diff --git a/examples/basics.jl b/examples/basics.jl
index 3594bdab..fbb48bcd 100644
--- a/examples/basics.jl
+++ b/examples/basics.jl
@@ -189,7 +189,7 @@ This is important to keep in mind when testing new models.
 In many applications, we have access to various observation sequences of different lengths.
 =#
 
-nb_seqs = 300
+nb_seqs = 1000
 long_obs_seqs = [last(rand(rng, hmm, rand(rng, 100:200))) for k in 1:nb_seqs];
 typeof(long_obs_seqs)
 
@@ -258,6 +258,5 @@ hcat(initialization(hmm_est_concat), initialization(hmm))
 # ## Tests  #src
 
 control_seq = fill(nothing, last(seq_ends));  #src
-test_identical_hmmbase(rng, hmm, 100; hmm_guess)  #src
 test_coherent_algorithms(rng, hmm, control_seq; seq_ends, hmm_guess)  #src
 test_type_stability(rng, hmm, control_seq; seq_ends, hmm_guess)  #src
diff --git a/examples/types.jl b/examples/types.jl
index 0945be63..628155c7 100644
--- a/examples/types.jl
+++ b/examples/types.jl
@@ -156,9 +156,8 @@ Another useful array type is [StaticArrays.jl](https://github.com/JuliaArrays/St
 
 @test nnz(log_transition_matrix(hmm)) == nnz(transition_matrix(hmm))  #src
 
-seq_ends = cumsum(rand(rng, 100:200, 100));  #src
+seq_ends = cumsum(rand(rng, 100:200, 1000));  #src
 control_seq = fill(nothing, last(seq_ends));  #src
-test_identical_hmmbase(rng, hmm, 100; hmm_guess)  #src
 test_coherent_algorithms(rng, hmm, control_seq; seq_ends, hmm_guess, init=false, atol=0.08)  #src
 test_type_stability(rng, hmm, control_seq; seq_ends, hmm_guess)  #src
 # https://github.com/JuliaSparse/SparseArrays.jl/issues/469  #src
diff --git a/libs/HMMTest/ext/HMMTestHMMBaseExt.jl b/libs/HMMTest/ext/HMMTestHMMBaseExt.jl
index f3c1c379..b13e7f64 100644
--- a/libs/HMMTest/ext/HMMTestHMMBaseExt.jl
+++ b/libs/HMMTest/ext/HMMTestHMMBaseExt.jl
@@ -3,7 +3,7 @@ module HMMTestHMMBaseExt
 using HiddenMarkovModels
 import HiddenMarkovModels as HMMs
 using HMMBase: HMMBase
-using HMMTest: HMMTest
+using HMMTest
 using Random: AbstractRNG
 using Statistics: mean
 using Test: @test, @testset, @test_broken