---
- branch: MAIN
  date: Sat Mar 11 10:05:26 UTC 2023
  files:
  - new: '1.6'
    old: '1.5'
    path: pkgsrc/math/R-nimble/Makefile
    pathrev: pkgsrc/math/R-nimble/Makefile@1.6
    type: modified
  - new: '1.6'
    old: '1.5'
    path: pkgsrc/math/R-nimble/distinfo
    pathrev: pkgsrc/math/R-nimble/distinfo@1.6
    type: modified
  id: 20230311T100526Z.25f9a17ec51153c789ee67863ee9cea1f1aed7b7
  log: |
    (math/R-nimble)  Updated 0.12.2 to 0.13.1

    (From: nimble/inst/NEWS.md)
    #                            CHANGES IN VERSION 0.13.1 (December 2022)

    ## BUG FIXES

    - Fix bug in MCMC sampler inclusion/exclusion of predictive nodes from
      target node dependencies (PR #1248).

    #                            CHANGES IN VERSION 0.13.0 (November 2022)

    ## USER LEVEL CHANGES

    - Thoroughly revamp handling of predictive nodes in MCMC sampling. If MCMC
    results identical to previous versions of NIMBLE are needed in models with
    posterior predictive nodes, set
    `nimbleOptions(MCMCusePredictiveDependenciesInCalculations = TRUE)`
    and `nimbleOptions(MCMCorderPosteriorPredictiveSamplersLast = FALSE)`.

        -- MCMC samplers, by default, will now exclude predictive dependencies
        from internal sampler calculations.  This can be reverted to the old behavior
        of including predictive dependencies in calculations using
        `nimbleOptions(MCMCusePredictiveDependenciesInCalculations = TRUE)`.

        -- At the time of `buildMCMC`, all `posterior_predictive` samplers are
        automatically reordered to operate last among all samplers. Doing so,
        posterior predictive samples are generated conditional on the other values
        in the MCMC sample.  This reordering can be disabled using
        `nimbleOptions(MCMCorderPosteriorPredictiveSamplersLast = FALSE)`
        (but doing so without also setting
        `nimbleOptions(MCMCusePredictiveDependenciesInCalculations = TRUE)` could
        result in samples that are invalid in terms of the joint posterior
        distribution (but with valid samples marginally).

        -- Removal of the `posterior_predictive_branch` sampler.  Filling the same
        role, the `posterior_predictive` sampler now updates all nodes downstream
        of its `target` node.  Assignment of the `posterior_predictive` sampler
        happens automatically during MCMC configuration, unless
        `nimbleOptions(MCMCusePosteriorPredictiveSampler = FALSE)`.

        -- Automatic determination of "predictive" model nodes, which are all
        stochastic non-data nodes that have no data nodes anywhere in their
        downstream dependencies. Tracking of predictive nodes is done
        automatically, but maybe be disabled using
        `nimbleOptions(determinePredictiveNodesInModel = FALSE)`.

        -- New arguments `includePredictive` (default value `TRUE`) and
        `predictiveOnly` (default value `FALSE`), for both the `getNodeNames`
        and the `getDependencies` methods of model objects.  These specify whether
        any predictive nodes are included in the results, and whether only
        predictive nodes are included, respectively.

        -- The MCMC configuration object will issue a warning message if there are
        stochastic non-data nodes which will not undergo MCMC sampling.  This
        warning can be disabled using
        `nimbleOptions(MCMCwarnUnsampledStochasticNodes = FALSE)`.

    - Add option to WAIC system (via `controlWAIC`) to allow additional burnin (in
    addition to standard MCMC burnin) before calculating online WAIC, thereby
    allowing inspection of initial samples without forcing them to be used for WAIC
    (PR #1244).

    - For MCMC configuration `addSampler` method, change name of the
    `scalarComponents` argument to `expandComponents` (PR #1215).

    - Add new `default` argument for the `addSampler` method of MCMC configuration
    objects.  When `default = TRUE`, default samplers (conjugate, or otherwise) will
    be added to the specified nodes.  The addition of this argument provides an
    entry point to the logic of default sampler determination and assignment,
    without creating a new MCMC configuration object (PR #1215).

    - Add new `nodes` argument for the `addSampler` method of MCMC configuration
    objects.  Nodes specified in `nodes` automatically undergo expansion according
    to `expandNodeNames` prior to sampler assignment, allowing for easier assignment
    of samplers to multiple nodes (PR #1215).

    - `rcar_normal` issues an informative error message when invoked from the R
    command line (PR #1243).

    - Warn users of unused constants during model building (PR #1242).

    - Add `replaceSamplers` method to MCMC configuration objects to simplify
    modifying how a node is sampled (PR #1222).

    - Convert `NEWS` to Markdown format for proper rendering in browser
    (issue #1231).

    - Indicate model code that produces warnings about unknown nimbleFunctions
    (issue #370).

    ## BUG FIXES

    - Avoid error occurring when a model variable name starts with "logProb"
    (PR #1240).

    - Avoid error occurring when a model variable is named "i" (PR #1239).

    - Prevent infinite recursion in particular cases in conjugacy checking
    (PR #1228).

    - Fix bug in simulating from `dcar_normal` nodes when multiple nodes passed to
    simulate (issue #1238).

    - Fix error message about duplicate node declarations (PR #1233).

    - Fix another issue with long variable names (PR #1217).

    - Fix warning related to `dataNodes` in WAIC.

    ## DEVELOPER LEVEL CHANGES

    - Remove use of bitwise `|` and `&` operators in C++ code, per CRAN request.

    - Refactor `nimbleMCMC` to pull out model creation (PR #1223).

    - Fix an issue with nested `nimbleList`s on MacOS (PR #1213).
  module: pkgsrc
  subject: 'CVS commit: pkgsrc/math/R-nimble'
  unixtime: '1678529126'
  user: mef