Click here for full text of all standards
srrstats tagNumbers of standards:
BS : 41 / 55
G : 57 / 68
RE : 31 / 48
Total : 129 / 171
Standards in function ‘confint.dynamitefit()’ on line#20 of file R/ci.R:
confint())Standards in function ‘coef.dynamitefit()’ on line#20 of file R/coef.R:
G2.3a Use match.arg() or equivalent where applicable to only permit expected values.
RE4.2 Model coefficients (via coeff() / coefficients())
Standards in on line#53 of file R/dynamite-package.R:
G1.3 All statistical terminology should be clarified and unambiguously defined.*
G1.4 Software should use roxygen2 to document all functions.
G1.4a All internal (non-exported) functions should also be documented in standard roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*
G2.0 Implement assertions on lengths of inputs, particularly through asserting that inputs expected to be single- or multi-valued are indeed so.
G2.0a Provide explicit secondary documentation of any expectations on lengths of inputs
G2.1 Implement assertions on types of inputs (see the initial point on nomenclature above).
G2.10 Software should ensure that extraction or filtering of single columns from tabular inputs should not presume any particular default behaviour, and should ensure all column-extraction operations behave consistently regardless of the class of tabular data used as input.*
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
G2.2 Appropriately prohibit or restrict submission of multivariate input to parameters expected to be univariate.
G2.3 For univariate character input:
G2.3a Use match.arg() or equivalent where applicable to only permit expected values.
G2.3b Either: use tolower() or equivalent to ensure input of character parameters is not case dependent; or explicitly document that parameters are strictly case-sensitive.
G2.7 Software should accept as input as many of the above standard tabular forms as possible, including extension to domain-specific forms.*
G2.8 Software should provide appropriate conversion or dispatch routines as part of initial pre-processing to ensure that all other sub-functions of a package receive inputs of a single defined class or type.
G5.1 Data sets created within, and used to test, a package should be exported (or otherwise made generally available) so that users can confirm tests and run examples.*
BS1.0 Bayesian software which uses the term “hyperparameter” should explicitly clarify the meaning of that term in the context of that software.*
BS1.2 Description of how to specify prior distributions, both in textual form describing the general principles of specifying prior distributions, along with more applied descriptions and examples, within:
BS1.2a The main package README, either as textual description or example code*
BS1.2b At least one package vignette, both as general and applied textual descriptions, and example code*
BS1.2c Function-level documentation, preferably with code included in examples*
BS2.15 Bayesian Software should explicitly enable errors to be caught, and appropriately processed either through conversion to warnings, or otherwise captured in return values. This should be tested.*
BS7.1 Software should demonstrate and confirm recovery of a prior distribution in the absence of any additional data or information
BS7.2 Software should demonstrate and confirm recovery of a expected posterior distribution given a specified prior and some input data*
RE1.2 Regression Software should document expected format (types or classes) for inputting predictor variables, including descriptions of types or classes which are not accepted.*
Standards in function ‘dynamite()’ on line#128 of file R/dynamite.R:
G2.9 Software should issue diagnostic messages for type conversion in which information is lost (such as conversion of variables from factor to character; standardisation of variable names; or removal of meta-data such as those associated with sf-format data) or added (such as insertion of variable or column names where none were provided).*
BS1.1 Descriptions of how to enter data, both in textual form and via code examples. Both of these should consider the simplest cases of single objects representing independent and dependent data, and potentially more complicated cases of multiple independent data inputs.
BS1.3 Description of all parameters which control the computational process (typically those determining aspects such as numbers and lengths of sampling processes, seeds used to start them, thinning parameters determining post-hoc sampling from simulated values, and convergence criteria). In particular:
BS1.3b Where applicable, Bayesian software should document, both in text and examples, how to use different sampling algorithms for a given model.
BS2.12 Bayesian Software should implement at least one parameter controlling the verbosity of output, defaulting to verbose output of all appropriate messages, warnings, errors, and progress indicators.
BS2.13 Bayesian Software should enable suppression of messages and progress indicators, while retaining verbosity of warnings and errors. This should be tested.
BS2.14 Bayesian Software should enable suppression of warnings where appropriate. This should be tested.
BS2.6 Check that values for computational parameters lie within plausible ranges.*
BS2.7 Enable starting values to be explicitly controlled via one or more input parameters, including multiple values for software which implements or enables multiple computational “chains.”
BS2.9 Ensure each chain is started with a different seed by default.
BS3.0 Explicitly document assumptions made in regard to missing values; for example that data is assumed to contain no missing (NA, Inf) values, and that such values, or entire rows including any such values, will be automatically removed from input data.*
BS4.0 Packages should document sampling algorithms (generally via literary citation, or reference to other software)
BS5.0 Return values should include starting value(s) or seed(s), including values for each sequence where multiple sequences are included
BS5.1 Return values should include appropriate metadata on types (or classes) and dimensions of input data*
BS5.2 Bayesian Software should either return the input function or prior distributional specification in the return object; or enable direct access to such via additional functions which accept the return object as single argument.*
BS5.3 Bayesian Software should return convergence statistics or equivalent
BS5.5 Appropriate diagnostic statistics to indicate absence of convergence should either be returned or immediately able to be accessed.*
RE1.1 Regression Software should document how formula interfaces are converted to matrix representations of input data.*
RE1.4 Regression Software should document any assumptions made with regard to input data; for example distributional assumptions, or assumptions that predictor data have mean values of zero. Implications of violations of these assumptions should be both documented and tested.*
RE2.0 Regression Software should document any transformations applied to input data, for example conversion of label-values to factor, and should provide ways to explicitly avoid any default transformations (with error or warning conditions where appropriate).
RE2.1 Regression Software should implement explicit parameters controlling the processing of missing values, ideally distinguishing NA or NaN values from Inf values (for example, through use of na.omit() and related functions from the stats package).*
RE3.0 Issue appropriate warnings or other diagnostic messages for models which fail to converge.
RE3.1 Enable such messages to be optionally suppressed, yet should ensure that the resultant model object nevertheless includes sufficient data to identify lack of convergence.
RE4.0 Regression Software should return some form of “model” object, generally through using or modifying existing class structures for model objects (such as lm, glm, or model objects from other packages), or creating a new class of model objects.
RE4.1 Regression Software may enable an ability to generate a model object without actually fitting values. This may be useful for controlling batch processing of computationally intensive fitting algorithms.*
RE4.13 Predictor variables, and associated “metadata” where applicable.*
RE4.4 The specification of the model, generally as a formula (via formula())
RE4.8 Response variables, and associated “metadata” where applicable.
RE5.0 Scaling relationships between sizes of input data (numbers of observations, with potential extension to numbers of variables/columns) and speed of algorithm.*
Standards in function ‘parse_data()’ on line#334 of file R/dynamite.R:
G2.11 Software should ensure that data.frame-like tabular objects which have columns which do not themselves have standard class attributes (typically, vector) are appropriately processed, and do not error without reason. This behaviour should be tested. Again, columns created by the units package provide a good test case.
G2.12 Software should ensure that data.frame-like tabular objects which have list columns should ensure that those columns are appropriately pre-processed either through being removed, converted to equivalent vector columns where appropriate, or some other appropriate treatment such as an informative error. This behaviour should be tested.*
G2.16 All functions should also provide options to handle undefined values (e.g., NaN, Inf and -Inf), including potentially ignoring or removing such values.*
G2.4d explicit conversion to factor via as.factor()
G2.5 Where inputs are expected to be of factor type, secondary documentation should explicitly state whether these should be ordered or not, and those inputs should provide appropriate error or other routines to ensure inputs follow these expectations.*
G2.6 Software which accepts one-dimensional input should ensure values are appropriately pre-processed regardless of class structures.*
Standards in function ‘dynamiteformula()’ on line#132 of file R/dynamiteformula.R:
G2.3b Either: use tolower() or equivalent to ensure input of character parameters is not case dependent; or explicitly document that parameters are strictly case-sensitive.
RE1.0 Regression Software should enable models to be specified via a formula interface, unless reasons for not doing so are explicitly documented.
Standards in function ‘get_priors()’ on line#41 of file R/exported_gets.R:
Standards in function ‘fitted.dynamitefit()’ on line#49 of file R/fitted.R:
Standards in function ‘lags()’ on line#32 of file R/lags.R:
match.arg() or equivalent where applicable to only permit expected values.Standards in function ‘full_model.matrix()’ on line#6 of file R/model_matrix.R:
RE1.3 Regression Software which passes or otherwise transforms aspects of input data onto output structures should ensure that those output structures retain all relevant aspects of input data, notably including row and column names, and potentially information from other attributes().
RE1.3a Where otherwise relevant information is not transferred, this should be explicitly documented.*
Standards in function ‘test_collinearity()’ on line#49 of file R/model_matrix.R:
BS3.1 Implement pre-processing routines to diagnose perfect collinearity, and provide appropriate diagnostic messages or warnings
BS3.2 Provide distinct routines for processing perfectly collinear data, potentially bypassing sampling algorithms*
RE2.4 Regression Software should implement pre-processing routines to identify whether aspects of input data are perfectly collinear, notably including:
RE2.4a Perfect collinearity among predictor variables
RE2.4b Perfect collinearity between independent and dependent variables*
Standards in function ‘nobs.dynamitefit()’ on line#14 of file R/nobs.R:
nobs())Standards in function ‘plot.dynamitefit()’ on line#38 of file R/plot.R:
BS6.1 Software should implement a default plot method for return objects
BS6.2 Software should provide and document straightforward abilities to plot sequences of posterior samples, with burn-in periods clearly distinguished
BS6.3 Software should provide and document straightforward abilities to plot posterior distributional estimates*
BS6.5 Software may provide abilities to plot both sequences of posterior samples and distributional estimates together in single graphic*
RE6.0 Model objects returned by Regression Software (see* RE4*) should have default plot methods, either through explicit implementation, extension of methods for existing model objects, or through ensuring default methods work appropriately.
RE6.1 Where the default plot method is NOT a generic plot method dispatched on the class of return objects (that is, through an S3-type plot.<myclass> function or equivalent), that method dispatch (or equivalent) should nevertheless exist in order to explicitly direct users to the appropriate function.
Standards in function ‘plot_deltas()’ on line#76 of file R/plot.R:
G2.3a Use match.arg() or equivalent where applicable to only permit expected values.
BS6.1 Software should implement a default plot method for return objects
BS6.3 Software should provide and document straightforward abilities to plot posterior distributional estimates*
RE6.0 Model objects returned by Regression Software (see* RE4*) should have default plot methods, either through explicit implementation, extension of methods for existing model objects, or through ensuring default methods work appropriately.
RE6.1 Where the default plot method is NOT a generic plot method dispatched on the class of return objects (that is, through an S3-type plot.<myclass> function or equivalent), that method dispatch (or equivalent) should nevertheless exist in order to explicitly direct users to the appropriate function.
Standards in function ‘plot_betas()’ on line#154 of file R/plot.R:
BS6.1 Software should implement a default plot method for return objects
BS6.3 Software should provide and document straightforward abilities to plot posterior distributional estimates*
RE6.0 Model objects returned by Regression Software (see* RE4*) should have default plot methods, either through explicit implementation, extension of methods for existing model objects, or through ensuring default methods work appropriately.
RE6.1 Where the default plot method is NOT a generic plot method dispatched on the class of return objects (that is, through an S3-type plot.<myclass> function or equivalent), that method dispatch (or equivalent) should nevertheless exist in order to explicitly direct users to the appropriate function.
Standards in function ‘plot_nus()’ on line#210 of file R/plot.R:
BS6.1 Software should implement a default plot method for return objects
BS6.3 Software should provide and document straightforward abilities to plot posterior distributional estimates*
RE6.0 Model objects returned by Regression Software (see* RE4*) should have default plot methods, either through explicit implementation, extension of methods for existing model objects, or through ensuring default methods work appropriately.
RE6.1 Where the default plot method is NOT a generic plot method dispatched on the class of return objects (that is, through an S3-type plot.<myclass> function or equivalent), that method dispatch (or equivalent) should nevertheless exist in order to explicitly direct users to the appropriate function.
Standards in function ‘predict.dynamitefit()’ on line#60 of file R/predict.R:
G2.13 Statistical Software should implement appropriate checks for missing data as part of initial pre-processing prior to passing data to analytic algorithms.
G2.14 Where possible, all functions should provide options for users to specify how to handle missing (NA) data, with options minimally including:
G2.14a error on missing data
G2.14b ignore missing data with default warnings or messages issued
G2.14c replace missing data with appropriately imputed values
G2.15 Functions should never assume non-missingness, and should never pass data with potential missing values to any base routines with default na.rm = FALSE-type parameters (such as mean(), sd() or cor()).
G2.3a Use match.arg() or equivalent where applicable to only permit expected values.
BS3.0 Explicitly document assumptions made in regard to missing values; for example that data is assumed to contain no missing (NA, Inf) values, and that such values, or entire rows including any such values, will be automatically removed from input data.*
RE2.2 Regression Software should provide different options for processing missing values in predictor and response data. For example, it should be possible to fit a model with no missing predictor data in order to generate values for all associated response points, even where submitted response values may be missing.
RE4.16 Regression Software which models distinct responses for different categorical groups should include the ability to submit new groups to predict() methods.*
Standards in function ‘prepare_stan_input()’ on line#25 of file R/prepare_stan_input.R:
G2.13 Statistical Software should implement appropriate checks for missing data as part of initial pre-processing prior to passing data to analytic algorithms.
G2.14 Where possible, all functions should provide options for users to specify how to handle missing (NA) data, with options minimally including:
G2.14a error on missing data
G2.14b ignore missing data with default warnings or messages issued
G2.14c replace missing data with appropriately imputed values
G2.15 Functions should never assume non-missingness, and should never pass data with potential missing values to any base routines with default na.rm = FALSE-type parameters (such as mean(), sd() or cor()).
G2.16 All functions should also provide options to handle undefined values (e.g., NaN, Inf and -Inf), including potentially ignoring or removing such values.*
G2.4 Provide appropriate mechanisms to convert between different data types, potentially including:
G2.4a explicit conversion to integer via as.integer()
G2.4b explicit conversion to continuous via as.numeric()
G2.4c explicit conversion to character via as.character() (and not paste or paste0)
G2.4d explicit conversion to factor via as.factor()
G2.4e explicit conversion from factor via as...() functions
BS2.1 Bayesian Software should implement pre-processing routines to ensure all input data is dimensionally commensurate, for example by ensuring commensurate lengths of vectors or numbers of rows of tabular inputs.
BS2.1a The effects of such routines should be tested.*
BS2.2 Ensure that all appropriate validation and pre-processing of distributional parameters are implemented as distinct pre-processing steps prior to submitting to analytic routines, and especially prior to submitting to multiple parallel computational chains.
BS2.3 Ensure that lengths of vectors of distributional parameters are checked, with no excess values silently discarded (unless such output is explicitly suppressed, as detailed below).
BS2.4 Ensure that lengths of vectors of distributional parameters are commensurate with expected model input (see example immediately below)
BS2.5 Where possible, implement pre-processing checks to validate appropriateness of numeric values submitted for distributional parameters; for example, by ensuring that distributional parameters defining second-order moments such as distributional variance or shape parameters, or any parameters which are logarithmically transformed, are non-negative.*
Standards in function ‘prepare_channel_default()’ on line#338 of file R/prepare_stan_input.R:
Standards in function ‘print.dynamitefit()’ on line#16 of file R/print.R:
BS5.3 Bayesian Software should return convergence statistics or equivalent
BS5.5 Appropriate diagnostic statistics to indicate absence of convergence should either be returned or immediately able to be accessed.*
BS6.0 Software should implement a default print method for return objects
RE4.17 Model objects returned by Regression Software should implement or appropriately extend a default print method which provides an on-screen summary of model (input) parameters and (output) coefficients.
Standards in function ‘summary.dynamitefit()’ on line#13 of file R/summary.R:
BS6.4 Software may provide summary methods for return objects
RE4.18 Regression Software may also implement summary methods for model objects, and in particular should implement distinct summary methods for any cases in which calculation of summary statistics is computationally non-trivial (for example, for bootstrapped estimates of confidence intervals).*
Standards in on line#8 of file tests/testthat/testthat.R:
G3.0 Statistical software should never compare floating point numbers for equality. All numeric equality comparisons should either ensure that they are made between integers, or use appropriate tolerances for approximate equality.*
G5.12 Any conditions necessary to run extended tests such as platform requirements, memory, expected runtime, and artefacts produced that may need manual inspection, should be described in developer documentation such as a CONTRIBUTING.md or tests/README.md file.
G5.2 Appropriate error and warning behaviour of all functions should be explicitly demonstrated through tests. In particular,
G5.2a Every message produced within R code by stop(), warning(), message(), or equivalent should be unique
G5.2b Explicit tests should demonstrate conditions which trigger every one of those messages, and should compare the result with expected values.
G5.3 For functions which are expected to return objects containing no missing (NA) or undefined (NaN, Inf) values, the absence of any such values in return objects should be explicitly tested.*
G5.5 Correctness tests should be run with a fixed random seed
G5.6b Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).*
RE7.2 Demonstrate that output objects retain aspects of input data such as row or case names (see RE1.3).
RE7.3 Demonstrate and test expected behaviour when objects returned from regression software are submitted to the accessor methods of RE4.2–RE4.7.
Standards in on line#5 of file tests/testthat/test-edgecases.R:
G5.8 *Edge condition tests** *to test that these conditions produce expected behaviour such as clear warnings or errors when confronted with data with extreme properties including but not limited to:
G5.8a Zero-length data
G5.8b Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)
G5.8c Data with all-NA fields or columns or all identical fields or columns
G5.8d Data outside the scope of the algorithm (for example, data with more fields (columns) than observations (rows) for some regression algorithms)
Standards in on line#435 of file tests/testthat/test-errors.R:
G2.11 Software should ensure that data.frame-like tabular objects which have columns which do not themselves have standard class attributes (typically, vector) are appropriately processed, and do not error without reason. This behaviour should be tested. Again, columns created by the units package provide a good test case.
G2.12 Software should ensure that data.frame-like tabular objects which have list columns should ensure that those columns are appropriately pre-processed either through being removed, converted to equivalent vector columns where appropriate, or some other appropriate treatment such as an informative error. This behaviour should be tested.*
Standards in on line#3 of file tests/testthat/test-grunfeld.R:
G5.0 Where applicable or practicable, tests should use standard data sets with known properties (for example, the NIST Standard Reference Datasets, or data sets provided by other widely-used R packages).
G5.4 *Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as RStata).
G5.4a For new methods, it can be difficult to separate out correctness of the method from the correctness of the implementation, as there may not be reference for comparison. In this case, testing may be implemented against simple, trivial cases or against multiple implementations such as an initial R implementation compared with results from a C/C++ implementation.
G5.4b For new implementations of existing methods, correctness tests should include tests against previous implementations. Such testing may explicitly call those implementations in testing, preferably from fixed-versions of other software, or use stored outputs from those where that is not possible.
G5.4c Where applicable, stored values may be drawn from published paper outputs when applicable and where code from original implementations is not available
Standards in on line#6 of file tests/testthat/test-predict.R:
G5.4 *Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as RStata).
BS7.4 Bayesian software should implement tests which confirm that predicted or fitted values are on (approximately) the same scale as input values.
Standards in on line#11 of file tests/testthat/test-recovery.R:
G5.10 Extended tests should included and run under a common framework with other tests but be switched on by flags such as as a <MYPKG>_EXTENDED_TESTS="true" environment variable.* - The extended tests can be then run automatically by GitHub Actions for example by adding the following to the env section of the workflow:
G5.4 *Correctness tests** *to test that statistical algorithms produce expected results to some fixed test data sets (potentially through comparisons using binding frameworks such as RStata).
G5.4a For new methods, it can be difficult to separate out correctness of the method from the correctness of the implementation, as there may not be reference for comparison. In this case, testing may be implemented against simple, trivial cases or against multiple implementations such as an initial R implementation compared with results from a C/C++ implementation.
G5.4b For new implementations of existing methods, correctness tests should include tests against previous implementations. Such testing may explicitly call those implementations in testing, preferably from fixed-versions of other software, or use stored outputs from those where that is not possible.
G5.4c Where applicable, stored values may be drawn from published paper outputs when applicable and where code from original implementations is not available
G5.5 Correctness tests should be run with a fixed random seed
G5.6 *Parameter recovery tests** *to test that the implementation produce expected results given data with known properties. For instance, a linear regression algorithm should return expected coefficient values for a simulated data set generated from a linear model.
G5.6a Parameter recovery tests should generally be expected to succeed within a defined tolerance rather than recovering exact values.
G5.6b Parameter recovery tests should be run with multiple random seeds when either data simulation or the algorithm contains a random component. (When long-running, such tests may be part of an extended, rather than regular, test suite; see G4.10-4.12, below).*
G5.7 *Algorithm performance tests** *to test that implementation performs as expected as properties of data change. For instance, a test may show that parameters approach correct estimates within tolerance as data size increases, or that convergence times decrease for higher convergence thresholds.
BS7.0 Software should demonstrate and confirm recovery of parametric estimates of a prior distribution
BS7.1 Software should demonstrate and confirm recovery of a prior distribution in the absence of any additional data or information
BS7.2 Software should demonstrate and confirm recovery of a expected posterior distribution given a specified prior and some input data*
Standards in on line#12 of file tests/testthat/test-scaling.R:
BS7.3 Bayesian software should include tests which demonstrate and confirm the scaling of algorithmic efficiency with sizes of input data.*
RE5.0 Scaling relationships between sizes of input data (numbers of observations, with potential extension to numbers of variables/columns) and speed of algorithm.*
Standards in on line#18 of file ./README.Rmd:
G1.1 Statistical Software should document whether the algorithm(s) it implements are:* - The first implementation of a novel algorithm; or - The first implementation within R of an algorithm which has previously been implemented in other languages or contexts; or - An improvement on other implementations of similar algorithms in R.
G1.2 Statistical Software should include a* Life Cycle Statement describing current and anticipated future states of development.
srrstatsNA tagNumbers of standards:
BS : 14 / 55
G : 11 / 68
RE : 17 / 48
Total : 42 / 171
Standards in on line#56 of file R/srr-stats-standards.R:
G1.0 Statistical Software should list at least one primary reference from published academic literature.*
G1.5 Software should include all code necessary to reproduce results which form the basis of performance claims made in associated publications.*
G1.6 Software should include code necessary to compare performance claims with alternative implementations in other R packages.*
G3.1 Statistical software which relies on covariance calculations should enable users to choose between different algorithms for calculating covariances, and should not rely solely on covariances from the stats::cov function.
G3.1a The ability to use arbitrarily specified covariance methods should be documented (typically in examples or vignettes).*
G4.0 Statistical Software which enables outputs to be written to local files should parse parameters specifying file names to ensure appropriate file suffices are automatically generated where not provided.*
G5.11 Where extended tests require large data sets or other assets, these should be provided for downloading and fetched as part of the testing workflow.
G5.11a When any downloads of additional data necessary for extended tests fail, the tests themselves should not fail, rather be skipped and implicitly succeed with an appropriate diagnostic message.
G5.9 *Noise susceptibility tests** *Packages should test for expected stochastic behaviour, such as through the following conditions:
G5.9a Adding trivial noise (for example, at the scale of .Machine$double.eps) to data does not meaningfully change results
G5.9b Running under different random seeds or initial conditions does not meaningfully change results*
BS1.3a Bayesian Software should document, both in text and examples, how to use the output of previous simulations as starting points of subsequent simulations.*
BS1.4 For Bayesian Software which implements or otherwise enables convergence checkers, documentation should explicitly describe and provide examples of use with and without convergence checkers.
BS1.5 For Bayesian Software which implements or otherwise enables multiple convergence checkers, differences between these should be explicitly tested.*
BS2.10 Issue diagnostic messages when identical seeds are passed to distinct computational chains.
BS2.11 Software which accepts starting values as a vector should provide the parameter with a plural name: for example, “starting_values” and not “starting_value”.*
BS2.8 Enable results of previous runs to be used as starting points for subsequent runs.*
BS4.1 Packages should provide explicit comparisons with external samplers which demonstrate intended advantage of implementation (generally via tests, vignettes, or both).*
BS4.3 Implement or otherwise offer at least one type of convergence checker, and provide a documented reference for that implementation.
BS4.4 Enable computations to be stopped on convergence (although not necessarily by default).
BS4.5 Ensure that appropriate mechanisms are provided for models which do not converge.*
BS4.6 Implement tests to confirm that results with convergence checker are statistically equivalent to results from equivalent fixed number of samples without convergence checking.
BS4.7 Where convergence checkers are themselves parametrised, the effects of such parameters should also be tested. For threshold parameters, for example, lower values should result in longer sequence lengths.*
BS5.4 Where multiple checkers are enabled, Bayesian Software should return details of convergence checker used
BS7.4a The implications of any assumptions on scales on input objects should be explicitly tested in this context; for example that the scales of inputs which do not have means of zero will not be able to be recovered.
RE2.3 Where applicable, Regression Software should enable data to be centred (for example, through converting to zero-mean equivalent values; or to z-scores) or offset (for example, to zero-intercept equivalent values) via additional parameters, with the effects of any such parameters clearly documented and tested.
RE3.2 Ensure that convergence thresholds have sensible default values, demonstrated through explicit documentation.
RE3.3 Allow explicit setting of convergence thresholds, unless reasons against doing so are explicitly documented.*
RE4.10 Model Residuals, including sufficient documentation to enable interpretation of residuals, and to enable users to submit residuals to their own tests.
RE4.11 Goodness-of-fit and other statistics associated such as effect sizes with model coefficients.
RE4.12 Where appropriate, functions used to transform input data, and associated inverse transform functions.*
RE4.14 Where possible, values should also be provided for extrapolation or forecast errors.
RE4.15 Sufficient documentation and/or testing should be provided to demonstrate that forecast errors, confidence intervals, or equivalent values increase with forecast horizons.*
RE4.6 The variance-covariance matrix of the model parameters (via vcov())
RE4.7 Where appropriate, convergence statistics*
RE6.2 The default plot method should produce a plot of the fitted values of the model, with optional visualisation of confidence intervals or equivalent.*
RE6.3 Where a model object is used to generate a forecast (for example, through a predict() method), the default plot method should provide clear visual distinction between modelled (interpolated) and forecast (extrapolated) values.*
RE7.0 Tests with noiseless, exact relationships between predictor (independent) data.
RE7.0a In particular, these tests should confirm ability to reject perfectly noiseless input data.
RE7.1 Tests with noiseless, exact relationships between predictor (independent) and response (dependent) data.
RE7.1a In particular, these tests should confirm that model fitting is at least as fast or (preferably) faster than testing with equivalent noisy data (see RE2.4b).*
RE7.4 Extending directly from RE4.15, where appropriate, tests should demonstrate and confirm that forecast errors, confidence intervals, or equivalent values increase with forecast horizons.