# srr report for demo

## Standards with srrstats tag

### R directory

Standards in on line#128 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.

• G1.5 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()).

• G2.16 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:

• G2.3a 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()

• G2.4c 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.*

• G2.6 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.

• 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.*

• G5.0 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).

• G5.1 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.*

• G5.10 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=1 environment variable.

• 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.*

• G5.4 *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.

• 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:

• G5.9a 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*

• RE1.0 Regression Software should enable models to be specified via a formula interface, unless reasons for not doing so are explicitly documented.

• RE1.1 Regression Software should document how formula interfaces are converted to matrix representations of 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.*

• 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.*

• 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).*

• 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.

• 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.

• 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*

• 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.

• 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.*

• RE4.0 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.*

• RE4.10 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.*

• RE4.13 Predictor variables, and associated “metadata” where applicable.*

• 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.*

• RE4.16 Regression Software which models distinct responses for different categorical groups should include the ability to submit new groups to predict() methods.*

• 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.

• 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).*

• RE4.2 Model coefficients (via coeff() / coefficients())

• RE4.3 Confidence intervals on those coefficients (via confint())

• RE4.4 The specification of the model, generally as a formula (via formula())

• RE4.5 Numbers of observations submitted to model (via nobs())

• RE4.6 The variance-covariance matrix of the model parameters (via vcov())

• RE4.7 Where appropriate, convergence statistics*

• RE4.8 Response variables, and associated “metadata” where applicable.

• RE4.9 Modelled values of response variables.

• RE5.0 Scaling relationships between sizes of input data (numbers of observations, with potential extension to numbers of variables/columns) and speed of algorithm.*

• 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.

• 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).*

• RE7.2 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 RE4.2RE4.7.

• 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.

Standards in function ‘test_fn()’ on line#10 of file R/test.R:

• 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.*

• 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).

• RE1.1 Regression Software should document how formula interfaces are converted to matrix representations of input data.*

### tests directory

Standards in on line#2 of file tests/testthat/test-a.R:

• 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.

### src directory

Standards in function ‘test()’ on line#6 of file src/cpptest.cpp:

### root directory

Standards in on line#17 of file ./README.Rmd:

• G1.4 Software should use roxygen2 to document all functions.

• G1.5 Software should include all code necessary to reproduce results which form the basis of performance claims made in associated publications.*

## Standards with srrstatsNA tag

### R directory

Standards in on line#139 of file R/srr-stats-standards.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.*

• G1.6 Software should include code necessary to compare performance claims with alternative implementations in other R packages.*

## Missing Standards

The following standards are missing:

General standards:

G1.4a, G2.0a, G2.1a, G2.3a, G2.3b, G2.4a, G2.4b, G2.4c, G2.4d, G2.4e, G2.14a, G2.14b, G2.14c, G3.1a, G5.2a, G5.2b, G5.4a, G5.4b, G5.4c, G5.6a, G5.6b, G5.8a, G5.8b, G5.8c, G5.8d, G5.9a, G5.9b, G5.11a

Regression standards:

RE1.3a, RE2.4a, RE2.4b, RE7.0a, RE7.1a