The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
gp3ml distinguishes the public API established by
version 0.2.0 from new development APIs. Stable exported names and
registered public S3 classes should not silently disappear or change
meaning within the 0.2.x line.
registry <- gp3ml_api_contracts()
registry
#> gp3ml API contract registry: 71 stable exports, 56 experimental exports, 38 stable public classes
head(registry$exports)
#> name stability present
#> 1 apply_gazepoint_calibrator stable TRUE
#> 2 apply_gazepoint_decision_rule experimental TRUE
#> 3 as_gp3ml_data experimental TRUE
#> 4 assert_gp3ml_engine_available experimental TRUE
#> 5 assert_gp3ml_use_case stable TRUE
#> 6 assess_gazepoint_calibration stable TRUE
registry$policy
#> contract
#> 1 exported_function_name
#> 2 public_s3_class
#> 3 function_formals
#> 4 named_return_components
#> stable_rule
#> 1 No removal or rename within the 0.3.x line.
#> 2 No removal or rename within the 0.3.x line.
#> 3 Existing arguments retain meaning; new arguments require defaults.
#> 4 Existing named components retain meaning; additive components are allowed.audit <- audit_gp3ml_api_stability(registry)
audit
#> gp3ml API stability audit: pass
#> check status n_issues
#> stable_exports_present pass 0
#> declared_exports_only pass 0
#> stable_public_classes_present pass 0
plot(audit)
A failure indicates removal of an established export or registered public class. An undeclared export is reviewable rather than silently accepted.
example_data <- data.frame(
participant_id = rep(sprintf("P%02d", 1:8), each = 2),
trial_id = sprintf("T%02d", 1:16),
stimulus_id = rep(c("S01", "S02"), 8),
assigned_condition = rep(c("A", "B"), 8),
stringsAsFactors = FALSE
)
task <- declare_gazepoint_task(
data = example_data,
outcome = "assigned_condition",
purpose = "Discriminate an experimentally assigned condition using predeclared observed variables",
task_type = "classification",
unit_id = "trial_id",
participant_id = "participant_id",
stimulus_id = "stimulus_id",
generalization_target = "new_participants",
positive = "B",
observed_outcome = TRUE,
sensitive_outcome = FALSE
)
validation <- validate_gp3ml_object_contract(task)
validation
#> gp3ml object contract: pass
#> check status
#> registered_public_class pass
#> named_components pass
#> schema_observable pass
#> detail
#> gp3ml_task
#> Named components are structurally valid.
#> Schema can be represented by gp3ml_object_schema().
validation$schema
#> component class typeof length nrow ncol
#> 1 outcome character character 1 NA NA
#> 2 purpose character character 1 NA NA
#> 3 task_type character character 1 NA NA
#> 4 unit_id character character 1 NA NA
#> 5 participant_id character character 1 NA NA
#> 6 stimulus_id character character 1 NA NA
#> 7 generalization_target character character 1 NA NA
#> 8 positive character character 1 NA NA
#> 9 observed_outcome logical logical 1 NA NA
#> 10 sensitive_outcome logical logical 1 NA NA
#> 11 created_at character character 1 NA NA
#> 12 levels character character 2 NA NA
#> 13 negative character character 1 NA NAThe contract policy is additive: established named components retain their meaning, while compatible additions remain possible.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.