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Version 6.24 (2026/05/15)
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**Highlight: Sensitivity Analysis Section**

A new section that describes Dakota's sensitivity analysis
capabilities has been added to the User Manual.

*Enabling / Accessing:* Visit :ref:`sa`.

**Highlight: JSON-format Input Files**

In addition to the existing freeform input file format, Dakota
now accepts JSON-format input files. The schema and other usage
tips are described in the :ref:`jsoninput` section. JSON input
provides a foundation for more structured, tool-friendly workflows,
and is a stepping stone toward more capable Python bindings.

The capability is experimental, and we appreciate your bug
reports. To fall back to the old input file reader, run Dakota
with the ``-parser nidr`` command line argument.

*Enabling / Accessing:* Write a JSON format input file and use the
``-json`` command line argument (e.g. ``dakota -json dakota_in.json``).

**Highlight: Robustness enhancements for multifidelity sampling**

All multifidelity sampling estimators that involve numerical solutions
for sample allocations (MFMC, ACV, parameterized ACV, and MLBLUE) now
include analytic derivatives of all accuracy and cost metrics, rendering
these allocations more accurate and numerically robust.

*Enabling / Accessing:* no additional specification is required, as
all numerical solutions have been promoted to analytic gradients.

*Documentation:* multifidelity sampling methods that support/require
numerical solutions are described at
:dakkw:`method-multifidelity_sampling`, 
:dakkw:`method-approximate_control_variate`, and
:dakkw:`method-multilevel_blue`.


**Improvements by Category**

*Interfaces, Input/Output*

- The `pyprepro` function is now available in the 
  :ref:`dakota.interfacing <interfaces:dprepro-and-pyprepro>` module.

*UQ Methods*

- Enhancements to numerical solutions within multifidelity Monte Carlo
  (MFMC) implementation: fix any remaining model order dependencies
  when using the default reorder-on-the-fly approach
  (:dakkw:`method-multifidelity_sampling-numerical_solve-model_reordering-auto_reorder`),
  and add an option to enable solutions for a specified model ordering
  by enforcing ordered sample allocations through linear constraints
  (:dakkw:`method-multifidelity_sampling-numerical_solve-model_reordering-fixed_order`).

*Sensitivity Analysis*

- The :dakkw:`method-sampling-variance_based_decomp-vbd_sampling_method-binned` approach 
  to variance-based decomposition now works with discrete variables.

**Miscellaneous Enhancements and Bugfixes**

- Fix issue with trilinos that prevented re-configuration in the same build directory

**Deprecated and Changed**

- Dakota's NIDR (New Input Deck Reader) is being deprecated and replaced with
  a newly written input parser. For this release, NIDR is available as a fallback.
  Use the command line argument ``-parser legacy`` to use it.

**Compatibility**

- The schema for JSON input files is defined by Pydantic models, which are
  located at `python/dakota/spec/` in the source tree. To update Dakota's
  input grammar, Python >=3.9 and Pydantic >=2.12 are required.