Click here for full text of all standards
srrstats tagNumbers of standards:
EA : 31 / 34
G : 50 / 68
Total : 81 / 102
Standards on line#10 of file R/additional_divergence_measures.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.
G2.2 Appropriately prohibit or restrict submission of multivariate input to parameters expected to be univariate.
Standards on line#28 of file R/additional_divergence_measures.R:
Standards on line#96 of file R/additional_divergence_measures.R:
Standards on line#165 of file R/additional_divergence_measures.R:
Standards on line#214 of file R/additional_divergence_measures.R:
Standards on line#286 of file R/additional_divergence_measures.R:
Standards on line#322 of file R/additional_divergence_measures.R:
Standards on line#45 of file R/cjs.R:
G2.0a Provide explicit secondary documentation of any expectations on lengths of inputs
G2.13 Statistical Software should implement appropriate checks for missing data as part of initial pre-processing prior to passing data to analytic algorithms.
G2.16 All functions should also provide options to handle
undefined values (e.g., NaN, Inf and
-Inf), including potentially ignoring or removing such
values.*
G2.2 Appropriately prohibit or restrict submission of multivariate input to parameters expected to be univariate.
G2.6 Software which accepts one-dimensional input should ensure values are appropriately pre-processed regardless of class structures.*
Standards on line#39 of file R/create_priorsense_data.R:
G2.1 Implement assertions on types of inputs (see the initial point on nomenclature above).
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
G2.4 Provide appropriate mechanisms to convert between different data types, potentially including:
G2.4b explicit conversion to continuous via
as.numeric()
G2.4c explicit conversion to character via
as.character() (and not paste or
paste0)
G2.4e explicit conversion from factor via as...()
functions
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.
Standards on line#15 of file R/example_powerscale_model.R:
Standards on line#9 of file R/find_alpha_threshold.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.*
EA4.1 EDA Software should implement parameters to enable explicit control of numeric precision
Standards on line#32 of file R/plots.R:
EA5.0 Graphical presentation in EDA software should be as accessible as possible or practicable. In particular, EDA software should consider accessibility in terms of:
EA5.0a Typeface sizes, which should default to sizes which explicitly enhance accessibility
EA5.0b Default colour schemes, which should be carefully constructed to ensure accessibility.
EA5.4 All visualisations should ensure values are rounded
sensibly (for example, via pretty() function).
Standards on line#40 of file R/powerscale.R:
G2.0 Implement assertions on lengths of inputs, particularly through asserting that inputs expected to be single- or multi-valued are indeed so.
G2.1 Implement assertions on types of inputs (see the initial point on nomenclature above).
Standards on line#91 of file R/powerscale.R:
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.
G2.4 Provide appropriate mechanisms to convert between different data types, potentially including:
G2.4b explicit conversion to continuous via
as.numeric()
G2.4c explicit conversion to character via
as.character() (and not paste or
paste0)
G2.4e explicit conversion from factor via as...()
functions
EA2.6 Routines should appropriately process vector data regardless of additional attributes*
Standards on line#34 of file R/powerscale_sensitivity.R:
Standards on line#4 of file R/print.R:
EA4.2 The primary routines of EDA Software should return objects
for which default print and plot methods give
sensible results. Default summary methods may also be
implemented.*
EA5.2 Screen-based output should never rely on default print
formatting of numeric types, rather should also use some
version of round(., digits), formatC,
sprintf, or similar functions for numeric formatting
according the parameter described in* EA4.1.
Standards on line#70 of file R/priorsense-package.R:
G1.0 Statistical Software should list at least one primary reference from published academic literature.*
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.
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.*
G2.10 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.*
EA1.0 Identify one or more target audiences for whom the software is intended
EA1.1 Identify the kinds of data the software is capable of analysing (see Kinds of Data below).
EA1.2 Identify the kinds of questions the software is intended to help explore.*
EA1.3 Identify the kinds of data each function is intended to accept as input*
EA2.0 EDA Software which accepts standard tabular data and implements or relies upon extensive table filter and join operations should utilise an index column system
EA2.1 All values in an index column must be unique, and this uniqueness should be affirmed as a pre-processing step for all input data.
EA2.2 Index columns should be explicitly identified, either:
EA2.2a by using an appropriate class system, or
EA2.2b through setting an attribute on a table,
x, of
attr(x, "index") <- <index_col_name>.*
EA2.3 Table join operations should not be based on any assumed variable or column names*
EA2.4 Use and demand an explicit class system for such input (for
example, via the DM
package).
EA2.5 Ensure all individual tables follow the above standards for Index Columns*
EA3.1 EDA software should enable standardised comparison of inputs, processes, models, or outputs which previous or reference implementations otherwise only enable in some comparably unstandardised form.*
EA5.3 Column-based summary statistics should always indicate the
storage.mode, class, or equivalent defining
attribute of each column.*
Standards on line#111 of file R/summarise_draws.R:
print and plot methods give
sensible results. Default summary methods may also be
implemented.*Standards on line#19 of file R/weighted_quantities.R:
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
G2.15 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()).
G2.6 Software which accepts one-dimensional input should ensure values are appropriately pre-processed regardless of class structures.*
Standards on line#8 of file R/whiten_draws.R:
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).*
Standards on line#14 of file tests/testthat/test_cjs.R:
numeric
values either using testthat::expect_equal() or equivalent
with a defined value for the tolerance parameter, or using
round(..., digits = x) with some defined value of
x prior to testing equality.*Standards on line#34 of file tests/testthat/test_cjs.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:
G5.8b Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)
Standards on line#25 of file tests/testthat/test_conjugate.R:
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.
G5.5 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.
G5.7 *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.
Standards on line#82 of file tests/testthat/test_conjugate.R:
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 G5.10-4.12, below).*
G5.9 *Noise susceptibility tests** *Packages should test for expected stochastic behaviour, such as through the following conditions:
G5.9b Running under different random seeds or initial conditions does not meaningfully change results*
Standards on line#38 of file tests/testthat/test_deriv.R:
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.
Standards on line#114 of file tests/testthat/test_plots.R:
vdiffr package
or equivalent.*Standards on line#3 of file tests/testthat/test_powerscale.R:
Standards on line#6 of file tests/testthat/test_powerscale.R:
Standards on line#18 of file tests/testthat/test_powerscale.R:
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.*
EA4.0 EDA Software should ensure all return results have types which are consistent with input types.*
Standards on line#46 of file tests/testthat/test_powerscale.R:
Standards on line#60 of file tests/testthat/test_powerscale.R:
EA6.0a Classes and types of objects
EA6.0b Dimensions of tabular objects
EA6.0c Column names (or equivalent) of tabular objects
EA6.0d Classes or types of all columns contained within
data.frame-type tabular objects
Standards on line#205 of file tests/testthat/test_powerscale.R:
.Machine$double.eps) to data does not meaningfully change
resultsStandards on line#219 of file tests/testthat/test_powerscale.R:
G5.2 Appropriate error and warning behaviour of all functions should be explicitly demonstrated through tests. In particular,
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:
G5.8d Data outside the scope of the algorithm (for example, data with more fields (columns) than observations (rows) for some regression algorithms)
Standards on line#227 of file tests/testthat/test_powerscale.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
Standards on line#239 of file tests/testthat/test_powerscale.R:
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.
G5.8b Data of unsupported types (e.g., character or complex numbers in for functions designed only for numeric data)
Standards on line#269 of file tests/testthat/test_powerscale.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:
G5.8c Data with all-NA fields or columns or all
identical fields or columns
Standards on line#3 of file tests/testthat/test_rstan.R:
srrstatsNA tagNumbers of standards:
EA : 3 / 34
G : 18 / 68
Total : 21 / 102
Standards on line#57 of file R/srr-stats-standards.R:
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.*
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.*
G2.14b ignore missing data with default warnings or messages issued
G2.14c replace missing data with appropriately imputed values
G2.4a explicit conversion to integer via
as.integer()
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.*
G2.9 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).*
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.*
G5.10 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:
G5.11 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.
G5.12 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.
G5.4 *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).
G5.4b 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
EA5.1 Any explicit specifications of typefaces which override
default values provided through other packages (including the
graphics package) should consider accessibility*
EA5.5 All visualisations should include units on all axes where such are specified or otherwise obtainable from input data or other routines.*
EA5.6 Any packages which internally bundle libraries used for dynamic visualization and which are also bundled in other, pre-existing R packages, should explain the necessity and advantage of re-bundling that library.*
EA5.6 Any packages which internally bundle libraries used for dynamic visualization and which are also bundled in other, pre-existing R packages, should explain the necessity and advantage of re-bundling that library.*