Package: SSPICE 0.1.0

Pierpaolo Massoli

SSPICE: Systematic Skip-Pattern Integrity and Consistency Engine

Provides a graph-based framework for representing, analyzing, and validating structured statistical data governed by explicit logical dependencies, including questionnaire routing and other rule-based data structures. It supports the construction and structural analysis of directed acyclic graphs, preparation of record-level data, detection and localization of logical and skip-pattern inconsistencies, and generation of variable domains and formal compatibility rules. It also provides tools for evaluating empirical closure and minimality properties of the resulting Horn theory and for supporting the correction of inconsistent statistical records.

Authors:Pierpaolo Massoli [aut, cre]

SSPICE_0.1.0.tar.gz
SSPICE_0.1.0.zip(r-4.7-any)SSPICE_0.1.0.zip(r-4.6-any)
SSPICE_0.1.0.tar.gz(r-4.7-any)SSPICE_0.1.0.tar.gz(r-4.6-any)
SSPICE_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SSPICE/json (API)

# Install 'SSPICE' in R:
install.packages('SSPICE', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.65 score 13 exports 40 dependencies

Last updated from:7e6cab926e. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK154
source / vignettesOK196
linux-releaseOK125
windows-develOK195
windows-releaseOK233
wasm-releaseOK113

Exports:build_DAGcached_routing_checkcalculate_hitscorrect_datacorrection_controlcorrection_quality_reportcount_admissible_patternsfind_isolated_nodesfind_longest_pathprepare_datarouting_checkvalidate_extended_elogvalidate_horn_theory

Dependencies:askpassbitbit64clicliprcodetoolscpp11crayoncurldoParalleldplyrforeachgenericsgluegmphmsiteratorsjsonlitelifecyclemagrittrpdftoolspillarpkgconfigprettyunitsprogressqpdfR6Rcppreadrrlangstringistringrsystibbletidyselecttzdbutf8vctrsvroomwithr

01 — SSPICE overview and workflow
Why SSPICE | Operational workflow | Reading order | Standalone first run
Rendered frompanoramica-01.Rmdusingknitr::rmarkdown

Last update: 2026-11-13
Started: 2026-11-13

02 — Metadata and external sources
Input files | Exogenous auxiliary variables and the observation unit | Defining auxiliary-variable categories | Purely arithmetic rules | Mixed rules | Application example: project auxiliary variables | Dedicated routing guide | Consistency of external sources | Defining semantic edits: logical_edit_rules.csv | Item mapping and semantic predicates
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Last update: 2026-11-13
Started: 2026-11-13

02b — Building the routing file
Scope and reference | Physical format and row order | Field dictionary | Variable types | Qualitative variables and nonconsecutive codes | Quantitative variables | Strings, leading zeros and identifiers | Continuation, conditional jumps and unconditional jumps | No jump: V6 = 0 | Conditional jump | UNCONDITIONAL (absolute) jump: V6 = 98 | Historical V6 = -1 convention | Logical groups | OR: at least one selected member | XOR: exactly one of two alternatives | Standalone construction examples | Checks before using routing metadata | From questionnaire instructions to routing metadata | Conditional and unconditional jumps | A multiple-response question represented by an OR group | OR groups and single-choice questions
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Last update: 2026-11-13
Started: 2026-11-13

02c — Item mapping: observed codes and routing codes
Purpose and place in the workflow | Three representations of the same category | Which codes to use where | A jump, six records, and a correction | VAUX and LEDIT retain the meaning of the original values | The bundled FASE variable | Supplying the file consistently | Validation and common mistakes
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Last update: 2026-11-13
Started: 2026-11-13

03 — Building and analysing the DAG
From routing metadata to a graph | What to inspect | Checking the meaning of the graph
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Last update: 2026-11-13
Started: 2026-11-13

04 — Data preparation, routing checks and localization
Preparing and checking data | Validating localization | Representation of prepared data | Reading the check object
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Last update: 2026-11-13
Started: 2026-11-13

05 — Domains, rules and Horn theory
Formal representation | Validating the theory | Domains and representation limits | Mapping at export
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Last update: 2026-11-13
Started: 2026-11-13

06 — Record correction
Extended DAG and initial partition | Configuring a pilot | Correction with mapped categories | Outputs and diagnosis | Full runs, batches and workers | Territorial stratum codes | Preparation before the first batch | Controls and returned objects
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Last update: 2026-11-13
Started: 2026-11-13

07 — Correction reports and quality assessment
Why assess the result? | Reading the report | Selecting the result to assess | Interpreting the counts | Denominator and post-hoc uncertainty
Rendered fromqualita-correzione-07.Rmdusingknitr::rmarkdown

Last update: 2026-11-13
Started: 2026-11-13