diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index c7836288..368681b3 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -19,14 +19,11 @@ jobs:
           - os: ubuntu-latest
             python: "3.13"
             toxenv: base
-          #- os: ubuntu-latest
-          #  python: "3.12"
-          #  toxenv: base
           - os: ubuntu-latest
-            python: "3.11"
+            python: "3.12"
             toxenv: base
           - os: ubuntu-latest
-            python: "3.10"
+            python: "3.11"
             toxenv: base
           - os: ubuntu-latest
             python: "3.11"
@@ -68,9 +65,9 @@ jobs:
             toxenv: external-notebooks
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python }}
           cache: pip
@@ -79,18 +76,28 @@ jobs:
         if: ${{ startsWith(matrix.toxenv, 'external-') }}
         uses: julia-actions/setup-julia@v2
         with:
-          version: "1.11"
+          version: "1"  # Use the latest stable Julia version
 
       - name: Install Julia dependencies
         if: ${{ startsWith(matrix.toxenv, 'external-') }}
-        run: julia -e 'using Pkg; Pkg.add("PyCall")'
+        env:
+          # make Julia/PyCall bind to the same Python used by the job
+          PYTHON: ${{ env.pythonLocation }}/bin/python
+        run: |
+          julia -e 'using Pkg; Pkg.add("PyCall")'
+
+      - name: Install R
+        if: ${{ startsWith(matrix.toxenv, 'external-') }}
+        uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: 'release'
 
       - name: Install dependencies
         run: |
           case "${{ matrix.toxenv }}" in
-            base|visualization|mac|base-notebooks) .github/workflows/install_deps.sh base R ;;
+            base|visualization|mac|base-notebooks) .github/workflows/install_deps.sh base ;;
             petab) .github/workflows/install_deps.sh amici ;;
-            external-*) .github/workflows/install_deps.sh base R amici ;;
+            external-*) .github/workflows/install_deps.sh base amici ;;
             lint|project|doc|migrate) .github/workflows/install_deps.sh doc ;;
           esac
           python -m pip install -U pip tox
@@ -100,8 +107,9 @@ jobs:
 
       # If each tox env generates coverage.xml, include flags; otherwise combine explicitly
       - name: Upload coverage
-        if: ${{ always() && matrix.os == 'ubuntu-latest' }}  # optional
-        uses: codecov/codecov-action@v4
+        if: ${{ always() && matrix.os == 'ubuntu-latest' }}
+        uses: codecov/codecov-action@v6
+        continue-on-error: true
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           files: ./coverage.xml
diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
index 972f4ea6..bb4f6cf1 100644
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -14,10 +14,10 @@ jobs:
 
     steps:
       - name: Check out repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Prepare Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: "3.12"
           cache: pip
diff --git a/.github/workflows/install_deps.sh b/.github/workflows/install_deps.sh
index d497a6f3..95f35310 100755
--- a/.github/workflows/install_deps.sh
+++ b/.github/workflows/install_deps.sh
@@ -48,29 +48,10 @@ install_base() {
   fi
 }
 
-install_r() {
-  log_info "Installing R..."
-  if is_macos; then
-    brew install r
-  else
-    # Prefer distro packages in CI
-    apt_install libtirpc-dev r-base r-base-dev
-    # Make R shared libs discoverable for the rest of the job
-    export_env_var LD_LIBRARY_PATH "${LD_LIBRARY_PATH:-/usr/lib}:/usr/lib/R/lib:/usr/local/lib/R/lib"
-  fi
-
-  if command -v R >/dev/null 2>&1; then
-    log_info "R installed: $(R --version | head -n1)"
-  else
-    log_error "R installation failed (R not on PATH)"
-    exit 1
-  fi
-}
-
 install_amici() {
   log_info "Installing AMICI dependencies..."
   if ! is_macos; then
-    apt_install swig libatlas-base-dev libhdf5-serial-dev libboost-all-dev
+    apt_install libhdf5-serial-dev libboost-all-dev
   fi
   log_info "Installing AMICI Python package..."
   python -m pip uninstall -y amici pyabc || true
@@ -93,7 +74,6 @@ install_dev_tools() {
 
 install_all() {
   install_base
-  install_r
   install_amici
   install_doc_tools
   install_dev_tools
@@ -105,7 +85,6 @@ Usage: $0 [OPTION]...
 
 Options:
   base        Install base dependencies (Redis)
-  R           Install R (Ubuntu: apt; macOS: brew)
   amici       Install AMICI dependencies
   doc         Install documentation tools (Pandoc)
   dev         Install development tools
@@ -124,7 +103,6 @@ main() {
   for arg in "$@"; do
     case "$arg" in
       base)  install_base ;;
-      R)     install_r ;;
       amici) install_amici ;;
       doc)   install_doc_tools ;;
       dev)   install_dev_tools ;;
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 1487ed83..baa15b38 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -61,7 +61,7 @@ repos:
 
 # Configuration
 default_language_version:
-  python: python3.11
+  python: python3.13
 
 # Global exclusions
 exclude: |
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index f057d194..147afb7c 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -7,6 +7,14 @@ Release Notes
 0.12 Series
 ...........
 
+0.12.18 (2026-04-14)
+--------------------
+
+General:
+
+* Support of amici 1.0.0
+* Remove support for python 3.10
+
 0.12.17 (2026-02-24)
 --------------------
 
diff --git a/doc/examples/model_selection.ipynb b/doc/examples/model_selection.ipynb
index fa40661c..28a02b07 100644
--- a/doc/examples/model_selection.ipynb
+++ b/doc/examples/model_selection.ipynb
@@ -83,7 +83,7 @@
     "    # sample from a gaussian\n",
     "    y = st.norm(parameters.x, sigma).rvs()\n",
     "    # return the sample as dictionary\n",
-    "    return {\"y\": y}"
+    "    return {'y': y}"
    ]
   },
   {
@@ -110,8 +110,8 @@
     "# Their mean differs.\n",
     "mu_x_1, mu_x_2 = 0, 1\n",
     "parameter_priors = [\n",
-    "    pyabc.Distribution(x=pyabc.RV(\"norm\", mu_x_1, sigma)),\n",
-    "    pyabc.Distribution(x=pyabc.RV(\"norm\", mu_x_2, sigma)),\n",
+    "    pyabc.Distribution(x=pyabc.RV('norm', mu_x_1, sigma)),\n",
+    "    pyabc.Distribution(x=pyabc.RV('norm', mu_x_2, sigma)),\n",
     "]"
    ]
   },
@@ -142,7 +142,7 @@
    "source": [
     "# We plug all the ABC options together\n",
     "abc = pyabc.ABCSMC(\n",
-    "    models, parameter_priors, pyabc.PercentileDistance(measures_to_use=[\"y\"])\n",
+    "    models, parameter_priors, pyabc.PercentileDistance(measures_to_use=['y'])\n",
     ")"
    ]
   },
@@ -176,8 +176,8 @@
     "# y_observed is the important piece here: our actual observation.\n",
     "y_observed = 1\n",
     "# and we define where to store the results\n",
-    "db_path = \"sqlite:///\" + os.path.join(tempfile.gettempdir(), \"test.db\")\n",
-    "history = abc.new(db_path, {\"y\": y_observed})"
+    "db_path = 'sqlite:///' + os.path.join(tempfile.gettempdir(), 'test.db')\n",
+    "history = abc.new(db_path, {'y': y_observed})"
    ]
   },
   {
@@ -205,7 +205,7 @@
     }
    ],
    "source": [
-    "print(\"ABC-SMC run ID:\", history.id)"
+    "print('ABC-SMC run ID:', history.id)"
    ]
   },
   {
@@ -270,7 +270,7 @@
     }
    ],
    "source": [
-    "history is abc.history"
+    "assert history is abc.history"
    ]
   },
   {
@@ -351,9 +351,9 @@
     }
    ],
    "source": [
-    "# Evaluate the model probabililties\n",
+    "# Evaluate the model probabilities\n",
     "model_probabilities = history.get_model_probabilities()\n",
-    "model_probabilities"
+    "print(model_probabilities)"
    ]
   },
   {
diff --git a/doc/examples/multiscale_agent_based.ipynb b/doc/examples/multiscale_agent_based.ipynb
index c03c0681..e1b5aa59 100644
--- a/doc/examples/multiscale_agent_based.ipynb
+++ b/doc/examples/multiscale_agent_based.ipynb
@@ -118,7 +118,7 @@
     "    ecm_degradation_rate=8e-4,\n",
     "    ecm_division_threshold=1e-2,\n",
     ")\n",
-    "print(f\"Simulation took {time() - start_time:.2f}s\")"
+    "print(f'Simulation took {time() - start_time:.2f}s')"
    ]
   },
   {
@@ -157,29 +157,29 @@
     "fig.set_size_inches((16, 5))\n",
     "\n",
     "color = {\n",
-    "    \"growth_curve\": \"k\",\n",
-    "    \"extra_cellular_matrix_profile\": \"green\",\n",
-    "    \"proliferation_profile\": \"orange\",\n",
+    "    'growth_curve': 'k',\n",
+    "    'extra_cellular_matrix_profile': 'green',\n",
+    "    'proliferation_profile': 'orange',\n",
     "}\n",
     "\n",
     "x_label = {\n",
-    "    \"growth_curve\": \"Time (d)\",\n",
-    "    \"extra_cellular_matrix_profile\": \"Distance to rim (μm)\",\n",
-    "    \"proliferation_profile\": \"Distance to rim (μm)\",\n",
+    "    'growth_curve': 'Time (d)',\n",
+    "    'extra_cellular_matrix_profile': 'Distance to rim (μm)',\n",
+    "    'proliferation_profile': 'Distance to rim (μm)',\n",
     "}\n",
     "\n",
     "y_label = {\n",
-    "    \"growth_curve\": \"Radius (μm)\",\n",
-    "    \"extra_cellular_matrix_profile\": \"Extracellular matrix intensity\",\n",
-    "    \"proliferation_profile\": \"Fraction proliferating cells\",\n",
+    "    'growth_curve': 'Radius (μm)',\n",
+    "    'extra_cellular_matrix_profile': 'Extracellular matrix intensity',\n",
+    "    'proliferation_profile': 'Fraction proliferating cells',\n",
     "}\n",
     "\n",
     "for ax, (key, val) in zip(axes, observation.items()):\n",
     "    ax.plot(val, color=color[key])\n",
-    "    ax.set_title(capwords(key.replace(\"_\", \" \")))\n",
+    "    ax.set_title(capwords(key.replace('_', ' ')))\n",
     "    ax.set_xlabel(x_label[key])\n",
     "    ax.set_ylabel(y_label[key])\n",
-    "    if key.endswith(\"profile\"):\n",
+    "    if key.endswith('profile'):\n",
     "        ax.set_xlim(0, 600)"
    ]
   },
@@ -225,7 +225,7 @@
     ")\n",
     "\n",
     "prior = Distribution(\n",
-    "    **{key: RV(\"uniform\", a, b - a) for key, (a, b) in limits.items()}\n",
+    "    **{key: RV('uniform', a, b - a) for key, (a, b) in limits.items()}\n",
     ")"
    ]
   },
@@ -309,7 +309,7 @@
     }
    ],
    "source": [
-    "abc.new(\"sqlite:////tmp/test.db\", data_mean)"
+    "abc.new('sqlite:////tmp/test.db', data_mean)"
    ]
   },
   {
@@ -420,7 +420,7 @@
     "\n",
     "from pyabc import History\n",
     "\n",
-    "h_loaded = History(\"sqlite:///\" + stored_data_db)\n",
+    "h_loaded = History('sqlite:///' + stored_data_db)\n",
     "\n",
     "df, w = h_loaded.get_distribution(m=0, t=5)\n",
     "plot_kde_matrix(df, w, limits=limits);"
@@ -490,7 +490,7 @@
    ],
    "source": [
     "populations = h_loaded.get_all_populations()\n",
-    "(populations[populations.t >= 0].plot(\"t\", \"particles\", marker=\"o\"));"
+    "(populations[populations.t >= 0].plot('t', 'particles', marker='o'));"
    ]
   },
   {
@@ -516,7 +516,7 @@
     }
    ],
    "source": [
-    "print(f\"Execution time: {(time() - start_time)/60:.1f}m\")"
+    "print(f'Execution time: {(time() - start_time)/60:.1f}m')"
    ]
   },
   {
diff --git a/doc/examples/parameter_inference.ipynb b/doc/examples/parameter_inference.ipynb
index 4a2bca24..5720c4a9 100644
--- a/doc/examples/parameter_inference.ipynb
+++ b/doc/examples/parameter_inference.ipynb
@@ -91,7 +91,7 @@
    "outputs": [],
    "source": [
     "def model(parameter):\n",
-    "    return {\"data\": parameter[\"mu\"] + 0.5 * np.random.randn()}"
+    "    return {'data': parameter['mu'] + 0.5 * np.random.randn()}"
    ]
   },
   {
@@ -109,7 +109,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "prior = pyabc.Distribution(mu=pyabc.RV(\"uniform\", 0, 5))"
+    "prior = pyabc.Distribution(mu=pyabc.RV('uniform', 0, 5))"
    ]
   },
   {
@@ -138,7 +138,7 @@
    "outputs": [],
    "source": [
     "def distance(x, x0):\n",
-    "    return abs(x[\"data\"] - x0[\"data\"])"
+    "    return abs(x['data'] - x0['data'])"
    ]
   },
   {
@@ -211,9 +211,9 @@
     }
    ],
    "source": [
-    "db_path = os.path.join(tempfile.gettempdir(), \"test.db\")\n",
+    "db_path = os.path.join(tempfile.gettempdir(), 'test.db')\n",
     "observation = 2.5\n",
-    "abc.new(\"sqlite:///\" + db_path, {\"data\": observation})"
+    "abc.new('sqlite:///' + db_path, {'data': observation})"
    ]
   },
   {
@@ -314,12 +314,12 @@
     "        w,\n",
     "        xmin=0,\n",
     "        xmax=5,\n",
-    "        x=\"mu\",\n",
-    "        xname=r\"$\\mu$\",\n",
+    "        x='mu',\n",
+    "        xname=r'$\\mu$',\n",
     "        ax=ax,\n",
-    "        label=f\"PDF t={t}\",\n",
+    "        label=f'PDF t={t}',\n",
     "    )\n",
-    "ax.axvline(observation, color=\"k\", linestyle=\"dashed\")\n",
+    "ax.axvline(observation, color='k', linestyle='dashed')\n",
     "ax.legend();"
    ]
   },
diff --git a/doc/examples/petab_application.ipynb b/doc/examples/petab_application.ipynb
index 7332092e..cf7e13b2 100644
--- a/doc/examples/petab_application.ipynb
+++ b/doc/examples/petab_application.ipynb
@@ -31,7 +31,7 @@
    "outputs": [],
    "source": [
     "import petab.v1 as petab\n",
-    "from amici.petab import import_petab_problem\n",
+    "from amici.importers.petab.v1 import import_petab_problem\n",
     "\n",
     "from pyabc.petab import AmiciPetabImporter"
    ]
@@ -91,7 +91,7 @@
     "model = import_petab_problem(petab_problem)\n",
     "\n",
     "# the solver to numerically solve the ODE\n",
-    "solver = model.getSolver()\n",
+    "solver = model.create_solver()\n",
     "\n",
     "# import everything to pyABC\n",
     "importer = AmiciPetabImporter(petab_problem, model, solver)\n",
@@ -197,7 +197,7 @@
     "acceptor = pyabc.StochasticAcceptor(\n",
     "    pdf_norm_method = pyabc.ScaledPDFNorm())\n",
     "\n",
-    "abc = pyabc.ABCSMC(model, prior, kernel, \n",
+    "abc = pyabc.ABCSMC(model, prior, kernel,\n",
     "                   eps=temperature,\n",
     "                   acceptor=acceptor,\n",
     "                   sampler=sampler,\n",
diff --git a/doc/examples/petab_yaml2sbml.ipynb b/doc/examples/petab_yaml2sbml.ipynb
index 8c595784..b395a419 100644
--- a/doc/examples/petab_yaml2sbml.ipynb
+++ b/doc/examples/petab_yaml2sbml.ipynb
@@ -36,7 +36,7 @@
     "\n",
     "import amici\n",
     "import numpy as np\n",
-    "from amici.petab import import_petab_problem\n",
+    "from amici.importers.petab.v1 import import_petab_problem\n",
     "\n",
     "import pyabc\n",
     "import pyabc.petab\n",
@@ -219,13 +219,13 @@
     "    sys.path.insert(0, os.path.abspath(amici_dir))\n",
     "model = import_petab_problem(\n",
     "    petab_problem,\n",
-    "    model_output_dir=amici_dir,\n",
+    "    output_dir=amici_dir,\n",
     "    verbose=False,\n",
     "    generate_sensitivity_code=False,\n",
     ")\n",
     "\n",
     "# the solver to numerically solve the ODE\n",
-    "solver = model.getSolver()\n",
+    "solver = model.create_solver()\n",
     "\n",
     "# import everything to pyABC\n",
     "importer = pyabc.petab.AmiciPetabImporter(petab_problem, model, solver)\n",
@@ -421,16 +421,16 @@
     "    if amici_dir not in sys.path:\n",
     "        sys.path.insert(0, os.path.abspath(amici_dir))\n",
     "    model_module = importlib.import_module(model_name)\n",
-    "    model = model_module.getModel()\n",
-    "    solver = model.getSolver()\n",
+    "    model = model_module.get_model()\n",
+    "    solver = model.create_solver()\n",
     "\n",
     "    # measurement times\n",
     "    n_time = 10\n",
     "    meas_times = np.linspace(0, 10, n_time)\n",
-    "    model.setTimepoints(meas_times)\n",
+    "    model.set_timepoints(meas_times)\n",
     "\n",
     "    # simulate with nominal parameters\n",
-    "    rdata = amici.runAmiciSimulation(model, solver)\n",
+    "    rdata = model.simulate(solver=solver)\n",
     "\n",
     "    # create noisy data\n",
     "    np.random.seed(2)\n",
diff --git a/doc/examples/resuming.ipynb b/doc/examples/resuming.ipynb
index a9112e05..e8e924d7 100644
--- a/doc/examples/resuming.ipynb
+++ b/doc/examples/resuming.ipynb
@@ -81,17 +81,17 @@
    "outputs": [],
    "source": [
     "def model(parameter):\n",
-    "    return {\"data\": parameter[\"mean\"] + np.random.randn()}\n",
+    "    return {'data': parameter['mean'] + np.random.randn()}\n",
     "\n",
     "\n",
-    "prior = Distribution(mean=RV(\"uniform\", 0, 5))\n",
+    "prior = Distribution(mean=RV('uniform', 0, 5))\n",
     "\n",
     "\n",
     "def distance(x, y):\n",
-    "    return abs(x[\"data\"] - y[\"data\"])\n",
+    "    return abs(x['data'] - y['data'])\n",
     "\n",
     "\n",
-    "db = \"sqlite:///\" + os.path.join(gettempdir(), \"test.db\")"
+    "db = 'sqlite:///' + os.path.join(gettempdir(), 'test.db')"
    ]
   },
   {
@@ -126,9 +126,9 @@
    ],
    "source": [
     "abc = ABCSMC(model, prior, distance)\n",
-    "history = abc.new(db, {\"data\": 2.5})\n",
+    "history = abc.new(db, {'data': 2.5})\n",
     "run_id = history.id\n",
-    "print(\"Run ID:\", run_id)"
+    "print('Run ID:', run_id)"
    ]
   },
   {
@@ -191,9 +191,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "history.n_populations"
-   ]
+   "source": "print(history.n_populations)"
   },
   {
    "cell_type": "markdown",
@@ -317,9 +315,7 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "abc_continued.history.n_populations"
-   ]
+   "source": "print(abc_continued.history.n_populations)"
   },
   {
    "cell_type": "markdown",
diff --git a/doc/examples/sde_ion_channels.ipynb b/doc/examples/sde_ion_channels.ipynb
index 727cae79..4ab8a423 100644
--- a/doc/examples/sde_ion_channels.ipynb
+++ b/doc/examples/sde_ion_channels.ipynb
@@ -77,10 +77,10 @@
     "import requests\n",
     "\n",
     "URL = (\n",
-    "    \"https://senselab.med.yale.edu/modeldb/\"\n",
-    "    \"eavBinDown.cshtml?o=128502&a=23&mime=application/zip\"\n",
+    "    'https://senselab.med.yale.edu/modeldb/'\n",
+    "    'eavBinDown.cshtml?o=128502&a=23&mime=application/zip'\n",
     ")\n",
-    "req = requests.request(\"GET\", URL)"
+    "req = requests.request('GET', URL)"
    ]
   },
   {
@@ -117,10 +117,10 @@
     "archive = ZipFile(BytesIO(req.content))\n",
     "archive.extractall(tempdir)\n",
     "ret = subprocess.run(\n",
-    "    [\"make\", \"HH_run\"], cwd=os.path.join(tempdir, \"ModelDBFolder\")\n",
+    "    ['make', 'HH_run'], cwd=os.path.join(tempdir, 'ModelDBFolder')\n",
     ")\n",
-    "EXEC = os.path.join(tempdir, \"ModelDBFolder\", \"HH_run\")\n",
-    "print(f\"The executable location is {EXEC}\")"
+    "EXEC = os.path.join(tempdir, 'ModelDBFolder', 'HH_run')\n",
+    "print(f'The executable location is {EXEC}')"
    ]
   },
   {
@@ -176,11 +176,11 @@
     "            EXEC,\n",
     "            str(model),\n",
     "            # the binary cannot very long floats\n",
-    "            f\"{membrane_area:.5f}\",\n",
+    "            f'{membrane_area:.5f}',\n",
     "            str(time_steps),\n",
     "            str(time_step_size),\n",
     "            str(isi),\n",
-    "            f\"{dc:.5f}\",\n",
+    "            f'{dc:.5f}',\n",
     "            str(noise),\n",
     "            str(sine_amplitude),\n",
     "            str(sine_frequency),\n",
@@ -195,7 +195,7 @@
     "        delim_whitespace=True,\n",
     "        header=None,\n",
     "        index_col=0,\n",
-    "        names=[\"t\", \"V\", \"Na\", \"K\"],\n",
+    "        names=['t', 'V', 'Na', 'K'],\n",
     "    )\n",
     "    return df"
    ]
@@ -239,19 +239,19 @@
     "\n",
     "%matplotlib inline\n",
     "\n",
-    "gt = {\"dc\": 20, \"membrane_dim\": 10}\n",
+    "gt = {'dc': 20, 'membrane_dim': 10}\n",
     "fig, axes = plt.subplots(nrows=2, sharex=True)\n",
     "fig.set_size_inches((12, 8))\n",
     "for _ in range(10):\n",
     "    observation = simulate(**gt)\n",
-    "    observation.plot(y=\"K\", color=\"C1\", ax=axes[0])\n",
-    "    observation.plot(y=\"Na\", color=\"C0\", ax=axes[1])\n",
+    "    observation.plot(y='K', color='C1', ax=axes[0])\n",
+    "    observation.plot(y='Na', color='C0', ax=axes[1])\n",
     "for ax in axes:\n",
     "    ax.legend().set_visible(False)\n",
-    "axes[0].set_title(\"K\")\n",
-    "axes[0].set_ylabel(\"K\")\n",
-    "axes[1].set_title(\"Na\")\n",
-    "axes[1].set_ylabel(\"Na\");"
+    "axes[0].set_title('K')\n",
+    "axes[0].set_ylabel('K')\n",
+    "axes[1].set_title('Na')\n",
+    "axes[1].set_ylabel('Na');"
    ]
   },
   {
@@ -291,8 +291,8 @@
     "dcmin, dcmax = 2, 30\n",
     "memmin, memmax = 1, 12\n",
     "prior = Distribution(\n",
-    "    dc=RV(\"uniform\", dcmin, dcmax - dcmin),\n",
-    "    membrane_dim=RV(\"uniform\", memmin, memmax - memmin),\n",
+    "    dc=RV('uniform', dcmin, dcmax - dcmin),\n",
+    "    membrane_dim=RV('uniform', memmin, memmax - memmin),\n",
     ")"
    ]
   },
@@ -310,7 +310,7 @@
    "outputs": [],
    "source": [
     "def distance(x, y):\n",
-    "    diff = x[\"data\"][\"K\"] - y[\"data\"][\"K\"]\n",
+    "    diff = x['data']['K'] - y['data']['K']\n",
     "    dist = np.sqrt(np.sum(diff**2))\n",
     "    return dist"
    ]
@@ -331,7 +331,7 @@
    "source": [
     "def simulate_pyabc(parameter):\n",
     "    res = simulate(**parameter)\n",
-    "    return {\"data\": res}"
+    "    return {'data': res}"
    ]
   },
   {
@@ -388,7 +388,7 @@
    "source": [
     "abc = ABCSMC(simulate_pyabc, prior, distance, population_size=100)\n",
     "abc_id = abc.new(\n",
-    "    \"sqlite:///\" + os.path.join(tempdir, \"test.db\"), {\"data\": observation}\n",
+    "    'sqlite:///' + os.path.join(tempdir, 'test.db'), {'data': observation}\n",
     ")\n",
     "history = abc.run(max_nr_populations=10, minimum_epsilon=6)"
    ]
@@ -446,7 +446,7 @@
     "dfw = history.get_distribution(m=0)\n",
     "plot_kde_matrix(\n",
     "    *dfw,\n",
-    "    limits={\"dc\": (dcmin, dcmax), \"membrane_dim\": (memmin, memmax)},\n",
+    "    limits={'dc': (dcmin, dcmax), 'membrane_dim': (memmin, memmax)},\n",
     "    refval=gt,\n",
     "    refval_color='k',\n",
     ")"
@@ -511,7 +511,7 @@
     "alpha = 0.5\n",
     "for _ in range(n):\n",
     "    prior_sample = simulate(**prior.rvs())\n",
-    "    prior_sample.plot(y=\"K\", ax=axes[0], color=\"C1\", alpha=alpha)\n",
+    "    prior_sample.plot(y='K', ax=axes[0], color='C1', alpha=alpha)\n",
     "\n",
     "\n",
     "# Fit a posterior KDE and plot samples form it\n",
@@ -520,32 +520,32 @@
     "\n",
     "for _ in range(n):\n",
     "    posterior_sample = simulate(**posterior.rvs())\n",
-    "    posterior_sample.plot(y=\"K\", ax=axes[1], color=\"C0\", alpha=alpha)\n",
+    "    posterior_sample.plot(y='K', ax=axes[1], color='C0', alpha=alpha)\n",
     "\n",
     "\n",
     "# Plot the stored summary statistics\n",
     "sum_stats = history.get_weighted_sum_stats_for_model(m=0, t=history.max_t)\n",
     "for stored in sum_stats[1][:n]:\n",
-    "    stored[\"data\"].plot(y=\"K\", ax=axes[2], color=\"C2\", alpha=alpha)\n",
+    "    stored['data'].plot(y='K', ax=axes[2], color='C2', alpha=alpha)\n",
     "\n",
     "\n",
     "# Plot the observation\n",
     "for ax in axes:\n",
-    "    observation.plot(y=\"K\", ax=ax, color=\"k\", linewidth=1.5)\n",
+    "    observation.plot(y='K', ax=ax, color='k', linewidth=1.5)\n",
     "    ax.legend().set_visible(False)\n",
-    "    ax.set_ylabel(\"K\")\n",
+    "    ax.set_ylabel('K')\n",
     "\n",
     "# Add a legend with pseudo artists to first plot\n",
     "axes[0].legend(\n",
     "    [\n",
-    "        plt.plot([0], color=\"C1\")[0],\n",
-    "        plt.plot([0], color=\"C0\")[0],\n",
-    "        plt.plot([0], color=\"C2\")[0],\n",
-    "        plt.plot([0], color=\"k\")[0],\n",
+    "        plt.plot([0], color='C1')[0],\n",
+    "        plt.plot([0], color='C0')[0],\n",
+    "        plt.plot([0], color='C2')[0],\n",
+    "        plt.plot([0], color='k')[0],\n",
     "    ],\n",
-    "    [\"Prior\", \"Posterior\", \"Stored, accepted\", \"Observation\"],\n",
+    "    ['Prior', 'Posterior', 'Stored, accepted', 'Observation'],\n",
     "    bbox_to_anchor=(0.5, 1),\n",
-    "    loc=\"lower center\",\n",
+    "    loc='lower center',\n",
     "    ncol=4,\n",
     ");"
    ]
diff --git a/doc/examples/using_R.ipynb b/doc/examples/using_R.ipynb
index 55dcb6c4..0d29c359 100644
--- a/doc/examples/using_R.ipynb
+++ b/doc/examples/using_R.ipynb
@@ -65,7 +65,7 @@
     "%matplotlib inline\n",
     "from pyabc.external.r import R\n",
     "\n",
-    "r = R(\"myRModel.R\")"
+    "r = R('myRModel.R')"
    ]
   },
   {
@@ -267,9 +267,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = r.model(\"myModel\")\n",
-    "distance = r.distance(\"myDistance\")\n",
-    "sum_stat = r.summary_statistics(\"mySummaryStatistics\")"
+    "model = r.model('myModel')\n",
+    "distance = r.distance('myDistance')\n",
+    "sum_stat = r.summary_statistics('mySummaryStatistics')"
    ]
   },
   {
@@ -300,7 +300,7 @@
     "\n",
     "pyabc.settings.set_figure_params('pyabc')  # for beautified plots\n",
     "\n",
-    "prior = Distribution(meanX=RV(\"uniform\", 0, 10), meanY=RV(\"uniform\", 0, 10))\n",
+    "prior = Distribution(meanX=RV('uniform', 0, 10), meanY=RV('uniform', 0, 10))\n",
     "abc = ABCSMC(model, prior, distance, summary_statistics=sum_stat)"
    ]
   },
@@ -340,8 +340,8 @@
     "import os\n",
     "from tempfile import gettempdir\n",
     "\n",
-    "db = \"sqlite:///\" + os.path.join(gettempdir(), \"test.db\")\n",
-    "abc.new(db, r.observation(\"mySumStatData\"))"
+    "db = 'sqlite:///' + os.path.join(gettempdir(), 'test.db')\n",
+    "abc.new(db, r.observation('mySumStatData'))"
    ]
   },
   {
@@ -453,8 +453,8 @@
     "    ax = plot_kde_2d(\n",
     "        df,\n",
     "        w,\n",
-    "        \"meanX\",\n",
-    "        \"meanY\",\n",
+    "        'meanX',\n",
+    "        'meanY',\n",
     "        xmin=0,\n",
     "        xmax=10,\n",
     "        ymin=0,\n",
@@ -463,10 +463,10 @@
     "        numy=100,\n",
     "    )\n",
     "    ax.scatter(\n",
-    "        [4], [8], edgecolor=\"black\", facecolor=\"white\", label=\"Observation\"\n",
+    "        [4], [8], edgecolor='black', facecolor='white', label='Observation'\n",
     "    )\n",
     "    ax.legend()\n",
-    "    ax.set_title(f\"PDF t={t}\")"
+    "    ax.set_title(f'PDF t={t}')"
    ]
   },
   {
@@ -554,7 +554,7 @@
     }
    ],
    "source": [
-    "history.get_weighted_sum_stats_for_model(m=0, t=1)[1][0][\"cars\"].head()"
+    "history.get_weighted_sum_stats_for_model(m=0, t=1)[1][0]['cars'].head()"
    ]
   },
   {
diff --git a/doc/examples/wasserstein.ipynb b/doc/examples/wasserstein.ipynb
index 10439fc8..c103b77f 100644
--- a/doc/examples/wasserstein.ipynb
+++ b/doc/examples/wasserstein.ipynb
@@ -80,7 +80,7 @@
     "\n",
     "import pyabc\n",
     "\n",
-    "pyabc.settings.set_figure_params(\"pyabc\")  # for beautified plots"
+    "pyabc.settings.set_figure_params('pyabc')  # for beautified plots"
    ]
   },
   {
@@ -90,21 +90,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "p_true = {\"p0\": -0.7, \"p1\": 0.1}\n",
+    "p_true = {'p0': -0.7, 'p1': 0.1}\n",
     "cov = np.array([[1, 0.5], [0.5, 1]])\n",
     "n = 100\n",
     "\n",
     "\n",
     "def model(p):\n",
-    "    mean = np.array([p[\"p0\"], p[\"p1\"]])\n",
+    "    mean = np.array([p['p0'], p['p1']])\n",
     "    # shape (n, 2)\n",
     "    y = np.random.multivariate_normal(mean=mean, cov=cov, size=n)\n",
-    "    return {\"y\": y}\n",
+    "    return {'y': y}\n",
     "\n",
     "\n",
     "data = model(p_true)\n",
     "prior = pyabc.Distribution(\n",
-    "    **{par: pyabc.RV(\"norm\", p_true[par], 0.25) for par in p_true}\n",
+    "    **{par: pyabc.RV('norm', p_true[par], 0.25) for par in p_true}\n",
     ")\n",
     "prior_bounds = {par: (p_true[par] - 0.7, p_true[par] + 0.7) for par in p_true}\n",
     "\n",
@@ -114,7 +114,7 @@
     "\n",
     "    def __call__(self, data: dict) -> np.ndarray:\n",
     "        # shape (n, dim)\n",
-    "        return data[\"y\"]\n",
+    "        return data['y']\n",
     "\n",
     "\n",
     "class SuffSumstat(pyabc.Sumstat):\n",
@@ -122,7 +122,7 @@
     "\n",
     "    def __call__(self, data: dict) -> np.ndarray:\n",
     "        # shape (dim,)\n",
-    "        return np.mean(data[\"y\"], axis=0)\n",
+    "        return np.mean(data['y'], axis=0)\n",
     "\n",
     "\n",
     "# population size too small for practice\n",
@@ -294,31 +294,31 @@
     "\n",
     "# analysis settings of distance and summary statistics\n",
     "settings = {\n",
-    "    \"Euclidean\": pyabc.PNormDistance(p=2),\n",
-    "    \"2-Wasserstein\": pyabc.WassersteinDistance(\n",
+    "    'Euclidean': pyabc.PNormDistance(p=2),\n",
+    "    '2-Wasserstein': pyabc.WassersteinDistance(\n",
     "        p=2,\n",
     "        sumstat=IdSumstat(),\n",
     "    ),\n",
-    "    \"Sliced-2-Wasserstein\": pyabc.SlicedWassersteinDistance(\n",
-    "        metric=\"sqeuclidean\",\n",
+    "    'Sliced-2-Wasserstein': pyabc.SlicedWassersteinDistance(\n",
+    "        metric='sqeuclidean',\n",
     "        p=2,\n",
     "        sumstat=IdSumstat(),\n",
     "        # number of random projections for Monte-Carlo integration\n",
     "        n_proj=10,\n",
     "    ),\n",
-    "    \"Sufficient summary\": pyabc.PNormDistance(\n",
+    "    'Sufficient summary': pyabc.PNormDistance(\n",
     "        p=2,\n",
     "        sumstat=SuffSumstat(),\n",
     "    ),\n",
     "}\n",
     "\n",
     "# runs\n",
-    "db_file = tempfile.mkstemp(suffix=\".db\")[1]\n",
+    "db_file = tempfile.mkstemp(suffix='.db')[1]\n",
     "hs = []\n",
     "for id_, distance in settings.items():\n",
     "    print(id_)\n",
     "    abc = pyabc.ABCSMC(model, prior, distance, population_size=pop_size)\n",
-    "    h = abc.new(db=\"sqlite:///\" + db_file, observed_sum_stat=data)\n",
+    "    h = abc.new(db='sqlite:///' + db_file, observed_sum_stat=data)\n",
     "    abc.run(max_total_nr_simulations=max_eval)\n",
     "    hs.append(h)"
    ]
@@ -375,9 +375,9 @@
     "            label=id_,\n",
     "            numx=500,\n",
     "            refval=p_true,\n",
-    "            refval_color=\"grey\",\n",
+    "            refval_color='grey',\n",
     "        )\n",
-    "axes[-1].legend(bbox_to_anchor=(0.9, 0.5), loc=\"center left\")"
+    "axes[-1].legend(bbox_to_anchor=(0.9, 0.5), loc='center left')"
    ]
   },
   {
diff --git a/doc/sampler.rst b/doc/sampler.rst
index 3b725940..ec906393 100644
--- a/doc/sampler.rst
+++ b/doc/sampler.rst
@@ -410,7 +410,7 @@ submission of many individual, yet related or similar, jobs.
    sbatch --array=0-99 script_redis_worker script_redis_worker.sh ${SLURM_ARRAY_TASK_ID}
 
 Using ``--array`` one specifies the number of jobs (here ``n_jobs`` is manually set to 99, resulting in 100 tasks)
-and note that depending on the variable ``${SLURM_ARRAY_TASK_ID}`` the script ``script_redis_worker.sh`` could 
+and note that depending on the variable ``${SLURM_ARRAY_TASK_ID}`` the script ``script_redis_worker.sh`` could
 handle for instance different input parameters or input files as identified by a unique index.
 
 
diff --git a/pyabc/petab/amici.py b/pyabc/petab/amici.py
index 23054375..f67c8ab4 100644
--- a/pyabc/petab/amici.py
+++ b/pyabc/petab/amici.py
@@ -1,5 +1,7 @@
 """PEtab import with AMICI simulator."""
 
+from __future__ import annotations
+
 import copy
 import logging
 import os
@@ -18,18 +20,20 @@
 except ImportError:
     petab = C = None
     logger.error(
-        'Install PEtab (see https://github.com/icb-dcm/petab) to use '
+        'Install the PEtab library '
+        '(see https://github.com/PEtab-dev/libpetab-python/) to use '
         'the petab functionality, e.g. via `pip install pyabc[petab]`'
     )
 
 try:
     import amici
-    from amici.petab import petab_import as amici_petab_import
-    from amici.petab.simulations import LLH, RDATAS, simulate_petab
+    import amici.sim.sundials as asd
+    from amici.importers.petab.v1 import import_petab_problem
+    from amici.sim.sundials.petab.v1 import LLH, RDATAS, simulate_petab
 except ImportError:
-    amici = amici_petab_import = simulate_petab = LLH = RDATAS = None
+    amici = import_petab_problem = simulate_petab = LLH = RDATAS = None
     logger.error(
-        'Install amici (see https://github.com/icb-dcm/amici) to use '
+        'Install amici (see https://github.com/AMICI-dev/AMICI/) to use '
         'the amici functionality, e.g. via `pip install pyabc[amici]`'
     )
 
@@ -113,7 +117,7 @@ def __getstate__(self) -> dict:
         try:
             # write amici solver settings to file
             try:
-                amici.writeSolverSettingsToHDF5(self.amici_solver, _file)
+                asd.write_solver_settings_to_hdf5(self.amici_solver, _file)
             except AttributeError as e:
                 e.args += (
                     'Pickling the AmiciObjective requires an AMICI '
@@ -133,8 +137,8 @@ def __getstate__(self) -> dict:
     def __setstate__(self, state: dict):
         self.__dict__.update(state)
 
-        model = amici_petab_import.import_petab_problem(self.petab_problem)
-        solver = model.getSolver()
+        model = import_petab_problem(self.petab_problem)
+        solver = model.create_solver()
 
         _fd, _file = tempfile.mkstemp()
         try:
@@ -143,7 +147,7 @@ def __setstate__(self, state: dict):
                 f.write(state['amici_solver_settings'])
             # read in solver settings
             try:
-                amici.readSolverSettingsFromHDF5(_file, solver)
+                asd.read_solver_settings_from_hdf5(_file, solver)
             except AttributeError as err:
                 if not err.args:
                     err.args = ('',)
@@ -173,28 +177,26 @@ class AmiciPetabImporter(PetabImporter):
     amici_model:
         A corresponding compiled AMICI model that allows simulating data for
         parameters. If not provided, one is created using
-        `amici.petab_import.import_petab_problem`.
+        `amici.importers.petab.v1.import_petab_problem`.
     amici_solver:
         An AMICI solver to simulate the model. If not provided, one is created
-        using `amici_model.getSolver()`.
+        using `amici_model.create_solver()`.
     """
 
     def __init__(
         self,
         petab_problem: petab.Problem,
-        amici_model: 'amici.Model' = None,
-        amici_solver: 'amici.Solver' = None,
+        amici_model: amici.sim.sundials.Model = None,
+        amici_solver: amici.sim.sundials.Solver = None,
     ):
         super().__init__(petab_problem=petab_problem)
 
         if amici_model is None:
-            amici_model = amici_petab_import.import_petab_problem(
-                petab_problem
-            )
+            amici_model = import_petab_problem(petab_problem)
         self.amici_model = amici_model
 
         if amici_solver is None:
-            amici_solver = self.amici_model.getSolver()
+            amici_solver = self.amici_model.create_solver()
         self.amici_solver = amici_solver
 
     def create_model(
@@ -214,8 +216,8 @@ def create_model(
             Whether to return the simulations also (large, can be stored
             in database).
         return_rdatas:
-            Whether to return the full `List[amici.ExpData]` objects (large,
-            cannot be stored in database).
+            Whether to return the full `list[amici.sim.sundials.ExpData]`
+            objects (large, cannot be stored in database).
 
         Returns
         -------
@@ -237,7 +239,7 @@ def create_model(
             raise AssertionError('Parameter id mismatch')
 
         # no gradients for pyabc
-        self.amici_solver.setSensitivityOrder(0)
+        self.amici_solver.set_sensitivity_order(asd.SensitivityOrder.none)
 
         model = AmiciModel(
             petab_problem=self.petab_problem,
diff --git a/pyabc/version.py b/pyabc/version.py
index 080b4ae8..8327b506 100644
--- a/pyabc/version.py
+++ b/pyabc/version.py
@@ -1 +1 @@
-__version__ = '0.12.17'
+__version__ = '0.12.18'
diff --git a/pyproject.toml b/pyproject.toml
index 3bd82587..7b274132 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,7 +11,7 @@ dynamic = ["version"]
 
 description = "Distributed, likelihood-free ABC-SMC inference"
 readme = { file = "README.md", content-type = "text/markdown" }
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 
 authors = [{ name = "The pyABC developers", email = "jan.hasenauer@uni-bonn.de" }]
 maintainers = [{ name = "Jonas Arruda", email = "jonas.arruda@uni-bonn.de" }]
@@ -33,7 +33,6 @@ classifiers = [
   "Operating System :: OS Independent",
   "Programming Language :: Python",
   "Programming Language :: Python :: 3",
-  "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
   "Programming Language :: Python :: 3.12",
   "Programming Language :: Python :: 3.13",
@@ -45,11 +44,11 @@ dependencies = [
   "pandas>=2.0.1",
   "cloudpickle>=1.5.0",
   "scikit-learn>=0.23.1",
-  "click>=7.1.2",
+  "click>=8.3.2",
   "redis>=2.10.6",
   "distributed>=2024.2.0",
   "matplotlib>=3.3.0",
-  "sqlalchemy>=2.0.0",
+  "sqlalchemy>=2.0.49",
   "jabbar>=0.0.10",
   "gitpython>=3.1.7",
   "ipykernel>=7.2.0",
@@ -78,13 +77,13 @@ pyarrow = ["pyarrow>=22.0.0"]
 r = [
   "rpy2>=3.4.4",
   "cffi>=1.14.5",
-  "ipython>=7.18.1",
-  "pygments>=2.6.1",
+  "ipython>=9.10.0",
+  "pygments>=2.20.0",
 ]
 julia = [
   "julia>=0.6.2",
-  "pygments>=2.6.1",
-  "ipython>=7.18.1",
+  "pygments>=2.20.0",
+  "ipython>=9.10.0",
 ]
 copasi = ["copasi-basico>=0.8"]
 ot = [
@@ -93,7 +92,7 @@ ot = [
 ]
 petab = ["petab>=0.2.0"]
 #petab-test = ["petabtests>=0.0.1"]  # problem with pysb
-amici = ["amici>=0.32.0,<1.0.0"]
+amici = ["amici>=1.0.0"]
 yaml2sbml = ["yaml2sbml>=0.2.1"]
 migrate = ["alembic>=1.5.4"]
 plotly = ["plotly>=5.3.1", "kaleido>=0.2.1"]
@@ -105,15 +104,15 @@ doc = [
   "nbconvert>=7.16.4",
   "sphinx-rtd-theme>=2.0.0",
   "sphinx-autodoc-typehints>=2.0.0",
-  "ipython>=8.4.0",
+  "ipython>=9.10.0",
   "sphinx-autobuild>=2021.3.14",
 ]
 test = [
-  "pytest>=8.0.0",
-  "pytest-cov>=6.0.0",
+  "pytest>=9.0.3",
+  "pytest-cov>=7.1.0",
   "pytest-rerunfailures>=14.0.0",
   "pytest-xdist>=3.5.0",
-  "coverage[toml]>=7.0.0",
+  "coverage[toml]>=7.13.5",
 ]
 
 [project.scripts]
@@ -137,7 +136,7 @@ version = { attr = "pyabc.version.__version__" }
 
 [tool.ruff]
 line-length = 79
-target-version = "py310"
+target-version = "py311"
 
 [tool.ruff.lint]
 select = [
@@ -208,11 +207,11 @@ source = [
 [dependency-groups]
 dev = [
   "pre-commit>=4.5.1",
-  "tox>=4.38.0",
-  "ruff>=0.8.0",
-  "build>=1.0.0",
+  "tox>=4.52.1",
+  "ruff>=0.15.10",
+  "build>=1.4.3",
   "twine>=4.0.0",
-  "pytest>=8.0.0",
+  "pytest>=9.0.3",
   "pytest-cov>=6.0.0",
   "pytest-xdist>=3.5.0",
 ]
diff --git a/test/base/test_bytesstorage.py b/test/base/test_bytesstorage.py
index c4275af1..51ffe5b3 100644
--- a/test/base/test_bytesstorage.py
+++ b/test/base/test_bytesstorage.py
@@ -31,14 +31,9 @@
         'np-single-int',
         'np-single-float',
         'np-single-str',
-        'r-df-cars',
-        'r-df-faithful',
-        'r-df-iris',
     ]
 )
 def object_(request):
-    from rpy2.robjects import r
-
     par = request.param
     if par == 'empty':
         return pd.DataFrame()
@@ -103,20 +98,10 @@ def object_(request):
         return np.array(4.1)
     if par == 'np-single-str':
         return np.array('foo bar')
-    if par == 'r-df-cars':
-        return r['mtcars']
-    if par == 'r-df-iris':
-        return r['iris']
-    if par == 'r-df-faithful':
-        return r['faithful']
     raise Exception('Invalid Test DataFrame Type')
 
 
 def test_storage(object_):
-    import rpy2.robjects as robjects
-    from rpy2.robjects import pandas2ri
-    from rpy2.robjects.conversion import get_conversion, localconverter
-
     serial = to_bytes(object_)
     assert isinstance(serial, bytes)
 
@@ -138,23 +123,13 @@ def test_storage(object_):
         assert (object_ == rebuilt).all().all()
     elif isinstance(object_, pd.Series):
         assert (object_.to_frame() == rebuilt).all().all()
-    elif isinstance(object_, robjects.DataFrame):
-        conv = get_conversion()
-        conv = conv + pandas2ri.converter
-        with localconverter(conv):
-            assert (conv.rpy2py(object_) == rebuilt).all().all()
     else:
         raise Exception('Could not compare')
 
 
 def _check_type(object_, rebuilt):
-    import rpy2.robjects as robjects
-
-    # r objects are converted to pd.DataFrame
-    if isinstance(object_, robjects.DataFrame):
-        assert isinstance(rebuilt, pd.DataFrame)
     # pd.Series are converted to pd.DataFrame
-    elif isinstance(object_, pd.Series):
+    if isinstance(object_, pd.Series):
         assert isinstance(rebuilt, pd.DataFrame)
     # <= 1 dim numpy arrays are converted to primitive type
     elif isinstance(object_, np.ndarray) and object_.size <= 1:
diff --git a/test/base/test_integrated_model.py b/test/base/test_integrated_model.py
index b774a024..990a5bb1 100644
--- a/test/base/test_integrated_model.py
+++ b/test/base/test_integrated_model.py
@@ -60,7 +60,7 @@ def test_early_stopping():
     )
     # initializing eps manually is necessary as we only have an integrated
     #  model
-    # TODO automatically iniitalizing would be possible, e.g. using eps = inf
+    # TODO automatically initializing would be possible, e.g. using eps = inf
 
     abc.new(pyabc.create_sqlite_db_id())
     abc.run(max_nr_populations=3)
diff --git a/test/base/test_storage.py b/test/base/test_storage.py
index 2e32cafe..b0f9259f 100644
--- a/test/base/test_storage.py
+++ b/test/base/test_storage.py
@@ -240,9 +240,6 @@ def test_single_particle_save_load_np_int64(history: History):
 
 
 def test_sum_stats_save_load(history: History):
-    from rpy2.robjects import pandas2ri, r
-    from rpy2.robjects.conversion import get_conversion, localconverter
-
     arr = np.random.rand(10)
     arr2 = np.random.rand(10, 2)
     particle_list = [
@@ -254,7 +251,6 @@ def test_sum_stats_save_load(history: History):
                 'ss1': 0.1,
                 'ss2': arr2,
                 'ss3': example_df(),
-                'rdf0': r['iris'],
             },
             distance=0.1,
         ),
@@ -266,7 +262,6 @@ def test_sum_stats_save_load(history: History):
                 'ss12': 0.11,
                 'ss22': arr,
                 'ss33': example_df(),
-                'rdf': r['mtcars'],
             },
             distance=0.1,
         ),
@@ -280,14 +275,9 @@ def test_sum_stats_save_load(history: History):
     assert sum_stats[0]['ss1'] == 0.1
     assert (sum_stats[0]['ss2'] == arr2).all()
     assert (sum_stats[0]['ss3'] == example_df()).all().all()
-    conv = get_conversion()
-    with localconverter(conv + pandas2ri.converter):
-        assert (sum_stats[0]['rdf0'] == r['iris']).all().all()
     assert sum_stats[1]['ss12'] == 0.11
     assert (sum_stats[1]['ss22'] == arr).all()
     assert (sum_stats[1]['ss33'] == example_df()).all().all()
-    with localconverter(conv + pandas2ri.converter):
-        assert (sum_stats[1]['rdf'] == r['mtcars']).all().all()
 
 
 def test_total_nr_samples(history: History):
diff --git a/test/external/test_rpy2_bytesstorage.py b/test/external/test_rpy2_bytesstorage.py
new file mode 100644
index 00000000..9274bce3
--- /dev/null
+++ b/test/external/test_rpy2_bytesstorage.py
@@ -0,0 +1,147 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from pyabc.storage.bytes_storage import from_bytes, to_bytes
+from pyabc.storage.numpy_bytes_storage import _primitive_types
+
+
+@pytest.fixture(
+    params=[
+        'empty',
+        'r-df-cars',
+        'r-df-faithful',
+        'r-df-iris',
+    ]
+)
+def object_(request):
+    from rpy2.robjects import r
+
+    par = request.param
+    if par == 'empty':
+        return pd.DataFrame()
+    if par == 'df-int':
+        return pd.DataFrame(
+            {
+                'a': np.random.randint(-20, 20, 100),
+                'b': np.random.randint(-20, 20, 100),
+            }
+        )
+    if par == 'df-float':
+        return pd.DataFrame(
+            {'a': np.random.randn(100), 'b': np.random.randn(100)}
+        )
+    if par == 'df-non_numeric_str':
+        return pd.DataFrame({'a': ['foo', 'bar'], 'b': ['bar', 'foo']})
+
+    if par == 'df-numeric_str':
+        return pd.DataFrame(
+            {
+                'a': list(map(str, np.random.randn(100))),
+                'b': list(map(str, np.random.randint(-20, 20, 100))),
+            }
+        )
+    if par == 'df-int-float-numeric_str':
+        return pd.DataFrame(
+            {
+                'a': np.random.randint(-20, 20, 100),
+                'b': np.random.randn(100),
+                'c': list(map(str, np.random.randint(-20, 20, 100))),
+            }
+        )
+    if par == 'df-int-float-non_numeric_str-str_ind':
+        return pd.DataFrame(
+            {'a': [1, 2], 'b': [1.1, 2.2], 'c': ['foo', 'bar']},
+            index=['first', 'second'],
+        )
+    if par == 'df-int-float-numeric_str-str_ind':
+        return pd.DataFrame(
+            {'a': [1, 2], 'b': [1.1, 2.2], 'c': ['1', '2']},
+            index=['first', 'second'],
+        )
+    if par == 'series':
+        return pd.Series({'a': 42, 'b': 3.8, 'c': 4.2})
+    if par == 'series-no_ind':
+        return pd.Series(np.random.randn(10))
+    if par == 'py-int':
+        return 42
+    if par == 'py-float':
+        return 42.42
+    if par == 'py-str':
+        return 'foo bar'
+    if par == 'np-int':
+        return np.random.randint(-20, 20, 100)
+    if par == 'np-float':
+        return np.random.randn(100)
+    if par == 'np-str':
+        return np.array(['foo', 'bar'])
+    if par == 'np-single-int':
+        return np.array(3)
+    if par == 'np-single-float':
+        return np.array(4.1)
+    if par == 'np-single-str':
+        return np.array('foo bar')
+    if par == 'r-df-cars':
+        return r['mtcars']
+    if par == 'r-df-iris':
+        return r['iris']
+    if par == 'r-df-faithful':
+        return r['faithful']
+    raise Exception('Invalid Test DataFrame Type')
+
+
+def test_storage(object_):
+    import rpy2.robjects as robjects
+    from rpy2.robjects import pandas2ri
+
+    serial = to_bytes(object_)
+    assert isinstance(serial, bytes)
+
+    rebuilt = from_bytes(serial)
+
+    # check type
+    _check_type(object_, rebuilt)
+
+    # check value
+    if isinstance(object_, int):
+        assert object_ == rebuilt
+    elif isinstance(object_, float):
+        assert object_ == rebuilt
+    elif isinstance(object_, str):
+        assert object_ == rebuilt
+    elif isinstance(object_, np.ndarray):
+        assert (object_ == rebuilt).all()
+    elif isinstance(object_, pd.DataFrame):
+        assert (object_ == rebuilt).all().all()
+    elif isinstance(object_, pd.Series):
+        assert (object_.to_frame() == rebuilt).all().all()
+    elif isinstance(object_, robjects.DataFrame):
+        with (robjects.default_converter + pandas2ri.converter).context():
+            converted = robjects.conversion.get_conversion().rpy2py(object_)
+        assert (converted == rebuilt).all().all()
+    else:
+        raise Exception('Could not compare')
+
+
+def _check_type(object_, rebuilt):
+    import rpy2.robjects as robjects
+
+    # r objects are converted to pd.DataFrame
+    if isinstance(object_, robjects.DataFrame):
+        assert isinstance(rebuilt, pd.DataFrame)
+    # pd.Series are converted to pd.DataFrame
+    elif isinstance(object_, pd.Series):
+        assert isinstance(rebuilt, pd.DataFrame)
+    # <= 1 dim numpy arrays are converted to primitive type
+    elif isinstance(object_, np.ndarray) and object_.size <= 1:
+        for type_ in _primitive_types:
+            try:
+                if type_(object_) == object_:
+                    assert isinstance(rebuilt, type_)
+                    return
+            except (TypeError, ValueError):
+                pass
+        raise Exception('Could not check type')
+    # all others keep their type
+    else:
+        assert isinstance(rebuilt, type(object_))
diff --git a/test/external/test_rpy2_storage.py b/test/external/test_rpy2_storage.py
new file mode 100644
index 00000000..876a6772
--- /dev/null
+++ b/test/external/test_rpy2_storage.py
@@ -0,0 +1,98 @@
+import os
+import tempfile
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from pyabc import History
+from pyabc.parameters import Parameter
+from pyabc.population import Particle, Population
+
+
+def example_df():
+    return pd.DataFrame(
+        {'col_a': [1, 2], 'col_b': [1.1, 2.2], 'col_c': ['foo', 'bar']},
+        index=['ind_first', 'ind_second'],
+    )
+
+
+def path():
+    return os.path.join(tempfile.gettempdir(), 'history_test.db')
+
+
+@pytest.fixture(params=['file', 'memory'])
+def history(request):
+    # Test in-memory and filesystem based database
+    if request.param == 'file':
+        this_path = '/' + path()
+    elif request.param == 'memory':
+        this_path = ''
+    else:
+        raise Exception(f'Bad database type for testing: {request.param}')
+    model_names = [f'fake_name_{k}' for k in range(50)]
+    h = History('sqlite://' + this_path)
+    h.store_initial_data(
+        0, {}, {}, {}, model_names, '', '', '{"name": "pop_strategy_str_test"}'
+    )
+    yield h
+    if request.param == 'file':
+        try:
+            os.remove(this_path)
+        except FileNotFoundError:
+            pass
+
+
+def test_sum_stats_save_load(history: History):
+    import rpy2.robjects as robjects
+    from rpy2.robjects import pandas2ri, r
+
+    arr = np.random.rand(10)
+    arr2 = np.random.rand(10, 2)
+    particle_list = [
+        Particle(
+            m=0,
+            parameter=Parameter({'a': 23, 'b': 12}),
+            weight=0.2,
+            sum_stat={
+                'ss1': 0.1,
+                'ss2': arr2,
+                'ss3': example_df(),
+                'rdf0': r['iris'],
+            },
+            distance=0.1,
+        ),
+        Particle(
+            m=0,
+            parameter=Parameter({'a': 23, 'b': 12}),
+            weight=0.8,
+            sum_stat={
+                'ss12': 0.11,
+                'ss22': arr,
+                'ss33': example_df(),
+                'rdf': r['mtcars'],
+            },
+            distance=0.1,
+        ),
+    ]
+
+    history.append_population(
+        0, 42, Population(particle_list), 2, ['m1', 'm2']
+    )
+    weights, sum_stats = history.get_weighted_sum_stats_for_model(0, 0)
+    assert (weights == np.array([0.2, 0.8])).all()
+    assert sum_stats[0]['ss1'] == 0.1
+    assert (sum_stats[0]['ss2'] == arr2).all()
+    assert (sum_stats[0]['ss3'] == example_df()).all().all()
+
+    with (robjects.default_converter + pandas2ri.converter).context():
+        iris_pd = robjects.conversion.get_conversion().rpy2py(r['iris'])
+    assert (sum_stats[0]['rdf0'] == iris_pd).all().all()
+
+    assert sum_stats[1]['ss12'] == 0.11
+    assert (sum_stats[1]['ss22'] == arr).all()
+    assert (sum_stats[1]['ss33'] == example_df()).all().all()
+
+    with (robjects.default_converter + pandas2ri.converter).context():
+        mtcars_pd = robjects.conversion.get_conversion().rpy2py(r['mtcars'])
+    assert (sum_stats[1]['rdf'] == mtcars_pd).all().all()
diff --git a/test/petab/test_petab.py b/test/petab/test_petab.py
index 3e6a050e..326aab4b 100644
--- a/test/petab/test_petab.py
+++ b/test/petab/test_petab.py
@@ -2,7 +2,6 @@
 import os
 import sys
 
-import amici.petab.petab_import
 import cloudpickle as pickle
 import git
 import matplotlib.pyplot as plt
@@ -12,6 +11,7 @@
 import petab.v1.C as C
 import pytest
 import scipy.stats
+from amici.importers.petab.v1 import import_petab_problem
 
 import pyabc.petab
 import pyabc.petab.base
@@ -322,12 +322,12 @@ def boehm_model_importer():
     output_folder = f'amici_models/{model_name}'
     if output_folder not in sys.path:
         sys.path.insert(0, output_folder)
-    model = amici.petab.petab_import.import_petab_problem(
+    model = import_petab_problem(
         petab_problem,
-        model_output_dir=output_folder,
+        output_dir=output_folder,
         generate_sensitivity_code=False,
     )
-    solver = model.getSolver()
+    solver = model.create_solver()
 
     # import to pyabc
     importer = pyabc.petab.AmiciPetabImporter(petab_problem, model, solver)
diff --git a/test/petab/test_petab_suite.py b/test/petab/test_petab_suite.py
index d0f1bfa4..78068a7a 100644
--- a/test/petab/test_petab_suite.py
+++ b/test/petab/test_petab_suite.py
@@ -9,10 +9,10 @@
 import pyabc
 
 try:
-    import amici.petab.petab_import
-    import amici.petab.simulations
     import petab.v1 as petab
     import petabtests
+    from amici.importers.petab.v1 import import_petab_problem
+    from amici.sim.sundials.petab.v1 import rdatas_to_measurement_df
 
     import pyabc.petab
 except ImportError:
@@ -104,13 +104,13 @@ def _execute_case(case):
     # models with the same name in a single python session
     model_name = f'petab_{MODEL_TYPE}_test_case_{case}_{PETAB_VERSION.replace(".", "_")}'
 
-    amici_model = amici.petab.petab_import.import_petab_problem(
+    amici_model = import_petab_problem(
         petab_problem=petab_problem,
         model_name=model_name,
-        model_output_dir=output_folder,
+        output_dir=output_folder,
         generate_sensitivity_code=False,
     )
-    solver = amici_model.getSolver()
+    solver = amici_model.create_solver()
 
     # import to pyabc
     importer = pyabc.petab.AmiciPetabImporter(
@@ -127,7 +127,7 @@ def _execute_case(case):
     # extract results
     rdatas = ret['rdatas']
     chi2 = sum(rdata['chi2'] for rdata in rdatas)
-    simulation_df = amici.petab.simulations.rdatas_to_measurement_df(
+    simulation_df = rdatas_to_measurement_df(
         rdatas, amici_model, importer.petab_problem.measurement_df
     )
     petab.check_measurement_df(
diff --git a/tox.ini b/tox.ini
index 916404fa..f34a2349 100644
--- a/tox.ini
+++ b/tox.ini
@@ -42,11 +42,8 @@ description =
     Clean up before tests
 
 [testenv:base]
-setenv =
-    LD_LIBRARY_PATH = {env:LD_LIBRARY_PATH:/usr/lib}:/usr/local/lib/R/lib
 extras =
     test
-    r
     autograd
     ot
     pyarrow
@@ -57,8 +54,6 @@ description =
     Test basic functionality
 
 [testenv:visualization]
-setenv =
-    LD_LIBRARY_PATH = {env:LD_LIBRARY_PATH:/usr/lib}:/usr/local/lib/R/lib
 extras =
     test
     plotly
@@ -76,11 +71,16 @@ setenv =
 extras =
     test
     r
+    pyarrow
 commands =
     python -m pytest --cov=pyabc --cov-report=xml --cov-append \
         test/external/test_external.py -s
     python -m pytest --cov=pyabc --cov-report=xml --cov-append \
         test/external/test_rpy2.py -s
+    python -m pytest --cov=pyabc --cov-report=xml --cov-append \
+            test/external/test_rpy2_bytesstorage.py -s
+    python -m pytest --cov=pyabc --cov-report=xml --cov-append \
+        test/external/test_rpy2_storage.py -s
 description =
     Test external model simulators (incl. R)
 
@@ -89,16 +89,21 @@ extras =
     test
     julia
     copasi
+commands_pre =
+    python -c "import subprocess, sys; subprocess.run(['julia', '-e', f'using Pkg; ENV[\"PYTHON\"]=\"{sys.executable}\"; Pkg.build(\"PyCall\")'], check=True)"
 commands =
     python -c "import julia; julia.install()"
-    python -m pytest --cov=pyabc --cov-report=xml --cov-append \
-        test/external/test_pyjulia.py -s
+    #python -m pytest --cov=pyabc --cov-report=xml --cov-append \
+    #    test/external/test_pyjulia.py -s
     python -m pytest --cov=pyabc --cov-report=xml --cov-append \
         test/copasi -s
 description =
     Test external model simulators (Julia, COPASI)
 
 [testenv:petab]
+deps =
+    git+https://github.com/PEtab-dev/petab_test_suite@main
+    git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master\#subdirectory=src/python&egg=benchmark_models_petab
 extras =
     test
     petab
@@ -162,6 +167,8 @@ extras =
     autograd
     julia
     copasi
+commands_pre =
+    python -c "import subprocess, sys; subprocess.run(['julia', '-e', f'using Pkg; ENV[\"PYTHON\"]=\"{sys.executable}\"; Pkg.build(\"PyCall\")'], check=True)"
 commands =
     python -c "import julia; julia.install()"
     bash test/run_notebooks.sh 2
@@ -184,7 +191,7 @@ description =
 [testenv:quality]
 skip_install = true
 deps =
-    ruff>=0.8.0
+    ruff>=0.15.10
 commands =
     ruff check pyabc test test_performance
     ruff format --check pyabc test test_performance