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❌ Error: Package documents compliance only with general standards.Statistical packages must document compliance with at least one set of category-specific standards as well.
srrstats tagNumbers of standards:
G : 68 / 68
Total : 68 / 68
Standards in function ‘anova.galamm()’ on line#45 of file R/anova.galamm.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘nobs.galamm()’ on line#133 of file R/anova.galamm.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘coef.galamm()’ on line#31 of file R/coef.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘confint.galamm()’ on line#42 of file R/confint.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
G2.3 For univariate character input:
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.
Standards in function ‘epilep()’ on line#25 of file R/data.R:
Standards in function ‘mresp()’ on line#45 of file R/data.R:
Standards in function ‘mresp_hsced()’ on line#70 of file R/data.R:
Standards in function ‘diet()’ on line#103 of file R/data.R:
Standards in function ‘hsced()’ on line#122 of file R/data.R:
Standards in function ‘cognition()’ on line#150 of file R/data.R:
Standards in function ‘latent_covariates()’ on line#175 of file R/data.R:
Standards in function ‘latent_covariates_long()’ on line#202 of file R/data.R:
Standards in function ‘extract_name()’ on line#17 of file R/define_factor_mappings.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘squeeze_mappings()’ on line#56 of file R/define_factor_mappings.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘mappingunwrapping()’ on line#121 of file R/define_factor_mappings.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘extend_lambda()’ on line#155 of file R/define_factor_mappings.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘factor_finder()’ on line#196 of file R/define_factor_mappings.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘define_factor_mappings()’ on line#244 of file R/define_factor_mappings.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘extract_optim_parameters.galamm()’ on line#50 of file R/extract_optim_parameters.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘factor_loadings.galamm()’ on line#56 of file R/factor_loadings.R:
G1.4 Software should use roxygen2 to document all functions.
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:
G2.4c explicit conversion to character via as.character() (and not paste or paste0)
Standards in function ‘family.galamm()’ on line#39 of file R/family.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘fitted.galamm()’ on line#29 of file R/fitted.galamm.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘fixef.galamm()’ on line#38 of file R/fixef.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘formula.galamm()’ on line#39 of file R/formula.R:
roxygen2 to document all functions.Standards in function ‘galamm_control()’ on line#71 of file R/galamm_control.R:
G1.4 Software should use roxygen2 to document all functions.
G2.3 For univariate character input:
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.
Standards in function ‘new_galamm_control()’ on line#148 of file R/galamm_control.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘galamm()’ on line#309 of file R/galamm.R:
G1.0 Statistical Software should list at least one primary reference from published academic literature.*
G1.3 All statistical terminology should be clarified and unambiguously defined.*
G1.4 Software should use roxygen2 to document all functions.
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.*
G2.13 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
G2.15 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.*
G2.1a 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:
G2.3 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:
G2.4 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.*
Standards in function ‘gamm4.setup()’ on line#24 of file R/gamm4-functions.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘gamm4()’ on line#123 of file R/gamm4-functions.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘gamm4.wrapup()’ on line#233 of file R/gamm4-functions.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘logLik.galamm()’ on line#26 of file R/logLik.galamm.R:
roxygen2 to document all functions.Standards in function ‘deviance.galamm()’ on line#64 of file R/logLik.galamm.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘gam.setup()’ on line#22 of file R/mgcv-functions.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘variable.summary()’ on line#266 of file R/mgcv-functions.R:
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.*
G2.4 Provide appropriate mechanisms to convert between different data types, potentially including:
G2.4d explicit conversion to factor via as.factor()
Standards in function ‘gam.side()’ on line#327 of file R/mgcv-functions.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘interpret.gam0()’ on line#364 of file R/mgcv-functions.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘setup_factor()’ on line#30 of file R/misc.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘setup_family()’ on line#100 of file R/misc.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘setup_response_object()’ on line#127 of file R/misc.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘find_k()’ on line#189 of file R/misc.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘set_initial_values()’ on line#222 of file R/misc.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘release_questions()’ on line#273 of file R/misc.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘skip_extended()’ on line#289 of file R/misc.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in function ‘plot_smooth.galamm()’ on line#64 of file R/plot_smooth.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘plot.galamm()’ on line#25 of file R/plot.galamm.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘predict.galamm()’ on line#50 of file R/predict.galamm.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
G2.3 For univariate character input:
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.
Standards in function ‘ranef.galamm()’ on line#42 of file R/ranef.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘residuals.galamm()’ on line#35 of file R/residuals.galamm.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
G2.3 For univariate character input:
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.
Standards in function ‘sigma.galamm()’ on line#41 of file R/sigma.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘sl()’ on line#78 of file R/smooths.R:
G1.4 Software should use roxygen2 to document all functions.
G2.3 For univariate character input:
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.
G2.5 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.*
Standards in function ‘t2l()’ on line#149 of file R/smooths.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
G2.3 For univariate character input:
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.
G2.5 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.*
Standards in on line#51 of file R/srr-stats-standards.R:
G1.2 Statistical Software should include a* Life Cycle Statement describing current and anticipated future states of development.
G1.5 Software should include all code necessary to reproduce results which form the basis of performance claims made in associated publications.*
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
G2.4 Provide appropriate mechanisms to convert between different data types, potentially including:
G2.4e explicit conversion from factor via as...() functions
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.
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).*
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.
G5.4c Where applicable, stored values may be drawn from published paper outputs when applicable and where code from original implementations is not available
Standards in function ‘summary.galamm()’ on line#62 of file R/summary.galamm.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘print.summary.galamm()’ on line#142 of file R/summary.galamm.R:
roxygen2 to document all functions.Standards in function ‘llikAIC()’ on line#200 of file R/summary.galamm.R:
roxygen2 to document all functions.Standards in function ‘VarCorr.galamm()’ on line#40 of file R/VarCorr.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
Standards in function ‘print.VarCorr.galamm()’ on line#101 of file R/VarCorr.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
G2.3 For univariate character input:
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.
Standards in function ‘vcov.galamm()’ on line#37 of file R/vcov.R:
G1.4 Software should use roxygen2 to document all functions.
G2.1a Provide explicit secondary documentation of expectations on data types of all vector inputs.
G2.3 For univariate character input:
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.
Standards in function ‘find_parm_inds()’ on line#89 of file R/vcov.R:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in on line#28 of file tests/testthat/testthat.R:
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.*
G5.4a 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.
G5.5 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.
Standards in on line#7 of file tests/testthat/test-galamm-glmm.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).
Standards in on line#122 of file tests/testthat/test-galamm-glmm.R:
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 in on line#7 of file tests/testthat/test-galamm-lmm.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).
Standards in on line#449 of file tests/testthat/test-galamm-lmm.R:
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*
Standards in on line#4 of file tests/testthat/test-galamm-semiparametric.R:
Standards in on line#15 of file tests/testthat/test-galamm-setup.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).
G5.8 *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#8 of file tests/testthat/test-parameter-recovery.R:
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.
Standards in function ‘marginal_likelihood(()’ on line#324 of file src/compute_galamm.cpp:
roxygen2 format, along with a final @noRd tag to suppress automatic generation of .Rd files.*Standards in on line#34 of file ./README.Rmd:
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.
G1.3 All statistical terminology should be clarified and unambiguously defined.*
Standards in on line#54 of file ./README.Rmd:
Standards in on line#65 of file ./README.Rmd: