./math/R-nimble, MCMC, Particle Filtering, and Programmable Hierarchical Modeling

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Branch: CURRENT, Version: 0.11.1, Package name: R-nimble-0.11.1, Maintainer: pkgsrc-users

A system for writing hierarchical statistical models largely
compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate
models and do basic R-style 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 model-generic algorithms as well. A full User Manual is available
at <https://r-nimble.org>.


Master sites: (Expand)


Version history: (Expand)


CVS history: (Expand)


   2021-10-07 16:28:36 by Nia Alarie | Files touched by this commit (458)
Log message:
math: Remove SHA1 hashes for distfiles
   2021-09-18 05:53:43 by Makoto Fujiwara | Files touched by this commit (2) | Package updated
Log message:
(math/R-nimble) 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 log-probabilities 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
   non-data 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 mis-formed 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 r-nimble.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 multi-dimensional
   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 non-valid 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 state-space 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
   BNP-based models with CRP components (PR #1033).

-- Move all sequential Monte Carlo (SMC; aka particle filtering) methods to
   new package `nimbleSMC`, including various particle-filter-based 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 previously-collected 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 k-fold cross-validation 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 non-default 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 auto-generated simulation ('r') functions for user-defined
   distributions in the global environment to avoid scoping issues (PR #1063).

-- Update user-defined distribution processing so user-defined 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 run-time 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 hard-coded 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 top-level data nodes in models (PR #1006).

BUG FIXES

-- Modify MCMC `autoBlock` routine to only group Wishart, Inverse-Wishart, 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 right-hand 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 user-defined
   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).
   2020-09-20 05:19:05 by Joerg Sonnenberger | Files touched by this commit (1)
Log message:
Add missing R-R6 dependency
   2020-08-07 04:59:26 by Brook Milligan | Files touched by this commit (3)
Log message:
math/R-nimble: import R-nimble-0.9.0

A system for writing hierarchical statistical models largely
compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate
models and do basic R-style 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 model-generic algorithms as well. A full User Manual is available
at <https://r-nimble.org>.