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math/Rnimble,
MCMC, Particle Filtering, and Programmable Hierarchical Modeling
Branch: CURRENT,
Version: 0.13.1,
Package name: Rnimble0.13.1,
Maintainer: pkgsrcusersA system for writing hierarchical statistical models largely
compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate
models and do basic Rstyle math, and compiling both models and
nimbleFunctions via custom generated C++. 'NIMBLE' includes default
methods for MCMC, particle filtering, Monte Carlo Expectation
Maximization, and some other tools. The nimbleFunction system makes it
easy to do things like implement new MCMC samplers from R, customize
the assignment of samplers to different parts of a model from R, and
compile the new samplers automatically via C++ alongside the samplers
'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by
making it extensible: New distributions and functions can be added,
including as calls to external compiled code. Although most people
think of MCMC as the main goal of the 'BUGS'/'JAGS' language for
writing models, one can use 'NIMBLE' for writing arbitrary other kinds
of modelgeneric algorithms as well. A full User Manual is available
at <https://rnimble.org>.
Master sites: (Expand)
Version history: (Expand)
 (20230311) Updated to version: Rnimble0.13.1
 (20220428) Updated to version: Rnimble0.12.2
 (20210918) Updated to version: Rnimble0.11.1
 (20200807) Package added to pkgsrc.se, version Rnimble0.9.0 (created)
CVS history: (Expand)
20230311 11:05:26 by Makoto Fujiwara  Files touched by this commit (2)  
Log message:
(math/Rnimble) 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 nondata 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 nondata 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).

20220831 20:41:07 by Brook Milligan  Files touched by this commit (1) 
Log message:
math/Rnimble: Add tool dependency on gmake.
A library, libnimble.a, is compiled by this package using a Gnu makefile.
Add a tool dependency on gmake to avoid syntax errors.

20220428 17:52:38 by Makoto Fujiwara  Files touched by this commit (2) 
Log message:
(math/Rnimble) Updated 0.11.1 to 0.12.2, NEWS.md unknown

20211026 12:56:13 by Nia Alarie  Files touched by this commit (458) 
Log message:
math: Replace RMD160 checksums with BLAKE2s checksums
All checksums have been doublechecked against existing RMD160 and
SHA512 hashes

20211007 16:28:36 by Nia Alarie  Files touched by this commit (458) 
Log message:
math: Remove SHA1 hashes for distfiles

20210918 05:53:43 by Makoto Fujiwara  Files touched by this commit (2)  
Log message:
(math/Rnimble) Updated 0.9.0 to 0.11.1
(from: inst/NEWS)
CHANGES IN VERSION 0.11.1 (May 2021)
USER LEVEL CHANGES
 Add information about categorical sampler and univariate version of ESS
sampler to `help(samplers)`.
BUG FIXES
 Fix to the `posterior_predictive_branch` MCMC sampler, to update
the logprobabilities of the sampled posterior predictive nodes (PR #1127).
CHANGES IN VERSION 0.11.0 (April 2021)
USER LEVEL CHANGES
 Add new `posterior_predictive_branch` MCMC sampler, which is
automatically assigned to trailing dependency node networks of entirely
nondata nodes (jointly posterior predictive branches). This sampler
simulates jointly from the predictive distribtion of these posterior
predictive node branches, and is designed to improve MCMC mixing of the
branch, and consequently of the entire model (PR #1086).
 Allow use of elliptical slice sampler for univariate nodes, which can be
useful in multimodal problems (PR #1109).
 Add a `getParents` method to the model API, allowing one to determine parent
nodes, analogous to use of `getDependencies` to determine child nodes
(PR #1094).
 Add `getConditionallyIndependentSets` method (not yet documented) to the model
API, allowing one to determine nodes that are conditionally independent of
each other given parent nodes (PR #1094).
 Improve efficiency of conjugate samplers by avoiding unneeded calculations
when a conjugate relationship does not involve shifting or scaling (PR #1087).
 Allow use of `nimNumeric`, `nimMatrix`, `nimArray` in model code (PR #1096).
 Add progress bar to `getSamplesDPmeasure` (NCT issue 110).
 Allow model definition using `if` without `else`, fixing a longstanding
oversight (PR #1104).
 Improve warning when multiple nodes provided to `getParam` (PR #1118).
 Check during model building for unnamed elements of `data` and `inits`
(PR #1117).
 Remove error trapping to prevent use of variables in defining node names,
such as `getDependencies('y[idx]')` as this is hard to check robustly and
efficiently (PR #1122).
 Improve error messages when reporting `getParam` cannot calculate a parameter
when checking a model (PR #1112).
 Error trap cases where model nodes are defined in two different declarations,
adding check for overlapping multivariate nodes (PR #1110).
 Improve error trapping of misformed stochastic declarations in models
(PR #1106).
 Increase maximum length of compiler output when using
`compileNimble(..., showCompilerOutput = TRUE)` (NCT issue 205).
 Point to parallelization example on rnimble.org in relevant places of manual.
BUG FIXES
 Fix a bug (issue #1091) causing incorrect node names when having more than
100,000 elements in a vector node or in a dimension of a multidimensional
node (PR #1092).
 Fix `getNodeNames` to return no nodes when `latentOnly` is `TRUE` and model
contains no latent nodes (PR #1116).
 Fix checking for unknown nimbleFunction methods and improve related error
trapping (PRs #1107, #1105).
DEVELOPER LEVEL CHANGES
 Update our testing code/infrastructure to use latest testthat API (PR #1090).
 Shift internal code to use `model$calculate(...)` style rather than
`calculate(model, ...)` style for various node functions (PR #1114).
 Clean up commented out code (PR #1098) and remove unused test files
(PR #1097).
 Update to a newer (but not latest) version of Eigen to suppress some compiler
warnings (PR #1093).
CHANGES IN VERSION 0.10.1 (November 2020)
USER LEVEL CHANGES
 Add `round` argument to `samplesSummary` (PR #1077).
 `samplesSummary` function (and also `runMCMC(..., summary = TRUE)`) was made
to be robust against nonvalid values in posterior samples array (PR #1075).
BUG FIXES
 Fix `makeParamInfo` when there is only one declID involved to address a bug
affecting usage of `getParam`. This bug was introduced in version 0.10.0 when
reducing memory use of `getParam` (PR #1016). This fixes incorrect behavior
of conjugate samplers (because of incorrect inputs from `getParam`) under
certain model structures, in particular statespace style models (PR #1080).
 Prevent usage of marginal version of WAIC (i.e., when not monitoring all
direct stochastic parents of data nodes); use of marginal version of WAIC in
previous versions gave incorrect results (PR #1083).
DEVELOPER LEVEL CHANGES
 Deprecate `samplerAssignmnentRules` system (PR #1078).
 Deprecate `autoBlock` MCMC option (PR #1079).
CHANGES IN VERSION 0.10.0 (October 2020)
USER LEVEL CHANGES
 Greatly extend BNP functionality with the CRP (Chinese restaurant process)
distribution by allowing multiple observations to be grouped together (e.g.,
for longitudinal or time series data) without requiring they be specified
as a multivariate node (PR #1033).
 Add a variety of conjugate cases to BNP conjugate samplers (PR #1033).
 Greatly improve efficiency of model and MCMC building and configuration for
BNPbased models with CRP components (PR #1033).
 Move all sequential Monte Carlo (SMC; aka particle filtering) methods to
new package `nimbleSMC`, including various particlefilterbased MCMC
samplers.
 Prevent use of variables in indexes of nodes, such as `y[idx]`, which was
incorrectly being evaluated based on R scoping rules (PR #1064).
 Allow use of `logdet` in model code (Issue #1018).
 New `resetMV` argument available to `mcmc$run` method. In combination
with `reset = FALSE`, specifying `resetMV = TRUE` will continue the current
run of the MCMC, but discard any previouslycollected samples
(PR #1051; thanks to 'DJRP').
 New methods `setMonitors` and `setMonitors2` added for MCMC configuration
objects. These methods replace the current set of monitors (or monitors2)
with the specified variables (PR #1061).
 Add `as.list` method for modelValues objects (PR #1060).
 Update `getSamplesDPmeasure` function to improve efficiency and reduce
output size; output is now a list of matrices (PR #1059).
 Add `dimensions` argument to `nimbleMCMC` (PR #1058).
 Add method `getWidthHistory` to slice sampler to retrieve sampling history
information (PR #1057; thanks to 'rpatin').
 Various improvements to the manual.
BUG FIXES
 Fix a bug in kfold crossvalidation routine (`runCrossValidate`), where
the merging of MCMC sampler configurations was done incorrectly and causing
incorrect results (PR #1068).
 Fix bug giving incorrect `dwish` density when using nondefault S
parameterization (PR #1017).
 Fix incorrect NaN eigenvalues in singular normalized adjacency matrices
under `dcar_normal` (PR #1019).
 Update all MCMC sampler functions to use a new syntax for control list
element extraction, which prevents a possible bug caused by R's partial
matching of list names (PR #1065).
 Define autogenerated simulation ('r') functions for userdefined
distributions in the global environment to avoid scoping issues (PR #1063).
 Update userdefined distribution processing so userdefined distributions
can be defined inside functions (PR #1063).
 Fix bug preventing use of `dirName` argument to `compileNimble` (PR #1062).
 Fix a bug preventing model building when there are overly long names of
model variables resulting from long deterministic expressions in model code
(PR #1069).
 Fix `buildMCEM` so it works with a compiled model as argument (PR #1028).
 Fix `dmvt` so default unnamed parameters work (PR #1027).
 Fix error in model building in corner case where
`makeVertexNamesFromIndexArray2` made a simplifying assumption to conclude
a block of nodes was contiguous (PR #1026).
 Fix bug in `nimbleRcall` causing runtime warnings when `returnType` is void
(PR #1013).
DEVELOPER LEVEL CHANGES
 Improve efficiency of `getParam` implementation, which improves speed for
MCMC compilation (PR #1016).
 Improve MCMC sampling efficiency by not copying data nodes, only data node
logProbs, during sampler execution for various samplers (PR #1040).
 Update Travis testing to use R 4.0.
 Remove `GID_map` internal to modelValues (PR #1032).
 Remove deprecated function `getLoadingNamespace` and (deprecated) use of
`where=getLoadingNamespace`. Also improve handling of environments set up
by `nimbleFunction` to make it easier to write packages depending on NIMBLE
(PRs #1029, 1011).
 Force intermediates of index range expressions to be of type 'int' for use
in AD (PR #1024).
CHANGES IN VERSION 0.9.1 (May 2020)
USER LEVEL CHANGES
 Switched from use of `system` to `system2` to avoid problems with installation
under R 4.0 on Windows (PR #1003).
 Modify various adaptive MCMC samplers so the exponent controlling the scale
decay of the adaptation is adjustable by user (rather than hardcoded at 0.8
(PR #981).
 Allow `pmin` and `pmax` to be used in models (PR #982).
 Add documentation for `is.na`,`is.nan`,`any`,`all` (PR #988)
 Add system option `MCMCuseConugacy` to control whether conjugate samplers are
used (PR #998).
 Adds checks for `niter`, `nburnin` in the `mcmc$run` method (PR #980).
 Modify print handling in `addSampler` and `configureMCMC` (PRs #986, 989).
 Improve handling of NA values in `dCRP` to avoid error messages when building
models (PR #994).
 Avoid monitoring toplevel data nodes in models (PR #1006).
BUG FIXES
 Modify MCMC `autoBlock` routine to only group Wishart, InverseWishart, and
Dirichlet nodes with themselves, to avoid violating the constraints of those
nodes (PR #999).
 Fix incorrect error message from `warnRHSonlyDynIdx` when variable appears
multiple times on righthand side of a model expression (PR #997).
 Fix `checkDistributionFunctions` to respect default `nDim=0` when extracting
first argument, to avoid error when dimension not specified in userdefined
distributions (PR #992).
 Fix `print` option of `addSampler` (PR #986).
 Improve handling of cases where indexing goes beyond extent of variable in
`expandNodeNames` and related queries of model structure (PR #977).
DEVELOPER LEVEL CHANGES
 Use `inherits` rather than testing for equality of `class(object)` (PR #988).

20200920 05:19:05 by Joerg Sonnenberger  Files touched by this commit (1) 
Log message:
Add missing RR6 dependency

20200807 04:59:26 by Brook Milligan  Files touched by this commit (3) 
Log message:
math/Rnimble: import Rnimble0.9.0
A system for writing hierarchical statistical models largely
compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate
models and do basic Rstyle math, and compiling both models and
nimbleFunctions via custom generated C++. 'NIMBLE' includes default
methods for MCMC, particle filtering, Monte Carlo Expectation
Maximization, and some other tools. The nimbleFunction system makes it
easy to do things like implement new MCMC samplers from R, customize
the assignment of samplers to different parts of a model from R, and
compile the new samplers automatically via C++ alongside the samplers
'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by
making it extensible: New distributions and functions can be added,
including as calls to external compiled code. Although most people
think of MCMC as the main goal of the 'BUGS'/'JAGS' language for
writing models, one can use 'NIMBLE' for writing arbitrary other kinds
of modelgeneric algorithms as well. A full User Manual is available
at <https://rnimble.org>.
