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title: bayesian-demos
tags: statistical-software-demos, statistical-software
robots: noindex, nofollow
---
Bayesian and Monte Carlo Software - Demonstration Application of Standards
==========================================================================
This file demonstrates the application of
[rOpenSci](https://ropensci.org) ’s [standards for statistical
software](https://ropenscilabs.github.io/statistical-software-review-book/standards.html)
to two [Bayesian
software](https://ropenscilabs.github.io/statistical-software-review-book/standards.html#bayesian-and-monte-carlo-software)
packages. These applications are not intended to represent or reflect
evaluations or assessment of the packages, and particularly not of the
extent to which they fail to meet standards. Rather, the demonstrations
are intended to highlight aspects of the software which could be
productively improved by adhering to the standards, and thereby more
generally to demonstrate the general usefulness of these standards in
advancing and improving software quality.
[`fmcmc`](https://github.com/USCbiostats/fmcmc)
-----------------------------------------------
### 1 [General Standards](https://ropenscilabs.github.io/statistical-software-review-book/standards.html#general-standards-for-statistical-software)
#### 1.1 Documentation
- [x] **G1.0** The primary reference is to a JOSS article, with
sufficient secondary references documented throughout.
**Statistical Terminology**
- [ ] **G1.1** Statistical terminology is generally used without
sufficient clarification or definition.
**Function-level Documentation**
- [x] **G1.2** *Software should use
[`roxygen`](https://roxygen2.r-lib.org/) to documentation all
functions.*
- [x] **G1.2a** internal functions are documented in standard
[`roxygen`](https://roxygen2.r-lib.org/) format
**Supplementary Documentation**
- [ ] **G1.3** No code included to reproduce results which form the
basis of performance claims made in associated publications.
- [ ] **G1.4** No code included to compare performance claims with
alternative implementations in other R packages.
#### 1.2 Input Structures
**Uni-variate (Vector) Input**
- [x] **G2.0** Lengths of inputs generally asserted, and explicit
secondary documentation of expected lengths provided.
- [x] **G2.1** Types of inputs generally asserted, and explicit
secondary documentation of expected types provided.
- [ ] **G2.2** Submission of multivariate input to parameters expected
to be univariate is possible, and not caught in pre-processing
- [x] **G2.3** No functions have single-valued character inputs, so
not applicable.
- [ ] **G2.4** Provide appropriate mechanisms to convert between
different *data types*, potentially including:
- [ ] **G2.4a** There is no explicit conversion to `integer` via
`as.integer()`
- [x] **G2.4b** explicit conversion to continuous uses
`as.numeric()`
- [x] **G2.4c** explicit conversion to character uses
`as.character()`
- [x] **G2.4d**–i**G2.4e** No `factor` data used, so not
applicable
- [x] **G2.5** No `factor` data used, so not applicable
**Tabular Input**
- [x] **G2.6**–**G2.9** Pre-processing of rectangular input expected
to be explicitly controlled through user specification of a
log-likelihood function, so not applicable.
**Missing or Undefined Values**
- [ ] **G2.10** There are no checks for missing data
- [ ] **G2.11** There are no options for users to specify how to
handle missing (`NA`) data
- [ ] **G2.12** Functions assume non-missingness, without explicitly
stating such
- [ ] **G2.13** There is no processing of other (non-`NA`) undefined
values.
#### 1.3 Output Structures
- [x] **G4.0** Nothing is written to local files, so not applicable
#### 1.4 Testing
**Test Data Sets**
- [x] **G5.0** No standard data sets used in tests (rather, all tests
use random data simulated with fixed random seeds)
- [x] **G5.1** Data sets are created within software, but not in a way
that is appropriate for export, so not applicable.
**Responses to Unexpected Input**
- [ ] **G5.2** Tests do not cover all error and warning conditions
- [x] **G5.2a** Every message produced `stop()`, `warning()`,
`message()`, or equivalent is unique
- [ ] **G5.2b** Tests do not cover every one of those messages
- [ ] **G5.3** Absence of missing or undefined values in return
objects not explicitly tested.
**Algorithm Tests**
- [x] **G5.4** Correctness tests are appropriately implemented
- [x] **G5.5** Correctness tests are run with a fixed random seed
- [x] **G5.6** Parameter recovery tests appropriately implemented.
- [x] **G5.7** Algorithm performance tests appropriately implemented.
- [x] **G5.8** Edge condition tests appropriately implemented.
- [ ] **G5.9** No noise susceptibility tests implemented.
**Extended tests**
- [x] **G5.10**–**G5.12** No extended tests included, so not
applicable
------------------------------------------------------------------------
### 2 [Bayesian Standards](https://ropenscilabs.github.io/statistical-software-review-book/standards.html#bayesian-and-monte-carlo-software)
#### 2.1 Documentation of Inputs
- [ ] **BS1.0** Uses the term “parameter” to refer to
“hyperparameters”, although does so entirely consistently
- [x] **BS1.1** Extensive descriptions of how to enter data, primarily
via vignettes.
- [ ] **BS1.2** Descriptions of how to specify prior distributions
provided
- [ ] **BS1.2a** Not in `README`
- [x] **BS1.2b** In vignette
- [ ] **BS1.2c** Not in Function-level documentation
- [x] **BS1.3** All parameters which control the computational process
extensively described.
- [x] **BS1.3a** Documents how to use the output of previous
simulations as starting points of subsequent simulations.
- [x] **BS1.3b** Does not document how to use different sampling
algorithms because only implements one.
- [x] **BS1.4** Explicitly describes and provides examples of
convergence checkers.
- [x] **BS1.5** Differences between convergence checkers are
explicitly tested.
#### 2.2 Input Data Structures and Validation
**Input Data**
- [x] **BS2.0** Vector inputs are appropriately pre-processed
regardless of class structures.
- [ ] **BS2.1** Some pre-processing routines are implemented for
alternative columns
- [x] **BS2.1a** Re-classed columns are processed via
`as.data.frame`
- [ ] **BS2.1b** List columns fail
- [ ] **BS2.2** No *pre-checks* are implemented to ensure input data
are commensurate, rather unhelpful errors are issued instead.
**Prior Distributions, Model Specifications, and Hyperparameters**
- [ ] **BS2.3** Hyperparameters are neither validated nor
pre-processed prior to submitting to analytic routines, with errors
passed through to multiple parallel computational chains (`burnin`,
for example, is only used *after* chains have been calculated, to
determine which parts of result are to be discarded).
- [ ] **BS2.4** Lengths of hyperparameter vectors are not explicitly
checked, rather passed through to attempted matrix multiplication,
and then issuing unhelpful error message (“Error in coeffs \* x. :
non-conformable arrays”).
- [x] **BS2.5** Passing to “coeffs \* x.” described above ensures that
lengths of hyperparameter vectors are commensurate with expected
model input
- [ ] **BS2.6** There are no pre-processing checks to validate
appropriateness of numeric values submitted for hyperparameters; for
example, hyperparameters defining second-order moments and given
negative values are passed through, and lead to unhelpful errors
(“Error in if (R\[i\] < klogratio) { : missing value where
TRUE/FALSE needed”).
**Computational Parameters**
- [ ] **BS2.7** There are no checks that values for parameters are
positive (see BS2.3 above; negative `burnin` values still lead to
chains being calculated).
- [ ] **BS2.8** There are no check on lengths of inputs, for example,
passing a length = 2 vector to `burnin` generates warnings on
various conditional checks expecting single values.
- [ ] **BS2.9** There are no checks that arguments are of expected
classes or types, rather passing unexpected classes or types
generally fails at some stage with uninformative error messages.
- [ ] **BS2.10** Parameters of inappropriate type are not
automatically rejected, rather usually fail as described above
(BS2.9).
**Seed Parameters**
- [ ] **BS2.11** Seeds are not able to be passed as a parameter,
rather random generation is determined by the system `seed` value.
- [x] **BS2.12** Results of previous runs can be used as starting
points
- [x] **BS2.13** Each chain is started with a different seed by
default
- [ ] **BS2.14** Seeds can not be passed, so no diagnostic messages
can be issued when identical seeds are passed to distinct
computational chains
- [ ] **BS2.15** There is no advice *not* to use `set.seed()`, rather
it is the suggested way to control random generation.
- [x] **BS2.16** The relevant parameter is called “initial”, which
require no pluralization.
**Output Verbosity**
- [ ] **BS2.17** There is no parameter controlling the verbosity of
output, rather just a single `progress` parameter to switch the
progress bar on and off, while convergence checkers have no
verbosity control at all.
- [x] **BS2.18** It is possible to suppress progress indicators while
retaining verbosity of warnings and errors, through the `progress`
parameter described above (**BS2.17**).
- [ ] **BS2.19** It is not possible to suppress warnings, and many
uninformative warnings may be triggered as described above.
- [ ] **BS2.20** There is no explicit way to catch errors
#### 2.3 Pre-processing and Data Transformation
**Missing Values**
- [ ] **BS3.0** No missing values are permitted, and no checks are
performed, rather missing values are passed through unchecked,
leading to unhelpful error messages.
- [ ] **BS3.1** There are no routines to pre-process missing values
prior to passing data through to main computational algorithms.
**Perfect Collinearity**
- [ ] **BS3.2** There are no pre-processing routines to diagnose
perfect collinearity, and there are no diagnostic messages or
warnings
- [ ] **BS3.3** There are no distinct routines for processing
perfectly collinear data.
#### 2.4 Analytic Algorithms
- [x] **BS4.0** Sampling algorithms are documented, including literary
citation
- [ ] **BS4.1** There are no comparisons with external samplers
- [ ] **BS4.2** There are no methods to validate posterior estimates
(other than convergence checkers used during simulation chains).
- [x] **BS4.3** Several different kinds of convergence checkers are
implemented and documented.
- [x] **BS4.4** Computations are able to be stopped on convergence
- [x] **BS4.5** Appropriate mechanisms are provided for models which
do not converge, by stopping after specified numbers of iterations
regardless of convergence.
- [ ] **BS4.6** There are no tests to confirm that results with
convergence checker are statistically equivalent to results from
equivalent fixed number of samples without convergence checking.
- [ ] **BS4.7** Effects of parameters of convergence checkers are not
tested.
#### 2.5 Return Values
- [ ] **BS5.0** Return objects do not include information on seed(s)
or starting value(s)
- [ ] **BS5.1** Return objects do not include metadata on types (or
classes) and dimensions of input data
- [ ] **BS5.2** Software returns neither their input function of
distributional parameters, nor enables subsequent access to such.
- [ ] **BS5.3** Return object does not include convergence statistics
- [ ] **BS5.4** Return object does not include details of convergence
checker used
- [ ] **BS5.5** Return object does not include diagnostic statistics
to indicate absence of convergence
#### 2.6 Additional Functionality
- [ ] **BS6.0** Return object does not Implement a default `print`
method
- [x] **BS6.1** Return object does Implement a default `plot` method
- [ ] **BS6.2** Software provides and documents abilities to plot
sequences of posterior samples, although burn-in periods are simply
excluded from plots.
- [x] **BS6.3** There are straightforward abilities to plot posterior
distributional estimates
- [x] **BS6.4** Provide `summary` methods for return objects are
provided
- [x] **BS6.5** It is possible to plot both sequences of posterior
samples and distributional estimates together in single graphic
#### 2.7 Tests
**Parameter Recovery Tests**
- [x] **BS7.0** Tests for recovery of parametric estimates of a prior
distribution
- [x] **BS7.1** Tests for recovery of prior distribution in the
absence of additional data
- [x] **BS7.2** Tests for recovery of expected posterior distribution
given a specified prior and input data
**Algorithmic Scaling Tests**
- [ ] **BS7.3** There are no tests for scaling of algorithmic
efficiency with sizes of input data
**Scaling of Input to Output Data**
- [x] **BS7.4** Tests confirm that predicted or fitted values are on
(approximately) the same scale as input values.
------------------------------------------------------------------------
[`rstan`](https://github.com/stan-dev/rstan)
--------------------------------------------
### 3 [General Standards](https://ropenscilabs.github.io/statistical-software-review-book/standards.html#general-standards-for-statistical-software)
#### 3.1 Documentation
- [x] **G1.0** Primary reference is provided.
**Statistical Terminology**
- [x] **G1.1** Statistical terminology clarified and unambiguously
defined.
**Function-level Documentation**
- [ ] **G1.2** [`roxygen`](https://roxygen2.r-lib.org/) not used to
documentation functions.
- [ ] **G1.2a** Internal functions generally not documented in any
form
**Supplementary Documentation**
- [x] **G1.3** Code is provided to reproduce results which form the
basis of performance claims made in associated publications (for
example, for `sbc()`).
- [ ] **G1.4** Code is provided to compare performance claims with
alternative implementations in other R packages (for example, for
`sbc()`).
#### 3.2 Input Structures
**Uni-variate (Vector) Input**
- [ ] **G2.0** There is no explicit documentation of expected lengths
of inputs
- [ ] **G2.1** There is no explicit documentation of expected *data
types* of vector inputs
- [ ] **G2.2** There is no explicit prohibition or restriction on
submission of multivariate input to parameters expected to be
univariate.
- [ ] **G2.3** Some control of single-valued character input provides
- [x] **G2.3a** `match.arg` is used
- [ ] **G2.3b** `tolower` is not used, nor is their explicit
mention of strict case sensitivity
- [x] **G2.4** Provide appropriate mechanisms to convert between
different *data types*, potentially including:
- [x] **G2.4a** explicit conversion to `integer` via
`as.integer()`
- [x] **G2.4b** explicit conversion to continuous via
`as.numeric()`
- [x] **G2.4c** explicit conversion to character via
`as.character()` (and not `paste` or `paste0`)
- [x] **G2.4d**–**G2.4e** There is no explicit `factor` input, so
not applicable
- [x] **G2.5** There is no explicit `factor` input, so not applicable
**Tabular Input**
- [x] **G2.6** Restrictions on tabular input very carefully documented
- [x] **G2.7** There are conversion routines for multivariate input,
for reasons explained in documentation.
- [x] **G2.8** There is no type conversion in which information is
lost, so not applicable.
- [x] **G2.9** Restrictions on tabular input very carefully
documented.
**Missing or Undefined Values**
- [x] **G2.10**–**2.13** Missing data is explicitly prohibited, as
extensively documented, and all appropriate checks are also
implemented.
#### 3.3 Output Structures
- [ ] **G4.0** Processing of names of local files explicitly
documented
#### 3.4 Testing
**Test Data Sets**
- [x] **G5.0** Tests use standard data sets
- [x] **G5.1** Data sets created to test package are not exported, but
are made generally available through direct inclusion within package
sub-directory.
**Responses to Unexpected Input**
- [ ] G4.2 Tests do not cover all error and warning conditions
- [ ] G4.3 Absence of missing or undefined values in return objects
not explicitly tested.
**Algorithm Tests**
- [x] **G5.3** Correctness tests appropriately implemented.
- [x] **G5.5** Correctness tests are run with a fixed random seed
- [x] **G5.6** Parameter recovery tests appropriately implemented.
- [x] **G5.7** Algorithm performance tests appropriately implemented.
- [x] **G5.8** Edge condition tests appropriately implemented.
- [ ] **G5.9** No noise susceptibility tests implemented
**Extended tests**
- [x] G4.10 Extended tests appropriately implemented (notably for
underlying C++ code).
- [x] G4.11 Extended tests require no large data sets, so not
applicable
- [ ] G4.12 Conditions necessary to run extended tests not
appropriately documented
------------------------------------------------------------------------
### 4 [Bayesian Standards](https://ropenscilabs.github.io/statistical-software-review-book/standards.html#bayesian-and-monte-carlo-software)
#### 4.1 Documentation of Inputs
- [ ] **BS1.0** There terms “parameter” and “hyperparameter” are used
interchangeably and ambiguously in much documentation (for example,
in [*Prior Choice
Recommendations*](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations)).
- [x] **BS1.1** There is extensive description of how to enter data
- [x] **BS1.2** There is extensive Description of how to specify prior
distributions
- [ ] **BS1.2a** Not in the main package `README`, although that
seems okay here, as the stan project is too big for that to
serve a simple demonstration purpose.
- [x] **BS1.2b** In at least one package vignette
- [x] **BS1.2c** In function-level documentation, preferably with
code included in examples
- [x] **BS1.3** All parameters which control the computational process
are extensively described
- [x] **BS1.3a** There is sufficient documentation of how to use
the output of previous simulations as starting points of
subsequent simulations.
- [x] **BS1.3b** There is sufficient documentation of how to use
different sampling algorithms.
- [x] **BS1.4** `rstan` does not directly implement convergence
checkers, rather provides convergence statistics throughout, and
exports several well-documented helper functions to aid subsequent
analyses of such.
- [x] **BS1.5** The documentation for the [`optimizing()`
function](https://mc-stan.org/rstan/reference/stanmodel-method-optimizing.html)
describes several ways of controlling convergence to point
estimates.
#### 4.2 Input Data Structures and Validation
**Input Data**
- [ ] **BS2.0** one-dimensional input is not appropriately
pre-processed, with submission of `units` vectors erroring (“both
operands of the expression should be ‘units’ objects”).
- [x] **BS2.1** `rstan` does not convert all plausible forms of
two-dimension input to standard forms, but does document very
explicitly the expected forms for such, and strongly discourages
submitting non-standard formats or classes.
- [x] **BS2.1a** `data.frame` or equivalent objects which have
columns with non-standard class attributes are simply coerced to
numeric, as clearly documented.
- [x] **BS2.1b** Submitting list columns is explicitly and
strongly discouraged in the
[documentation](https://mc-stan.org/rstan/reference/stan.html#passing-data-to-stan),
although possible as described there.
- [x] **BS2.2** Pre-processing routines ensure all input data is
dimensionally commensurate
**Prior Distributions, Model Specifications, and Hyperparameters**
- [x] **BS2.3** Validation and pre-processing of hyperparameters is
expected to be part of user-provide model specifications, as clearly
documented.
- [x] **BS2.4**–**BS2.6** Hyperparameter vectors are not submitted to
`rstan` like most other Bayesian software, and so these standards do
not apply.
**Computational Parameters**
- [ ] **BS2.7** There are some checks that values for parameters are
positive where expected, but these are only implemented within each
chain, not as single pre-processing step.
- [x] **BS2.8** Lengths of inputs are checked and error appropriately
- [x] **BS2.9** Arguments of unexpected classes or types are
appropriately pre-converted
- [ ] **BS2.10** Parameters of inappropriate type are not
automatically rejected; rather are submitted to sub-routines,
triggering unhelpful errors.
**Seed Parameters**
- [x] **BS2.11** Seeds can be passed as a parameter
- [x] **BS2.12** Results of previous runs can be used as starting
points for subsequent runs
- [x] **BS2.13** Each chain is always started with a different seed by
default
- [x] **BS2.14** It is not possible to pass identical seeds to
distinct computational chains, so standard not applicable
- [x] **BS2.15** Advice *not* to use `set.seed()` is explicitly
documented (and has no effect).
- [x] **BS2.16** The parameter controlling starting values is called
`init`, so appropriately not pluralised.
**Output Verbosity**
- [x] **BS2.17** There are multiple ways of controlling the verbosity
of output
- [x] **BS2.18** It is possible to suppress messages and progress
indicators, while retaining verbosity of warnings and errors.
- [x] **BS2.19** It is possible to suppress warnings
- [ ] **BS2.20** No errors are able to be either caught or converted
to warnings
#### 4.3 Pre-processing and Data Transformation
**Missing Values**
- [x] **BS3.0** Documentation and error message explicitly state that
no missing values are permitted
- [x] **BS3.1** No missing values are permitted, no pre-processing
standards not applicable
**Perfect Collinearity**
- [ ] **BS3.2** There are no pre-processing routines to diagnose
perfect collinearity, no are diagnostic messages or warnings issued
- [ ] **BS3.3** There are no distinct routines for processing
perfectly collinear data, rather all data are passed directly to
sampling algorithms
#### 4.4 Analytic Algorithms
- [x] **BS4.0** Sampling algorithms are explicitly documented and
referenced.
- [ ] **BS4.1** There are no explicit comparisons with external
samplers
- [x] **BS4.2** A method to validate posterior estimates is
implemented (via the [`sbc()`
function](https://mc-stan.org/rstan/reference/sbc.html)).
- [x] **BS4.3** No convergence checker as such implemented, but
extensive convergence statistics are provided, documented, and able
to be extracted.
- [x] **BS4.4** Computations are able to be stopped on convergence
(via appropriate post-processing functions such as
[`optimizing()`](https://mc-stan.org/rstan/reference/stanmodel-method-optimizing.html)).
- [x] **BS4.5** Appropriate mechanisms are provided for models which
do not converge.
- [ ] **BS4.6** Convergence statistics are not explicitly tested
within the R package (even though the more general `stan` code is
checked very comprehensively).
- [ ] **BS4.7** Effects of parameters passed to convergence checkers
are not tested.
#### 4.5 Return Values
- [x] **BS5.0** Return objects include both seeds and starting values
(able to be extracted via `get_seed()` and `get_inits()` functions).
- [ ] **BS5.1** Return objects DO NOT include appropriate metadata on
types or dimensions of input data
- [x] **BS5.2** `rstan` enables direct access to prior specification
through the `get_stancode()` and `get_stanmod()` functions.
- [x] **BS5.3** Convergence statistics or equivalent are returned or
able to be extracted.
- [x] **BS5.4** Where multiple checkers are enabled, return details of
convergence checker used
- [x] **BS5.5** Appropriate diagnostic statistics are returned (or
able to be accessed) to indicate absence of convergence
#### 4.6 Additional Functionality
- [x] **BS6.0** Default `print` method for return objects is
implemented
- [x] **BS6.1** Default `plot` method for return objects is
implemented
- [x] **BS6.2** Sequences of posterior samples are able to be plotted,
although burn-in periods are omitted from plot
- [x] **BS6.3** Posterior distributional estimates able to be directed
plotted
- [x] **BS6.4** `summary` methods are provided for return objects
- [ ] **BS6.5** It is not possible to plot both sequences of posterior
samples and distributional estimates together in single graphic
#### 4.7 Tests
**Parameter Recovery Tests**
- [x] **BS7.0** There are tests for recovery of parametric estimates
of prior distribution
- [x] **BS7.1** There are tests for recovery of prior distribution in
the absence of additional data
- [x] **BS7.2** There are tests for recovery of posterior distribution
given specified prior and input data
**Algorithmic Scaling Tests**
- [ ] **BS7.3** There are no tests for scaling of algorithmic
efficiency with sizes of input data
**Scaling of Input to Output Data**
- [x] **BS7.4** There are tests that predicted or fitted values are on
the same scale as input values.
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