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srrstats tagNumbers of standards:
EA : 24 / 34
G : 47 / 68
PD : 7 / 14
Total : 78 / 116
Standards in on line#94 of file R/srr-stats-standards.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.*
G1.5 Software should include all code necessary to reproduce results which form the basis of performance claims made in associated publications.*
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.*
G2.11 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.*
G2.13 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()).
G2.1a 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.
G2.4 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()
G2.6 Software which accepts one-dimensional input should ensure values are appropriately pre-processed regardless of class structures.*
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.
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.*
G5.1 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.*
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).*
G5.8 *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)
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*
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*
EA2.6 Routines should appropriately process vector data regardless of additional attributes*
EA3.0 The algorithmic components of EDA Software should enable automated extraction and/or reporting of statistics as some sufficiently “meta” level (such as variable or model selection), for which previous or reference implementations require manual intervention.
EA4.0 EDA Software should ensure all return results have types which are consistent with input types.*
EA4.1 EDA Software should implement parameters to enable explicit control of numeric precision
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.
EA5.3 Column-based summary statistics should always indicate the storage.mode, class, or equivalent defining attribute of each column.*
EA5.4 All visualisations should ensure values are rounded sensibly (for example, via pretty() function).
EA6.0 Return values from all functions should be tested, including tests for the following characteristics:
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
EA6.0e Values of single-valued objects; for 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.*
PD1.0 Software should provide references justifying choice and usage of particular probability distributions.*
PD2.0 Where possible, software should represent probability distributions using a package for general representation.*
PD3.0 Manipulation of probability distributions should very generally be analytic, with numeric manipulations only implemented with clear justification (ideally including references).*
PD3.1 Operations on probability distributions should generally be contained within separate functions which themselves accept the names of the distributions as one input parameter.*
PD4.0 The numeric outputs of probability distribution functions should be tested, not just output structures. These tests should generally be tests for numeric equality.*
PD4.1 Tests for numeric equality should compare the output of of probability distribution functions with the output of code which explicitly demonstrates how such values are derived (generally defined in the same location in test files).*
PD4.2 All functions constructed in accordance with PD2.1 - that is, which use a fixed distribution, and which name that distribution as an input parameter - should be tested using at least two different distributions.*
srrstatsNA tagNumbers of standards:
EA : 10 / 34
G : 21 / 68
PD : 7 / 14
Total : 38 / 116
Standards in on line#177 of file R/srr-stats-standards.R:
G1.6 Software should include code necessary to compare performance claims with alternative implementations in other R packages.*
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.*
G2.4c 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
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).*
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.*
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).*
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).
G5.10 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.
G5.2 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.*
G5.7 *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.
G5.9a Adding trivial noise (for example, at the scale of .Machine$double.eps) to data does not meaningfully change results
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.*
EA5.0 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.
EA5.1 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.*
EA6.1 The properties of graphical output from EDA software should be explicitly tested, for example via the vdiffr package or equivalent.*
PD3.2 Use of optimisation routines to estimate parameters from probability distributions should explicitly specify and explain values of all parameters, including all uses of default parameters.
PD3.3 Return objects which include values generated from optimisation algorithms should include information on optimisation algorithm and performance, minimally including the name of the algorithm used, the convergence tolerance, and the number of iterations.*
PD3.4 Use of routines to integrate probability distributions should explicitly document conditions under which integrals are expected to remain stable, and ideally include pre-processing checks for potentially unstable behaviour.
PD3.5 Integration routines should only rely on discrete summation where such use can be justified (for example, through providing a literature reference), in which case the following applies:
PD3.5a Use of discrete summation to approximate integrals must demonstrate that the Reimann sum has a finite limit (or, equivalently, must explicitly describe the conditions under which the sum may be expected to be finite).*
PD4.3 Tests of optimisation or integration algorithms should compare default results with results generated with alternative values for every parameter, including all parameters for the chosen algorithm (whether exposed as function inputs or not).*
PD4.4 Tests of optimisation or integration algorithms should compare equivalent results generated with at least one alternative algorithm.*