", [b => end for b in bi]...] == 0.0
 
-    ϕ = ITensorNetwork(ElT, s, state_map)
+    ϕ = ITensorNetwork(elt, s, state_map)
     t = ϕ[2, 2]
     si = only(siteinds(ϕ, (2, 2)))
     bi = map(e -> only(linkinds(ϕ, e)), incident_edges(ϕ, (2, 2)))
-    @test eltype(t) == ElT
-    @test abs(t[si => "↑", [b => end for b in bi]...]) == convert(ElT, 1.0) # insert_links introduces extra signs through factorization...
-    @test t[si => "↓", [b => end for b in bi]...] == convert(ElT, 0.0)
+    @test eltype(t) == elt
+    @test abs(t[si => "↑", [b => end for b in bi]...]) == convert(elt, 1.0) # insert_links introduces extra signs through factorization...
+    @test t[si => "↓", [b => end for b in bi]...] == convert(elt, 0.0)
   end
 
   @testset "Priming and tagging" begin
@@ -306,7 +306,7 @@ using Test: @test, @test_broken, @testset
     tooth_lengths = fill(2, 3)
     c = named_comb_tree(tooth_lengths)
     is = siteinds("S=1/2", c)
-    tn = randomITensorNetwork(is; link_space=3)
+    tn = random_itensornetwork(is; link_space=3)
     @test_broken swapprime(tn, 0, 2)
   end
 end
diff --git a/test/test_opsum_to_ttn.jl b/test/test_opsum_to_ttn.jl
index 0a3952b5..5d6c394f 100644
--- a/test/test_opsum_to_ttn.jl
+++ b/test/test_opsum_to_ttn.jl
@@ -16,7 +16,7 @@ using ITensors:
 using ITensors.ITensorMPS: MPO
 using ITensors.NDTensors: matrix
 using ITensorGaussianMPS: hopping_hamiltonian
-using ITensorNetworks: ITensorNetworks, OpSum, TTN, relabel_sites, siteinds
+using ITensorNetworks: ITensorNetworks, OpSum, ttn, relabel_sites, siteinds
 using KrylovKit: eigsolve
 using LinearAlgebra: eigvals, norm
 using NamedGraphs: leaf_vertices, named_comb_tree, named_grid, post_order_dfs_vertices
@@ -61,7 +61,7 @@ end
 
     @testset "Svd approach" for root_vertex in leaf_vertices(is)
       # get TTN Hamiltonian directly
-      Hsvd = TTN(H, is; root_vertex=root_vertex, cutoff=1e-10)
+      Hsvd = ttn(H, is; root_vertex=root_vertex, cutoff=1e-10)
       # get corresponding MPO Hamiltonian
       Hline = MPO(relabel_sites(H, vmap), sites)
       # compare resulting dense Hamiltonians
@@ -72,7 +72,7 @@ end
       @test Tttno ≈ Tmpo rtol = 1e-6
 
       # this breaks for longer range interactions
-      Hsvd_lr = TTN(Hlr, is; root_vertex=root_vertex, algorithm="svd", cutoff=1e-10)
+      Hsvd_lr = ttn(Hlr, is; root_vertex=root_vertex, algorithm="svd", cutoff=1e-10)
       Hline_lr = MPO(relabel_sites(Hlr, vmap), sites)
       @disable_warn_order begin
         Tttno_lr = prod(Hline_lr)
@@ -96,9 +96,9 @@ end
     os1 += 1.0, "Sx", (1, 1)
     os2 = OpSum()
     os2 += 1.0, "Sy", (1, 1)
-    H1 = TTN(os1, s)
-    H2 = TTN(os2, s)
-    H3 = TTN(os1 + os2, s)
+    H1 = ttn(os1, s)
+    H2 = ttn(os2, s)
+    H3 = ttn(os1 + os2, s)
 
     @test H1 + H2 ≈ H3 rtol = 1e-6
     if auto_fermion_enabled
@@ -138,7 +138,7 @@ end
 
     @testset "Svd approach" for root_vertex in leaf_vertices(is)
       # get TTN Hamiltonian directly
-      Hsvd = TTN(H, is; root_vertex=root_vertex, cutoff=1e-10)
+      Hsvd = ttn(H, is; root_vertex=root_vertex, cutoff=1e-10)
       # get corresponding MPO Hamiltonian
       Hline = MPO(relabel_sites(H, vmap), sites)
       # compare resulting sparse Hamiltonians
@@ -150,7 +150,7 @@ end
       @test Tttno ≈ Tmpo rtol = 1e-6
 
       # this breaks for longer range interactions ###not anymore
-      Hsvd_lr = TTN(Hlr, is; root_vertex=root_vertex, algorithm="svd", cutoff=1e-10)
+      Hsvd_lr = ttn(Hlr, is; root_vertex=root_vertex, algorithm="svd", cutoff=1e-10)
       Hline_lr = MPO(relabel_sites(Hlr, vmap), sites)
       @disable_warn_order begin
         Tttno_lr = prod(Hline_lr)
@@ -182,7 +182,7 @@ end
 
     @testset "Svd approach" for root_vertex in leaf_vertices(is)
       # get TTN Hamiltonian directly
-      Hsvd = TTN(H, is; root_vertex=root_vertex, cutoff=1e-10)
+      Hsvd = ttn(H, is; root_vertex=root_vertex, cutoff=1e-10)
       # get corresponding MPO Hamiltonian
       sites = [only(is[v]) for v in reverse(post_order_dfs_vertices(c, root_vertex))]
       vmap = Dictionary(reverse(post_order_dfs_vertices(c, root_vertex)), 1:length(sites))
@@ -252,7 +252,7 @@ end
 
     @testset "Svd approach" for root_vertex in leaf_vertices(is)
       # get TTN Hamiltonian directly
-      Hsvd = TTN(H, is_missing_site; root_vertex=root_vertex, cutoff=1e-10)
+      Hsvd = ttn(H, is_missing_site; root_vertex=root_vertex, cutoff=1e-10)
       # get corresponding MPO Hamiltonian
       Hline = MPO(relabel_sites(H, vmap), sites)
 
@@ -263,7 +263,7 @@ end
       end
       @test Tttno ≈ Tmpo rtol = 1e-6
 
-      Hsvd_lr = TTN(
+      Hsvd_lr = ttn(
         Hlr, is_missing_site; root_vertex=root_vertex, algorithm="svd", cutoff=1e-10
       )
       Hline_lr = MPO(relabel_sites(Hlr, vmap), sites)
diff --git a/test/test_tno.jl b/test/test_tno.jl
index a9868eae..f42ca585 100644
--- a/test/test_tno.jl
+++ b/test/test_tno.jl
@@ -8,7 +8,7 @@ using ITensorNetworks:
   gate_group_to_tno,
   get_tnos,
   ising,
-  randomITensorNetwork,
+  random_itensornetwork,
   siteinds
 using ITensors: ITensor, noprime
 using NamedGraphs: named_grid
@@ -32,7 +32,7 @@ using Test: @test, @testset
   #Construct a single tno which represents prod(gates)
   single_tno = gate_group_to_tno(s, gates)
 
-  ψ = randomITensorNetwork(s; link_space=2)
+  ψ = random_itensornetwork(s; link_space=2)
 
   ψ_gated = copy(ψ)
   for gate in gates
diff --git a/test/test_treetensornetworks/test_expect.jl b/test/test_treetensornetworks/test_expect.jl
index 82db353a..3acbd83b 100644
--- a/test/test_treetensornetworks/test_expect.jl
+++ b/test/test_treetensornetworks/test_expect.jl
@@ -1,7 +1,7 @@
 @eval module $(gensym())
 using Graphs: vertices
 using ITensors.ITensorMPS: MPS
-using ITensorNetworks: TTN, expect, random_mps, siteinds
+using ITensorNetworks: ttn, expect, random_mps, siteinds
 using NamedGraphs: named_comb_tree
 using Test: @test, @testset
 
@@ -27,7 +27,7 @@ end
     magnetization[v] = isodd(i) ? 0.5 : -0.5
   end
   states = v -> d[v]
-  state = TTN(s, states)
+  state = ttn(s, states)
   res = expect("Sz", state)
   @test all([isapprox(res[v], magnetization[v]; atol=1e-8) for v in vertices(s)])
 end
diff --git a/test/test_treetensornetworks/test_position.jl b/test/test_treetensornetworks/test_position.jl
index f1c1e0a8..096141fd 100644
--- a/test/test_treetensornetworks/test_position.jl
+++ b/test/test_treetensornetworks/test_position.jl
@@ -1,7 +1,7 @@
 @eval module $(gensym())
 using Graphs: vertices
 using ITensors: ITensors
-using ITensorNetworks: ITensorNetworks, ProjTTN, TTN, environments, position, siteinds
+using ITensorNetworks: ITensorNetworks, ProjTTN, ttn, environments, position, siteinds
 using NamedGraphs: named_comb_tree
 using Test
 
@@ -23,14 +23,14 @@ using Test
 
   os = ITensorNetworks.heisenberg(c)
 
-  H = TTN(os, s)
+  H = ttn(os, s)
 
   d = Dict()
   for (i, v) in enumerate(vertices(s))
     d[v] = isodd(i) ? "Up" : "Dn"
   end
   states = v -> d[v]
-  psi = TTN(s, states)
+  psi = ttn(s, states)
 
   # actual test, verifies that position is out of place
   vs = vertices(s)
diff --git a/test/test_treetensornetworks/test_solvers/test_contract.jl b/test/test_treetensornetworks/test_solvers/test_contract.jl
index c78b2c64..9e208150 100644
--- a/test/test_treetensornetworks/test_solvers/test_contract.jl
+++ b/test/test_treetensornetworks/test_solvers/test_contract.jl
@@ -5,7 +5,7 @@ using ITensorNetworks:
   OpSum,
   ProjOuterProdTTN,
   ProjTTNSum,
-  TTN,
+  ttn,
   TreeTensorNetwork,
   apply,
   contract,
@@ -105,7 +105,7 @@ end
   psi = normalize(random_ttn(s; link_space=8))
 
   os = ITensorNetworks.heisenberg(c; J1=1, J2=1)
-  H = TTN(os, s)
+  H = ttn(os, s)
 
   # Test basic usage with default parameters
   Hpsi = apply(H, psi; alg="fit", init=psi, nsweeps=1, cutoff=eps())
@@ -120,7 +120,7 @@ end
   # BLAS.axpy-like test
   os_id = OpSum()
   os_id += -1, "Id", vertices(s)[1], "Id", vertices(s)[1]
-  minus_identity = TTN(os_id, s)
+  minus_identity = ttn(os_id, s)
   Hpsi = ITensorNetworks.sum_apply(
     [(H, psi), (minus_identity, psi)]; alg="fit", init=psi, nsweeps=1
   )
diff --git a/test/test_treetensornetworks/test_solvers/test_dmrg.jl b/test/test_treetensornetworks/test_solvers/test_dmrg.jl
index 00eb181b..828b168e 100644
--- a/test/test_treetensornetworks/test_solvers/test_dmrg.jl
+++ b/test/test_treetensornetworks/test_solvers/test_dmrg.jl
@@ -7,7 +7,7 @@ using ITensors.ITensorMPS: MPO, MPS, randomMPS
 using ITensorNetworks:
   ITensorNetworks,
   OpSum,
-  TTN,
+  ttn,
   apply,
   dmrg,
   inner,
@@ -179,7 +179,7 @@ end
 
     os = ITensorNetworks.heisenberg(c)
 
-    H = TTN(os, s)
+    H = ttn(os, s)
 
     # make init_state
     d = Dict()
@@ -187,7 +187,7 @@ end
       d[v] = isodd(i) ? "Up" : "Dn"
     end
     states = v -> d[v]
-    psi = TTN(s, states)
+    psi = ttn(s, states)
 
     #    psi = random_ttn(s; link_space=20) #FIXME: random_ttn broken for QN conserving case
 
@@ -243,14 +243,14 @@ end
   ITensors.enable_auto_fermion()
 
   # now get auto-fermion results 
-  H = TTN(os, s)
+  H = ttn(os, s)
   # make init_state
   d = Dict()
   for (i, v) in enumerate(vertices(s))
     d[v] = isodd(i) ? "Up" : "Dn"
   end
   states = v -> d[v]
-  psi = TTN(s, states)
+  psi = ttn(s, states)
   psi = dmrg(
     H, psi; nsweeps, maxdim, cutoff, nsites, updater_kwargs=(; krylovdim=3, maxiter=1)
   )
@@ -277,7 +277,7 @@ end
   c = named_comb_tree((3, 2))
   s = siteinds("S=1/2", c)
   os = ITensorNetworks.heisenberg(c)
-  H = TTN(os, s)
+  H = ttn(os, s)
   psi = random_ttn(s; link_space=5)
   psi = dmrg(H, psi; nsweeps, maxdim, nsites)
 
diff --git a/test/test_treetensornetworks/test_solvers/test_dmrg_x.jl b/test/test_treetensornetworks/test_solvers/test_dmrg_x.jl
index 505a4775..309144d1 100644
--- a/test/test_treetensornetworks/test_solvers/test_dmrg_x.jl
+++ b/test/test_treetensornetworks/test_solvers/test_dmrg_x.jl
@@ -3,7 +3,7 @@ using Graphs: nv
 using ITensorNetworks:
   ITensorNetworks,
   OpSum,
-  TTN,
+  ttn,
   apply,
   contract,
   dmrg_x,
@@ -60,11 +60,11 @@ end
   # Random fields h ∈ [-W, W]
   h = W * (2 * rand(nv(c)) .- 1)
 
-  H = TTN(ITensorNetworks.heisenberg(c; h), s)
+  H = ttn(ITensorNetworks.heisenberg(c; h), s)
 
-  # TODO: Use `TTN(s; states=v -> rand(["↑", "↓"]))` or
+  # TODO: Use `ttn(s; states=v -> rand(["↑", "↓"]))` or
   # `ttns(s; states=v -> rand(["↑", "↓"]))`
-  ψ = normalize(TTN(s, v -> rand(["↑", "↓"])))
+  ψ = normalize(ttn(s, v -> rand(["↑", "↓"])))
 
   dmrg_x_kwargs = (nsweeps=20, normalize=true, maxdim=20, cutoff=1e-10, outputlevel=0)
 
diff --git a/test/test_treetensornetworks/test_solvers/test_tdvp.jl b/test/test_treetensornetworks/test_solvers/test_tdvp.jl
index 1bc39a19..9b05cd95 100644
--- a/test/test_treetensornetworks/test_solvers/test_tdvp.jl
+++ b/test/test_treetensornetworks/test_solvers/test_tdvp.jl
@@ -4,7 +4,7 @@ using ITensors: ITensor, contract, dag, inner, noprime, normalize, prime, scalar
 using ITensorNetworks:
   ITensorNetworks,
   OpSum,
-  TTN,
+  ttn,
   apply,
   expect,
   mpo,
@@ -420,7 +420,7 @@ end
 
     os = ITensorNetworks.heisenberg(c)
 
-    H = TTN(os, s)
+    H = ttn(os, s)
 
     ψ0 = normalize(random_ttn(s))
 
@@ -460,8 +460,8 @@ end
       os2 += "Sz", src(e), "Sz", dst(e)
     end
 
-    H1 = TTN(os1, s)
-    H2 = TTN(os2, s)
+    H1 = ttn(os1, s)
+    H2 = ttn(os2, s)
     Hs = [H1, H2]
 
     ψ0 = normalize(random_ttn(s; link_space=10))
@@ -496,12 +496,12 @@ end
     s = siteinds("S=1/2", c)
 
     os = ITensorNetworks.heisenberg(c)
-    H = TTN(os, s)
+    H = ttn(os, s)
     HM = contract(H)
 
     Ut = exp(-im * tau * HM)
 
-    state = TTN(ComplexF64, s, v -> iseven(sum(isodd.(v))) ? "Up" : "Dn")
+    state = ttn(ComplexF64, s, v -> iseven(sum(isodd.(v))) ? "Up" : "Dn")
     statex = contract(state)
 
     Sz_tdvp = Float64[]
@@ -544,7 +544,7 @@ end
     s = siteinds("S=1/2", c)
 
     os = ITensorNetworks.heisenberg(c)
-    H = TTN(os, s)
+    H = ttn(os, s)
 
     gates = ITensor[]
     for e in edges(c)
@@ -559,7 +559,7 @@ end
     end
     append!(gates, reverse(gates))
 
-    state = TTN(s, v -> iseven(sum(isodd.(v))) ? "Up" : "Dn")
+    state = ttn(s, v -> iseven(sum(isodd.(v))) ? "Up" : "Dn")
     phi = copy(state)
     c = (2, 1)
 
@@ -598,7 +598,7 @@ end
     # Evolve using TDVP
     # 
 
-    phi = TTN(s, v -> iseven(sum(isodd.(v))) ? "Up" : "Dn")
+    phi = ttn(s, v -> iseven(sum(isodd.(v))) ? "Up" : "Dn")
     obs = observer(
       "Sz" => (; state) -> expect("Sz", state; vertices=[c])[c],
       "En" => (; state) -> real(inner(state', H, state)),
@@ -630,7 +630,7 @@ end
     s = siteinds("S=1/2", c)
 
     os = ITensorNetworks.heisenberg(c)
-    H = TTN(os, s)
+    H = ttn(os, s)
 
     state = normalize(random_ttn(s; link_space=2))
 
diff --git a/test/test_treetensornetworks/test_solvers/test_tdvp_time_dependent.jl b/test/test_treetensornetworks/test_solvers/test_tdvp_time_dependent.jl
index 872a6720..6bae3b7c 100644
--- a/test/test_treetensornetworks/test_solvers/test_tdvp_time_dependent.jl
+++ b/test/test_treetensornetworks/test_solvers/test_tdvp_time_dependent.jl
@@ -1,6 +1,6 @@
 @eval module $(gensym())
 using ITensors: contract
-using ITensorNetworks: ITensorNetworks, TimeDependentSum, TTN, mpo, mps, siteinds, tdvp
+using ITensorNetworks: ITensorNetworks, TimeDependentSum, ttn, mpo, mps, siteinds, tdvp
 using OrdinaryDiffEq: Tsit5
 using KrylovKit: exponentiate
 using LinearAlgebra: norm
@@ -193,7 +193,7 @@ end
   ℋ⃗₀ = [ℋ₁₀, ℋ₂₀]
   H⃗₀ = [TTN(ℋ₀, s) for ℋ₀ in ℋ⃗₀]
 
-  ψ₀ = TTN(ComplexF64, s, v -> iseven(sum(isodd.(v))) ? "↑" : "↓")
+  ψ₀ = ttn(ComplexF64, s, v -> iseven(sum(isodd.(v))) ? "↑" : "↓")
 
   ψₜ_ode = tdvp(
     H⃗₀,
diff --git a/test/test_ttno.jl b/test/test_ttno.jl
index f134cf20..4a25a871 100644
--- a/test/test_ttno.jl
+++ b/test/test_ttno.jl
@@ -1,6 +1,6 @@
 @eval module $(gensym())
 using Graphs: vertices
-using ITensorNetworks: TTN, contract, ortho_center, siteinds, union_all_inds
+using ITensorNetworks: ttn, contract, ortho_center, siteinds, union_all_inds
 using ITensors: @disable_warn_order, prime, randomITensor
 using LinearAlgebra: norm
 using NamedGraphs: named_comb_tree
@@ -26,15 +26,15 @@ using Test: @test, @testset
     # create random ITensor with these indices
     O = randomITensor(sites_o...)
     # dense TTN constructor from IndsNetwork
-    @disable_warn_order o1 = TTN(O, is_isp; cutoff)
+    @disable_warn_order o1 = ttn(O, is_isp; cutoff)
     # dense TTN constructor from Vector{Vector{Index}} and NamedDimGraph
-    @disable_warn_order o2 = TTN(O, sites_o, c; vertex_order, cutoff)
+    @disable_warn_order o2 = ttn(O, sites_o, c; vertex_order, cutoff)
     # convert to array with proper index order
     AO = Array(O, sites_o...)
     # dense array constructor from IndsNetwork
-    @disable_warn_order o3 = TTN(AO, is_isp; vertex_order, cutoff)
+    @disable_warn_order o3 = ttn(AO, is_isp; vertex_order, cutoff)
     # dense array constructor from Vector{Vector{Index}} and NamedDimGraph
-    @disable_warn_order o4 = TTN(AO, sites_o, c; vertex_order, cutoff)
+    @disable_warn_order o4 = ttn(AO, sites_o, c; vertex_order, cutoff)
     # see if this actually worked
     root_vertex = only(ortho_center(o1))
     @disable_warn_order begin
diff --git a/test/test_ttns.jl b/test/test_ttns.jl
index 81bc9760..5c7cece6 100644
--- a/test/test_ttns.jl
+++ b/test/test_ttns.jl
@@ -1,7 +1,7 @@
 @eval module $(gensym())
 using DataGraphs: vertex_data
 using Graphs: vertices
-using ITensorNetworks: TTN, contract, ortho_center, siteinds
+using ITensorNetworks: ttn, contract, ortho_center, siteinds
 using ITensors: @disable_warn_order, randomITensor
 using LinearAlgebra: norm
 using NamedGraphs: named_comb_tree
@@ -25,15 +25,15 @@ using Test: @test, @testset
     # create random ITensor with these indices
     S = randomITensor(vertex_data(is)...)
     # dense TTN constructor from IndsNetwork
-    @disable_warn_order s1 = TTN(S, is; cutoff)
+    @disable_warn_order s1 = ttn(S, is; cutoff)
     # dense TTN constructor from Vector{Index} and NamedDimGraph
-    @disable_warn_order s2 = TTN(S, sites_s, c; vertex_order, cutoff)
+    @disable_warn_order s2 = ttn(S, sites_s, c; vertex_order, cutoff)
     # convert to array with proper index order
     @disable_warn_order AS = Array(S, sites_s...)
     # dense array constructor from IndsNetwork
-    @disable_warn_order s3 = TTN(AS, is; vertex_order, cutoff)
+    @disable_warn_order s3 = ttn(AS, is; vertex_order, cutoff)
     # dense array constructor from Vector{Index} and NamedDimGraph
-    @disable_warn_order s4 = TTN(AS, sites_s, c; vertex_order, cutoff)
+    @disable_warn_order s4 = ttn(AS, sites_s, c; vertex_order, cutoff)
     # see if this actually worked
     root_vertex = only(ortho_center(s1))
     @disable_warn_order begin