diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 7ef70c1..93bb4d2 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -82,4 +82,6 @@ jobs:
           activate-environment: true
 
       - name: Run tests
-        run: pixi run test
+        run: |
+          pixi run python -m ipykernel install --name flopy4 --user
+          pixi run test
diff --git a/docs/examples/array_example.py b/docs/examples/array_example.py
index afc641f..468f501 100644
--- a/docs/examples/array_example.py
+++ b/docs/examples/array_example.py
@@ -12,11 +12,28 @@
 #     name: python3
 # ---
 
-# This example demonstrates the `MFArray` class.
+# # Array variables
+#
+# This example demonstrates how to work with array input variables.
+#
+# ## Overview
+#
+# FloPy works natively with NumPy arrays. Array input data can be provided
+# as `ndarray` (or anything acting like one). FloPy also provides an array
+# subclass `MFArray` supporting some special behaviors:
+#
+# - more efficient memory usage for constant arrays
+# - convenient layered array manipulation
+# - applying a multiplication factor
+#
+# TODO: rewrite the io stuff (external/internal) once that is moved to a
+# separate layer from MFArray
 
 # +
 from pathlib import Path
+from tempfile import TemporaryDirectory
 
+import flopy
 import git
 import matplotlib.pyplot as plt
 import numpy as np
@@ -271,3 +288,218 @@
 mlmfa.sum()
 
 mlmfa.min(), mlmfa.mean(), mlmfa.max()
+
+
+# ## Grid-shaped array data
+#
+# Most MODFLOW array data are two (row, column) or three (layer,
+# row, column) dimensional and represent data on the model grid.
+# grid. Other MODFLOW array data contain data by stress period.
+# The following list summarizes the types of MODFLOW array data.
+
+# * Time-invariant multi-dimensional array data.  This includes:
+#   1. One and two dimensional arrays that do not have a layer dimension.
+#      Examples include `top`, `delc`, and `delr`.
+#   2. Three dimensional arrays that can contain a layer dimension.
+#      Examples include `botm`, `idomain`, and `k`.
+# * Transient arrays that can change with time and therefore contain arrays of
+#    data for one or more stress periods.  Examples include `irch` and
+#    `recharge` in the `RCHA` package.
+#
+# In the example below a three dimensional ndarray is constructed for the
+# `DIS` package's `botm` array.  First, the a simulation and groundwater-flow
+# model are set up.
+
+# set up where simulation workspace will be stored
+temp_dir = TemporaryDirectory()
+workspace = temp_dir.name
+name = "grid_array_example"
+
+# create the FloPy simulation and tdis objects
+sim = flopy.mf6.MFSimulation(
+    sim_name=name, exe_name="mf6", version="mf6", sim_ws=workspace
+)
+tdis = flopy.mf6.modflow.mftdis.ModflowTdis(
+    sim,
+    pname="tdis",
+    time_units="DAYS",
+    nper=2,
+    perioddata=[(1.0, 1, 1.0), (1.0, 1, 1.0)],
+)
+# create the Flopy groundwater flow (gwf) model object
+model_nam_file = f"{name}.nam"
+gwf = flopy.mf6.ModflowGwf(sim, modelname=name, model_nam_file=model_nam_file)
+# create the flopy iterative model solver (ims) package object
+ims = flopy.mf6.modflow.mfims.ModflowIms(sim, pname="ims", complexity="SIMPLE")
+
+# Then a three-dimensional ndarray of floating point values is created using
+# numpy's `linspace` method.
+
+bot = np.linspace(-50.0 / 3.0, -3.0, 3)
+delrow = delcol = 4.0
+
+# The `DIS` package is then created passing the three-dimensional array to the
+# `botm` parameter.  The `botm` array defines the model's cell bottom
+# elevations.
+
+dis = flopy.mf6.modflow.mfgwfdis.ModflowGwfdis(
+    gwf,
+    pname="dis",
+    nogrb=True,
+    nlay=3,
+    nrow=10,
+    ncol=10,
+    delr=delrow,
+    delc=delcol,
+    top=0.0,
+    botm=bot,
+)
+
+# ## Adding MODFLOW Grid Array Data
+# MODFLOW grid array data, like the data found in the `NPF` package's
+# `GridData` block, can be specified as:
+#
+# 1. A constant value
+# 2. A n-dimensional list
+# 3. A numpy ndarray
+#
+# Additionally, layered grid data (generally arrays with a layer dimension) can
+# be specified by layer.
+#
+# In the example below `icelltype` is specified as constants by layer, `k` is
+# specified as a numpy ndarray, `k22` is specified as an array by layer, and
+# `k33` is specified as a constant.
+
+# First `k` is set up as a 3 layer, by 10 row, by 10 column array with all
+# values set to 10.0 using numpy's full method.
+
+k = np.full((3, 10, 10), 10.0)
+
+# Next `k22` is set up as a three dimensional list of nested lists. This
+# option can be useful for those that are familiar with python lists but are
+# not familiar with the numpy library.
+
+k22_row = []
+for row in range(0, 10):
+    k22_row.append(8.0)
+k22_layer = []
+for col in range(0, 10):
+    k22_layer.append(k22_row)
+k22 = [k22_layer, k22_layer, k22_layer]
+
+# `K33` is set up as a single constant value.  Whenever an array has all the
+# same values the easiest and most efficient way to set it up is as a constant
+# value.  Constant values also take less space to store.
+
+k33 = 1.0
+
+# The `k`, `k22`, and `k33` values defined above are then passed in on
+# construction of the npf package.
+
+npf = flopy.mf6.ModflowGwfnpf(
+    gwf,
+    pname="npf",
+    save_flows=True,
+    icelltype=[1, 1, 1],
+    k=k,
+    k22=k22,
+    k33=k33,
+    xt3doptions="xt3d rhs",
+    rewet_record="REWET WETFCT 1.0 IWETIT 1 IHDWET 0",
+)
+
+# ### Layered Data
+#
+# When we look at what will be written to the npf input file, we
+# see that the entire `npf.k22` array is written as one long array with the
+# number of values equal to `nlay` * `nrow` * `ncol`.  And this whole-array
+# specification may be of use in some cases.  Often times, however, it is
+# easier to work with each layer separately.  An `MFArray` object, such as
+# `npf.k22` can be converted to a layered array as follows.
+
+npf.k22.make_layered()
+
+# By changing `npf.k22` to layered, we are then able to manage each layer
+# separately.  Before doing so, however, we need to pass in data that can be
+# separated into three layers.  An array of the correct size is one option.
+
+shp = npf.k22.array.shape
+a = np.arange(shp[0] * shp[1] * shp[2]).reshape(shp)
+npf.k22 = a
+
+# Now that `npf.k22` has been set to be layered, if we print information about
+# it, we see that each layer is stored separately, however, `npf.k22.array`
+# will still return a full three-dimensional array.
+
+type(npf.k22)
+npf.k22
+
+# We also see that each layer is printed separately to the npf
+# Package input file, and that the LAYERED keyword is activated:
+
+npf.k22
+
+# Working with a layered array provides lots of flexibility.  For example,
+# constants can be set for some layers, but arrays for others:
+
+npf.k22.set_data([1, a[2], 200])
+npf.k22
+
+# The array can be interacted with as usual for NumPy arrays:
+npf.k22 = np.stack(
+    [
+        100 * np.ones((10, 10)),
+        50 * np.ones((10, 10)),
+        30 * np.ones((10, 10)),
+    ]
+)
+npf.k22
+
+# ## Adding MODFLOW Stress Period Array Data
+# Transient array data spanning multiple stress periods must be specified as a
+# dictionary of arrays, where the dictionary key is the stress period,
+# expressed as a zero-based integer, and the dictionary value is the grid
+# data for that stress period.
+
+# In the following example a `RCHA` package is created.  First a dictionary
+# is created that contains recharge for the model's two stress periods.
+# Recharge is specified as a constant value in this example, though it could
+# also be specified as a 3-dimensional ndarray or list of lists.
+
+rch_sp1 = 0.01
+rch_sp2 = 0.03
+rch_spd = {0: rch_sp1, 1: rch_sp2}
+
+# The `RCHA` package is created and the dictionary constructed above is passed
+# in as the `recharge` parameter.
+
+rch = flopy.mf6.ModflowGwfrcha(
+    gwf, readasarrays=True, pname="rch", print_input=True, recharge=rch_spd
+)
+
+# Below the `NPF` `k` array is retrieved using the various methods highlighted
+# above.
+
+# First, view the `k` array.
+
+npf.k
+
+# `repr` gives a string representation of the data.
+
+repr(npf.k)
+
+# `str` gives a similar string representation of the data.
+
+str(npf.k)
+
+# Next, view the 4-dimensional array.
+
+rch.recharge
+
+# `repr` gives a string representation of the data.
+
+repr(rch.recharge)
+
+# str gives a similar representation of the data.
+
+str(rch.recharge)
diff --git a/docs/examples/attrs_demo.py b/docs/examples/attrs_demo.py
new file mode 100644
index 0000000..2ac0a52
--- /dev/null
+++ b/docs/examples/attrs_demo.py
@@ -0,0 +1,246 @@
+# # Attrs demo
+
+# This example demonstrates a tentative `attrs`-based object model.
+
+from pathlib import Path
+from typing import List, Literal, Optional, Union
+
+# ## GWF IC
+import numpy as np
+from attr import asdict, define, field, fields_dict
+from cattr import Converter
+from numpy.typing import NDArray
+
+# We can define block classes where variable descriptions become
+# the variable's docstring. Ideally we can come up with a Python
+# input specification that is equivalent to (and convertible to)
+# the original MF6 input specification, while knowing as little
+# as possible about the MF6 input format; but anything we can't
+# get rid of can go in field `metadata`.
+
+
+@define
+class Options:
+    export_array_ascii: bool = field(
+        default=False,
+        metadata={"longname": "export array variables to netcdf output files"},
+    )
+    """
+    keyword that specifies input griddata arrays should be
+    written to layered ascii output files.
+    """
+
+    export_array_netcdf: bool = field(
+        default=False, metadata={"longname": "starting head"}
+    )
+    """
+    keyword that specifies input griddata arrays should be
+    written to the model output netcdf file.
+    """
+
+
+# Eventually we may be able to take advantage of NumPy
+# support for shape parameters:
+# https://github.com/numpy/numpy/issues/16544
+#
+# We can still take advantage of type parameters.
+
+
+@define
+class PackageData:
+    strt: NDArray[np.float64] = field(
+        metadata={"longname": "starting head", "shape": ("nodes")}
+    )
+    """
+    is the initial (starting) head---that is, head at the
+    beginning of the GWF Model simulation.  STRT must be specified for
+    all simulations, including steady-state simulations. One value is
+    read for every model cell. For simulations in which the first stress
+    period is steady state, the values used for STRT generally do not
+    affect the simulation (exceptions may occur if cells go dry and (or)
+    rewet). The execution time, however, will be less if STRT includes
+    hydraulic heads that are close to the steady-state solution.  A head
+    value lower than the cell bottom can be provided if a cell should
+    start as dry.
+    """
+
+
+# Putting it all together:
+
+
+@define
+class GwfIc:
+    options: Options = field()
+    packagedata: PackageData = field()
+
+
+# ## GWF OC
+#
+# The output control package has a more complicated variable structure.
+# Below docstrings/descriptions are omitted for space-saving.
+
+
+@define
+class Format:
+    columns: int = field()
+    width: int = field()
+    digits: int = field()
+    format: Literal["exponential", "fixed", "general", "scientific"] = field()
+
+
+@define
+class Options:
+    budget_file: Optional[Path] = field(default=None)
+    budget_csv_file: Optional[Path] = field(default=None)
+    head_file: Optional[Path] = field(default=None)
+    printhead: Optional[Format] = field(default=None)
+
+
+# It's awkward to have single-parameter classes, but
+# it's the only way I got `cattrs` to distinguish a
+# number of choices with the same shape in a union
+# like `OCSetting`. There may be a better way.
+
+
+@define
+class All:
+    all: bool = field()
+
+
+@define
+class First:
+    first: bool = field()
+
+
+@define
+class Last:
+    last: bool = field()
+
+
+@define
+class Steps:
+    steps: List[int] = field()
+
+
+@define
+class Frequency:
+    frequency: int = field()
+
+
+PrintSave = Literal["print", "save"]
+RType = Literal["budget", "head"]
+OCSetting = Union[All, First, Last, Steps, Frequency]
+
+
+@define
+class OutputControlData:
+    printsave: PrintSave = field()
+    rtype: RType = field()
+    ocsetting: OCSetting = field()
+
+    @classmethod
+    def from_tuple(cls, t):
+        t = list(t)
+        printsave = t.pop(0)
+        rtype = t.pop(0)
+        ocsetting = {
+            "all": All,
+            "first": First,
+            "last": Last,
+            "steps": Steps,
+            "frequency": Frequency,
+        }[t.pop(0).lower()](t)
+        return cls(printsave, rtype, ocsetting)
+
+
+Period = List[OutputControlData]
+Periods = List[Period]
+
+
+@define
+class GwfOc:
+    options: Options = field()
+    periods: Periods = field()
+
+
+# We now set up a `cattrs` converter to convert an unstructured
+# representation of the package input data to a structured form.
+
+converter = Converter()
+
+
+# Register a hook for the `OutputControlData.from_tuple` method.
+# MODFLOW 6 defines records as tuples, from which we'll need to
+# instantiate objects.
+
+
+def output_control_data_hook(value, _) -> OutputControlData:
+    return OutputControlData.from_tuple(value)
+
+
+converter.register_structure_hook(OutputControlData, output_control_data_hook)
+
+
+# We can inspect the input specification with `attrs` machinery.
+
+
+spec = fields_dict(OutputControlData)
+assert len(spec) == 3
+
+ocsetting = spec["ocsetting"]
+assert ocsetting.type is OCSetting
+
+
+# We can define a block with some data.
+
+
+options = Options(
+    budget_file="some/file/path.cbc",
+)
+assert isinstance(options.budget_file, str)  # TODO path
+assert len(asdict(options)) == 4
+
+
+# We can load a record from a tuple.
+
+
+ocdata = OutputControlData.from_tuple(("print", "budget", "steps", 1, 3, 5))
+assert ocdata.printsave == "print"
+assert ocdata.rtype == "budget"
+assert ocdata.ocsetting == Steps([1, 3, 5])
+
+
+# We can load the full package from an unstructured dictionary,
+# as would be returned by a separate IO layer in the future.
+# (Either hand-written or using e.g. lark.)
+
+
+gwfoc = converter.structure(
+    {
+        "options": {
+            "budget_file": "some/file/path.cbc",
+            "head_file": "some/file/path.hds",
+            "printhead": {
+                "columns": 1,
+                "width": 10,
+                "digits": 8,
+                "format": "scientific",
+            },
+        },
+        "periods": [
+            [
+                ("print", "budget", "steps", 1, 3, 5),
+                ("save", "head", "frequency", 2),
+            ]
+        ],
+    },
+    GwfOc,
+)
+assert gwfoc.options.budget_file == Path("some/file/path.cbc")
+assert gwfoc.options.printhead.width == 10
+assert gwfoc.options.printhead.format == "scientific"
+period = gwfoc.periods[0]
+assert len(period) == 2
+assert period[0] == OutputControlData.from_tuple(
+    ("print", "budget", "steps", 1, 3, 5)
+)
diff --git a/docs/examples/input_data_model_example.py b/docs/examples/input_data_model_example.py
new file mode 100644
index 0000000..5c694b7
--- /dev/null
+++ b/docs/examples/input_data_model_example.py
@@ -0,0 +1,83 @@
+# # Input data model
+#
+# TODO: flesh this out, describe prospective plan for type hints,
+# determine whether we want to work directly with numpy or with
+# e.g. xarray, etc. This will probably evolve for a while as we
+# rework the prototype with c/attrs
+#
+# FloPy organizes input variables in components: simulations, models,
+# packages, and subpackages.
+#
+# ```mermaid
+# classDiagram
+#     Simulation *-- "1+" Package
+#     Simulation *-- "1+" Model
+#     Simulation *-- "1+" Variable
+#     Model *-- "1+" Package
+#     Model *-- "1+" Subpackage
+#     Model *-- "1+" Variable
+#     Package *-- "1+" Subpackage
+#     Package *-- "1+" Variable
+# ```
+#
+# Note that this is not identical to the underlying object model, which
+# is yet to be determined. TODO: update this once we have a full prototype.
+#
+# # Variable types
+#
+# Variables are scalars, paths, arrays, or composite types: list, sum, union.
+#
+# MODFLOW 6 defines the following scalar types:
+#
+# - `keyword`
+# - `integer`
+# - `double precision`
+# - `string`
+#
+# And the following composites:
+#
+# - `record`: product type
+# - `keystring`: union type
+# - `recarray`: list type
+#
+# Scalars may (and `recarray` must) have a `shape`. If a scalar has a `shape`,
+# its type becomes a homogeneous scalar array. A `recarray` may contain records
+# or unions of records as items.
+#
+# We map this typology roughly to the following in Python:
+#
+# TODO: update the following as we develop a more concrete idea of what
+# type hints corresponding to the mf6 input data model will look like
+
+# +
+from os import PathLike
+from typing import (
+    Any,
+    Iterable,
+    Tuple,
+    Union,
+)
+
+from numpy.typing import ArrayLike
+from pandas import DataFrame
+
+Scalar = Union[bool, int, float, str]
+Path = PathLike
+Array = ArrayLike
+Record = Tuple[Union[Scalar, "Record"], ...]
+Table = Union[Iterable[Record], DataFrame]
+List = Iterable[Any]
+Variable = Union[Scalar, Array, Record, Table, List]
+# -
+
+# Note that:
+#
+# - Keystrings are omitted above, since a keystring simply becomes a
+# `Union` of records or scalars.
+# - List input may be regular (items all have the same record type) or
+# irregular (items are unions of records).
+# - A table is a special case of list input; since it is regular it can
+# be represented as a dataframe.
+# - While MODFLOW 6 typically formulates file path inputs as records with
+# 3 fields (identifying keyword, `filein`/`fileout`, and filename), FloPy
+# simply accepts `PathLike`.
diff --git a/docs/examples/record_example.py b/docs/examples/record_example.py
new file mode 100644
index 0000000..df1a057
--- /dev/null
+++ b/docs/examples/record_example.py
@@ -0,0 +1,12 @@
+# # Record variables
+#
+# This example demonstrates how to work with record input variables.
+#
+# A record variable is a product type. Record fields can be scalars,
+# other record variables, or unions of such.
+#
+# MODFLOW 6 represents records as (possibly variadic) tuples. FloPy
+# supports both a low-level tuple interface for records, conforming
+# to MODFLOW 6, and a high-level, strongly-typed record interface.
+#
+# TODO flesh out
diff --git a/flopy4/typing.py b/flopy4/typing.py
index 3ad3f22..cfe9379 100644
--- a/flopy4/typing.py
+++ b/flopy4/typing.py
@@ -1,6 +1,6 @@
 """Enumerates supported input variable types."""
 
-from pathlib import Path
+from os import PathLike
 from typing import (
     Iterable,
     Tuple,
@@ -9,21 +9,25 @@
 
 from numpy.typing import ArrayLike
 
-Scalar = Union[bool, int, float, str, Path]
+Scalar = Union[bool, int, float, str]
 """A scalar input variable."""
 
 
-Array = ArrayLike
-"""An array input variable"""
+Path = PathLike
+"""A file path input variable."""
 
 
-Table = Iterable["Record"]
-"""A table input variable."""
+Array = ArrayLike
+"""An array input variable."""
 
 
 Record = Tuple[Union[Scalar, "Record"], ...]
 """A record input variable."""
 
 
+Table = Iterable["Record"]
+"""A table input variable."""
+
+
 Variable = Union[Scalar, Array, Table, Record]
 """An input variable."""
diff --git a/pixi.lock b/pixi.lock
index 1cc48eb..8272c1c 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -191,7 +191,7 @@ environments:
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       - pypi: https://files.pythonhosted.org/packages/a0/d9/a1e041c5e7caa9a05c925f4bdbdfb7f006d1f74996af53467bc394c97be7/importlib_metadata-8.5.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl
-      - pypi: https://files.pythonhosted.org/packages/dc/9d/8b6dde58a028a3962ce17e84d5fe73758df61378e00ef8ac3d85da34b0ff/interegular-0.3.3.tar.gz
+      - pypi: https://files.pythonhosted.org/packages/c4/01/72d6472f80651673716d1deda2a5bbb633e563ecf94f4479da5519d69d25/interegular-0.3.3-py37-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/94/5c/368ae6c01c7628438358e6d337c19b05425727fbb221d2a3c4303c372f42/ipykernel-6.29.5-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/f4/3a/5d8680279ada9571de8469220069d27024ee47624af534e537c9ff49a450/ipython-8.28.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/22/2d/9c0b76f2f9cc0ebede1b9371b6f317243028ed60b90705863d493bae622e/ipywidgets-8.1.5-py3-none-any.whl
@@ -358,7 +358,7 @@ environments:
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       - pypi: https://files.pythonhosted.org/packages/a0/d9/a1e041c5e7caa9a05c925f4bdbdfb7f006d1f74996af53467bc394c97be7/importlib_metadata-8.5.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl
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-  - interegular<0.4.0,>=0.3.1 ; extra == 'interegular'
+  - interegular>=0.3.1,<0.4.0 ; extra == 'interegular'
   - js2py ; extra == 'nearley'
   - regex ; extra == 'regex'
   requires_python: '>=3.8'
@@ -5116,7 +5117,7 @@ packages:
   sha256: f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f
   requires_dist:
   - jupyter-client>=6.1.12
-  - jupyter-core!=5.0.*,>=4.12
+  - jupyter-core>=4.12,!=5.0.*
   - nbformat>=5.1
   - traitlets>=5.4
   - pre-commit ; extra == 'dev'
@@ -5135,7 +5136,7 @@ packages:
   - nbconvert>=7.0.0 ; extra == 'test'
   - pytest-asyncio ; extra == 'test'
   - pytest-cov>=4.0 ; extra == 'test'
-  - pytest<8,>=7.0 ; extra == 'test'
+  - pytest>=7.0,<8 ; extra == 'test'
   - testpath ; extra == 'test'
   - xmltodict ; extra == 'test'
   requires_python: '>=3.8.0'
@@ -5153,7 +5154,7 @@ packages:
   - jupyter-core>=4.7
   - jupyterlab-pygments
   - markupsafe>=2.0
-  - mistune<4,>=2.0.3
+  - mistune>=2.0.3,<4
   - nbclient>=0.5.0
   - nbformat>=5.7
   - packaging
@@ -5198,7 +5199,7 @@ packages:
   requires_dist:
   - fastjsonschema>=2.15
   - jsonschema>=2.6
-  - jupyter-core!=5.0.*,>=4.12
+  - jupyter-core>=4.12,!=5.0.*
   - traitlets>=5.1
   - myst-parser ; extra == 'docs'
   - pydata-sphinx-theme ; extra == 'docs'
@@ -5268,10 +5269,10 @@ packages:
   url: https://files.pythonhosted.org/packages/46/77/53732fbf48196af9e51c2a61833471021c1d77d335d57b96ee3588c0c53d/notebook-7.2.2-py3-none-any.whl
   sha256: c89264081f671bc02eec0ed470a627ed791b9156cad9285226b31611d3e9fe1c
   requires_dist:
-  - jupyter-server<3,>=2.4.0
-  - jupyterlab-server<3,>=2.27.1
-  - jupyterlab<4.3,>=4.2.0
-  - notebook-shim<0.3,>=0.2
+  - jupyter-server>=2.4.0,<3
+  - jupyterlab-server>=2.27.1,<3
+  - jupyterlab>=4.2.0,<4.3
+  - notebook-shim>=0.2,<0.3
   - tornado>=6.2.0
   - hatch ; extra == 'dev'
   - pre-commit ; extra == 'dev'
@@ -5283,8 +5284,8 @@ packages:
   - sphinxcontrib-spelling ; extra == 'docs'
   - importlib-resources>=5.0 ; python_full_version < '3.10' and extra == 'test'
   - ipykernel ; extra == 'test'
-  - jupyter-server[test]<3,>=2.4.0 ; extra == 'test'
-  - jupyterlab-server[test]<3,>=2.27.1 ; extra == 'test'
+  - jupyter-server[test]>=2.4.0,<3 ; extra == 'test'
+  - jupyterlab-server[test]>=2.27.1,<3 ; extra == 'test'
   - nbval ; extra == 'test'
   - pytest-console-scripts ; extra == 'test'
   - pytest-timeout ; extra == 'test'
@@ -5298,7 +5299,7 @@ packages:
   url: https://files.pythonhosted.org/packages/f9/33/bd5b9137445ea4b680023eb0469b2bb969d61303dedb2aac6560ff3d14a1/notebook_shim-0.2.4-py3-none-any.whl
   sha256: 411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef
   requires_dist:
-  - jupyter-server<3,>=1.8
+  - jupyter-server>=1.8,<3
   - pytest ; extra == 'test'
   - pytest-console-scripts ; extra == 'test'
   - pytest-jupyter ; extra == 'test'
@@ -6566,7 +6567,7 @@ packages:
   - platformdirs>=2.5.0
   - packaging>=20.0
   - requests>=2.19.0
-  - tqdm<5.0.0,>=4.41.0 ; extra == 'progress'
+  - tqdm>=4.41.0,<5.0.0 ; extra == 'progress'
   - paramiko>=2.7.0 ; extra == 'sftp'
   - xxhash>=1.4.3 ; extra == 'xxhash'
   requires_python: '>=3.7'
@@ -6671,7 +6672,7 @@ packages:
   requires_dist:
   - iniconfig
   - packaging
-  - pluggy<2,>=1.5
+  - pluggy>=1.5,<2
   - exceptiongroup>=1.0.0rc8 ; python_full_version < '3.11'
   - tomli>=1 ; python_full_version < '3.11'
   - colorama ; sys_platform == 'win32'
@@ -7043,7 +7044,7 @@ packages:
   sha256: a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
   requires_dist:
   - six>=1.5
-  requires_python: '!=3.0.*,!=3.1.*,!=3.2.*,>=2.7'
+  requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*'
 - kind: pypi
   name: python-dotenv
   version: 1.0.1
@@ -7287,12 +7288,12 @@ packages:
   url: https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl
   sha256: 70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6
   requires_dist:
-  - charset-normalizer<4,>=2
-  - idna<4,>=2.5
-  - urllib3<3,>=1.21.1
+  - charset-normalizer>=2,<4
+  - idna>=2.5,<4
+  - urllib3>=1.21.1,<3
   - certifi>=2017.4.17
-  - pysocks!=1.5.7,>=1.5.6 ; extra == 'socks'
-  - chardet<6,>=3.0.2 ; extra == 'use-chardet-on-py3'
+  - pysocks>=1.5.6,!=1.5.7 ; extra == 'socks'
+  - chardet>=3.0.2,<6 ; extra == 'use-chardet-on-py3'
   requires_python: '>=3.8'
 - kind: pypi
   name: requests-toolbelt
@@ -7300,7 +7301,7 @@ packages:
   url: https://files.pythonhosted.org/packages/3f/51/d4db610ef29373b879047326cbf6fa98b6c1969d6f6dc423279de2b1be2c/requests_toolbelt-1.0.0-py2.py3-none-any.whl
   sha256: cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06
   requires_dist:
-  - requests<3.0.0,>=2.0.1
+  - requests>=2.0.1,<3.0.0
   requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*'
 - kind: pypi
   name: rfc3339-validator
@@ -7426,7 +7427,7 @@ packages:
   - pywin32 ; sys_platform == 'win32' and extra == 'nativelib'
   - pyobjc-framework-cocoa ; sys_platform == 'darwin' and extra == 'objc'
   - pywin32 ; sys_platform == 'win32' and extra == 'win32'
-  requires_python: '!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7'
+  requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*'
 - kind: pypi
   name: setuptools
   version: 75.1.0
@@ -7460,11 +7461,11 @@ packages:
   - sphinx-inline-tabs ; extra == 'doc'
   - sphinx-reredirects ; extra == 'doc'
   - sphinxcontrib-towncrier ; extra == 'doc'
-  - sphinx-notfound-page<2,>=1 ; extra == 'doc'
+  - sphinx-notfound-page>=1,<2 ; extra == 'doc'
   - pyproject-hooks!=1.1 ; extra == 'doc'
   - towncrier<24.7 ; extra == 'doc'
   - pytest-enabler>=2.2 ; extra == 'enabler'
-  - pytest!=8.1.*,>=6 ; extra == 'test'
+  - pytest>=6,!=8.1.* ; extra == 'test'
   - virtualenv>=13.0.0 ; extra == 'test'
   - wheel>=0.44.0 ; extra == 'test'
   - pip>=19.1 ; extra == 'test'
@@ -7705,7 +7706,7 @@ packages:
   - pre-commit ; extra == 'test'
   - pytest-mock ; extra == 'test'
   - pytest-mypy-testing ; extra == 'test'
-  - pytest<8.2,>=7.0 ; extra == 'test'
+  - pytest>=7.0,<8.2 ; extra == 'test'
   requires_python: '>=3.8'
 - kind: pypi
   name: twine
@@ -7716,7 +7717,7 @@ packages:
   - pkginfo>=1.8.1
   - readme-renderer>=35.0
   - requests>=2.20
-  - requests-toolbelt!=0.9.0,>=0.8.0
+  - requests-toolbelt>=0.8.0,!=0.9.0
   - urllib3>=1.26.0
   - importlib-metadata>=3.6
   - keyring>=15.1
@@ -7805,8 +7806,8 @@ packages:
   requires_dist:
   - brotli>=1.0.9 ; platform_python_implementation == 'CPython' and extra == 'brotli'
   - brotlicffi>=0.8.0 ; platform_python_implementation != 'CPython' and extra == 'brotli'
-  - h2<5,>=4 ; extra == 'h2'
-  - pysocks!=1.5.7,<2.0,>=1.5.6 ; extra == 'socks'
+  - h2>=4,<5 ; extra == 'h2'
+  - pysocks>=1.5.6,!=1.5.7,<2.0 ; extra == 'socks'
   - zstandard>=0.18.0 ; extra == 'zstd'
   requires_python: '>=3.8'
 - kind: conda
@@ -7963,7 +7964,7 @@ packages:
   - sphinx-lint ; extra == 'doc'
   - jaraco-tidelift>=1.4 ; extra == 'doc'
   - pytest-enabler>=2.2 ; extra == 'enabler'
-  - pytest!=8.1.*,>=6 ; extra == 'test'
+  - pytest>=6,!=8.1.* ; extra == 'test'
   - jaraco-itertools ; extra == 'test'
   - jaraco-functools ; extra == 'test'
   - more-itertools ; extra == 'test'
diff --git a/pyproject.toml b/pyproject.toml
index 880d504..ba7ceea 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,6 +47,7 @@ dependencies = [
 dynamic = ["version"]
 
 [project.optional-dependencies]
+dev = ["flopy4[lint,test,build]"]
 lint = [
     "ruff"
 ]
diff --git a/test/attrs/test_gwfic.py b/test/attrs/test_gwfic.py
deleted file mode 100644
index d407ee0..0000000
--- a/test/attrs/test_gwfic.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import numpy as np
-from attr import define, field
-from numpy.typing import NDArray
-
-
-@define
-class Options:
-    export_array_ascii: bool = field(
-        default=False,
-        metadata={"longname": "export array variables to netcdf output files"},
-    )
-    """
-    keyword that specifies input griddata arrays should be
-    written to layered ascii output files.
-    """
-
-    export_array_netcdf: bool = field(
-        default=False, metadata={"longname": "starting head"}
-    )
-    """
-    keyword that specifies input griddata arrays should be
-    written to the model output netcdf file.
-    """
-
-
-@define
-class PackageData:
-    strt: NDArray[np.float64] = field(
-        metadata={"longname": "starting head", "shape": ("nodes")}
-    )
-    """
-    is the initial (starting) head---that is, head at the
-    beginning of the GWF Model simulation.  STRT must be specified for
-    all simulations, including steady-state simulations. One value is
-    read for every model cell. For simulations in which the first stress
-    period is steady state, the values used for STRT generally do not
-    affect the simulation (exceptions may occur if cells go dry and (or)
-    rewet). The execution time, however, will be less if STRT includes
-    hydraulic heads that are close to the steady-state solution.  A head
-    value lower than the cell bottom can be provided if a cell should
-    start as dry.
-    """
-
-
-@define
-class GwfIc:
-    options: Options = field()
-    packagedata: PackageData = field()
diff --git a/test/attrs/test_gwfoc.py b/test/attrs/test_gwfoc.py
deleted file mode 100644
index 3eeabce..0000000
--- a/test/attrs/test_gwfoc.py
+++ /dev/null
@@ -1,216 +0,0 @@
-from pathlib import Path
-from typing import List, Literal, Optional, Union
-
-from attr import asdict, define, field, fields_dict
-from cattr import Converter
-
-ArrayFormat = Literal["exponential", "fixed", "general", "scientific"]
-
-
-@define
-class PrintFormat:
-    columns: int = field()
-    """
-    number of columns for writing data
-    """
-
-    width: int = field()
-    """
-    width for writing each number
-    """
-
-    digits: int = field()
-    """
-    number of digits to use for writing a number
-    """
-
-    array_format: ArrayFormat = field()
-    """
-    write format can be EXPONENTIAL, FIXED, GENERAL, or SCIENTIFIC
-    """
-
-
-@define
-class Options:
-    budget_file: Optional[Path] = field(default=None)
-    """
-    name of the output file to write budget information
-    """
-
-    budget_csv_file: Optional[Path] = field(default=None)
-    """
-    name of the comma-separated value (CSV) output 
-    file to write budget summary information. 
-    A budget summary record will be written to this 
-    file for each time step of the simulation.
-    """
-
-    head_file: Optional[Path] = field(default=None)
-    """
-    name of the output file to write head information.
-    """
-
-    print_format: Optional[PrintFormat] = field(default=None)
-    """
-    specify format for printing to the listing file
-    """
-
-
-@define
-class All:
-    all: bool = field()
-    """
-    keyword to indicate save for all time steps in period.
-    """
-
-
-@define
-class First:
-    first: bool = field()
-    """
-    keyword to indicate save for first step in period.
-    """
-
-
-@define
-class Last:
-    last: bool = field()
-    """
-    keyword to indicate save for last step in period
-    """
-
-
-@define
-class Steps:
-    steps: List[int] = field()
-    """
-    save for each step specified
-    """
-
-
-@define
-class Frequency:
-    frequency: int = field()
-    """
-    save at the specified time step frequency.
-    """
-
-
-# It's awkward to have single-parameter contexts, but
-# it's the only way I got `cattrs` to distinguish the
-# choices in the union. There is likely a better way.
-
-
-PrintSave = Literal["print", "save"]
-RType = Literal["budget", "head"]
-OCSetting = Union[All, First, Last, Steps, Frequency]
-
-
-@define
-class OutputControlData:
-    printsave: PrintSave = field()
-    rtype: RType = field()
-    ocsetting: OCSetting = field()
-
-    @classmethod
-    def from_tuple(cls, t):
-        t = list(t)
-        printsave = t.pop(0)
-        rtype = t.pop(0)
-        ocsetting = {
-            "all": All,
-            "first": First,
-            "last": Last,
-            "steps": Steps,
-            "frequency": Frequency,
-        }[t.pop(0).lower()](t)
-        return cls(printsave, rtype, ocsetting)
-
-
-Period = List[OutputControlData]
-Periods = List[Period]
-
-
-@define
-class GwfOc:
-    options: Options = field()
-    """
-    options block
-    """
-
-    periods: Periods = field()
-    """
-    period blocks
-    """
-
-
-# Converter
-
-converter = Converter()
-
-
-def output_control_data_hook(value, type) -> OutputControlData:
-    return OutputControlData.from_tuple(value)
-
-
-converter.register_structure_hook(OutputControlData, output_control_data_hook)
-
-
-# Tests
-
-
-def test_spec():
-    spec = fields_dict(OutputControlData)
-    assert len(spec) == 3
-
-    ocsetting = spec["ocsetting"]
-    assert ocsetting.type is OCSetting
-
-
-def test_options_to_dict():
-    options = Options(
-        budget_file="some/file/path.cbc",
-    )
-    assert isinstance(options.budget_file, str)  # TODO path
-    assert len(asdict(options)) == 4
-
-
-def test_output_control_data_from_tuple():
-    ocdata = OutputControlData.from_tuple(
-        ("print", "budget", "steps", 1, 3, 5)
-    )
-    assert ocdata.printsave == "print"
-    assert ocdata.rtype == "budget"
-    assert ocdata.ocsetting == Steps([1, 3, 5])
-
-
-def test_gwfoc_from_dict():
-    gwfoc = converter.structure(
-        {
-            "options": {
-                "budget_file": "some/file/path.cbc",
-                "head_file": "some/file/path.hds",
-                "print_format": {
-                    "columns": 1,
-                    "width": 10,
-                    "digits": 8,
-                    "array_format": "scientific",
-                },
-            },
-            "periods": [
-                [
-                    ("print", "budget", "steps", 1, 3, 5),
-                    ("save", "head", "frequency", 2),
-                ]
-            ],
-        },
-        GwfOc,
-    )
-    assert gwfoc.options.budget_file == Path("some/file/path.cbc")
-    assert gwfoc.options.print_format.width == 10
-    assert gwfoc.options.print_format.array_format == "scientific"
-    period = gwfoc.periods[0]
-    assert len(period) == 2
-    assert period[0] == OutputControlData.from_tuple(
-        ("print", "budget", "steps", 1, 3, 5)
-    )