Sustainable Catalyst Reproducible Analytics Engine
Catalyst Analytics R
Catalyst Analytics R is the reproducible computational layer for
governed data intake, indicator calculation, comparative scenarios,
uncertainty analysis, sensitivity testing, stress testing,
and climate, carbon, and natural-capital accounting.
The R package preserves inputs, assumptions, units, methods,
seeds, transformations, failures, thresholds, model versions,
analytical boundaries, and export manifests so results can be
inspected, challenged, rerun, and connected to the wider platform.
v0.6.0 — Climate, Carbon, and Natural-Capital Accounting.
Built-in analytical model:
khncpa@1.0.0.Analytical outputs remain decision support, not automatic factual,
causal, financial, scientific, or policy conclusions.
Engine overview
Reproducible analysis from governed data to reviewable output
Catalyst Analytics R combines documented data intake,
versioned indicator definitions, governed scenario execution,
comparative diagnostics, uncertainty and sensitivity analysis,
stress testing, accounting models, and portable analytical bundles.
Ingest
Prepare governed analytical data
Read CSV or JSON data, preserve source and license metadata,
validate fields and units, diagnose quality, and record transformations.
Compute
Calculate versioned indicators
Apply registered formulas with declared units,
required fields, direction, methodology, targets, and calculation traces.
Test
Compare scenarios and uncertainty
Run alternatives, ensembles, thresholds, sensitivity,
shocks, stress cases, trade-offs, rankings, and Pareto diagnostics.
Publish
Export inspectable analysis bundles
Preserve data, assumptions, results, failures,
manifests, checksums, methods, model contracts, and interpretation boundaries.
Live analytical workspace
Explore Catalyst Analytics R in the browser
The public demonstration exposes a mapped browser workflow
for data intake, indicator calculation, scenario comparison,
uncertainty, sensitivity, stress testing, and canonical JSON export.
the browser interface maps package concepts for public exploration.
It should not be interpreted as numerical identity with every R workflow
or as a substitute for package execution, validated source data,
documented methods, or qualified review.
Catalyst Analytics R v2.0.0 / WordPress v3.0.0
Connected Sustainability Analytics and Decision Platform
Map workspaces, evidence, models, decisions, governance, publications, and first-party handoffs into one reviewable analytical graph.
Graph
Connected analytical path
Lineage
Evidence to publication
Registries
Federated records
Platform connections
Governance and handoff
Current capabilities
Eight governed layers of reproducible analytics
The current package brings together the comparative,
uncertainty, data-intake, indicator, and accounting releases
into one inspectable analytical system.
01 · Data intake
Governed datasets and source records
Read and validate CSV or JSON data, preserve publisher,
URL, license, citation, retrieval, field, unit, scope,
currency, missing-data, quality, and transformation metadata.
02 · Indicators
Versioned indicator registry
Register formulas, required fields, units,
direction, aggregation behavior, methodology,
targets, status, and calculation trace.
03 · Models
Declared model contracts
Preserve model identity, semantic version,
parameters, policy assumptions, initial state,
constraints, units, outputs, and interpretation boundaries.
04 · Scenarios
Comparative scenario engine
Execute governed scenario batches and compare
baselines, policies, rankings, scorecards, deltas,
trade-offs, dominance, and Pareto-efficient alternatives.
05 · Uncertainty
Reproducible ensembles and intervals
Use Monte Carlo or Latin hypercube sampling,
declared distributions, reproducible seeds,
failed-run records, intervals, probabilities, and thresholds.
06 · Sensitivity
Global and local sensitivity analysis
Estimate Spearman or Pearson relationships,
perturb parameters, calculate derivatives and elasticities,
and inspect tornado-style analytical summaries.
07 · Stress
Named shocks and stress cases
Apply set, add, or multiply shocks,
combine multi-shock cases, retain baselines,
compare deltas, and preserve stress-test roles and limits.
08 · Accounting
Climate, carbon, and natural capital
Connect emissions, carbon intensity, cumulative budgets,
atmospheric carbon, adjusted savings,
natural-capital stocks, change, restoration, and long-horizon trajectories.
Data and indicators
Documented inputs and versioned calculations
Analytical reproducibility begins before model execution.
Catalyst Analytics R preserves the dataset contract,
source context, quality diagnostics, unit conversions,
transformations, indicator definitions, and calculation trace.
Dataset contract
Stable analytical identity
Dataset IDs and fingerprints exclude volatile timestamps
and machine-local paths so unchanged contracts retain
stable identities across environments.
Quality
Visible diagnostics
Review required fields, missing values,
duplicate rows, duplicate keys, type conflicts,
time ordering, unit compatibility, and declared scope.
Transformation
Recorded analytical changes
Preserve cleaning, normalization, unit conversion,
aggregation, filtering, derivation, assumptions,
software context, and method notes.
Registry
Governed indicator definitions
Use semantic versions, formulas, required inputs,
output units, preferred direction, methodology sources,
targets, aggregation, and lifecycle status.
Built-in indicators
Economic and sustainability measures
GDP, emissions, adjusted net savings,
natural capital, atmospheric carbon, per-capita measures,
carbon intensity, cumulative emissions, and stock change.
Trace
Calculation lineage
Connect each result to the indicator definition,
dataset fields, source record, unit conversions,
transformation history, model version, and export manifest.
Scenario and uncertainty engine
Compare alternatives without hiding assumptions or failures
Catalyst Analytics R records scenario roles,
policy parameters, thresholds, uncertainty distributions,
sampling methods, random seeds, failed realizations,
sensitivity estimates, rankings, trade-offs, and Pareto diagnostics.
Comparison
Baseline and policy alternatives
Compare terminal indicators, absolute and relative deltas,
scorecards, ranks, trade-offs, dominance, and Pareto-efficient scenarios.
Probability
Intervals and threshold likelihood
Report P2.5, P10, median, P90, P97.5,
means, standard deviations, ranges, success counts,
denominators, and direction-aware probabilities.
Sensitivity
Parameter influence and fragility
Identify influential parameters, zero-variance cases,
incomplete samples, derivative behavior, elasticity,
and assumptions that make results unstable.
Stress
Named adverse or boundary cases
Construct transparent shocks, combine stressors,
compare against the retained baseline,
and preserve analytical roles and interpretation limits.
Climate and natural-capital accounting
Connect economic activity, emissions, carbon budgets, and asset change
The v0.6.0 release extends the governed analytical stack
into climate, carbon, inclusive-wealth, and natural-capital accounting
while preserving explicit assumptions and simplified-model boundaries.
Emissions
Flows and intensity
Track emissions trajectories, emissions per capita,
carbon intensity, cumulative emissions,
intervention effects, and declared budget thresholds.
Carbon
Atmospheric and budget state
Preserve atmospheric-carbon state,
budget consumption, remaining budget,
threshold status, scenario deltas, and uncertainty bands.
Natural capital
Stocks, depletion, and restoration
Model natural-capital values and change,
depletion, restoration, reinvestment,
assumptions, units, and long-horizon consequences.
Inclusive wealth
Adjusted savings and capital accounting
Combine economic savings, human and social investment,
environmental loss, restoration,
capital values, and simplified adjusted-savings estimates.
Analytical workflow
From question to reproducible analytical bundle
Each step preserves the information needed to rerun,
compare, inspect, and challenge the final result.
- 01Define
State the question, decision context, scope, indicators, thresholds, and boundaries.
- 02Ingest
Read data and preserve source, license, citation, retrieval, unit, and scope metadata.
- 03Prepare
Validate quality, normalize units, record transformations, and declare missing-data rules.
- 04Register
Select versioned indicators, formulas, methods, targets, and preferred directions.
- 05Model
Declare model version, parameters, policy, initial state, constraints, and outputs.
- 06Analyze
Run scenarios, ensembles, probabilities, sensitivity, stress cases, and accounting outputs.
- 07Interpret
Review failures, quality limits, uncertainty, trade-offs, thresholds, and analytical boundaries.
- 08Export
Create portable data, comparison, uncertainty, stress, manifest, and publication bundles.
Platform connections
How Catalyst Analytics R connects to Sustainable Catalyst
Catalyst Analytics R remains the specialized R engine
while exchanging governed data, assumptions, scenarios,
methods, results, and evidence with connected platform products.
Governed data
Catalyst Data
Receive canonical entities, indicators, periods,
observations, measurements, methods, sources,
provenance, validation states, and evidence chains.
Public context
Site Intelligence
Analyze country, indicator, earth-system,
climate, energy, trade, humanitarian,
geospatial, temporal, and live-event records.
Interactive analysis
Workbench
Exchange equations, variables, units,
calculations, model assumptions, graphs,
parameter studies, validation, and technical reports.
Scientific execution
Research Lab
Connect reproducible experiments, datasets,
calibration, validation, parameter studies,
uncertainty analysis, compute jobs, and provenance.
Decision support
Decision Studio
Send scenarios, rankings, trade-offs,
thresholds, uncertainty, stress results,
evidence, confidence, and interpretation boundaries into Decision Packets.
Sources and publication
Knowledge Library
Connect analytical outputs to sources,
citations, methods, documents, research collections,
publication records, and durable evidence pathways.
Research routing
Research Librarian
Route missing data, source needs,
related research, method questions,
analytical gaps, and product-specific follow-up work.
Strategic design
Catalyst Canvas
Receive hypotheses, criteria, assumptions,
experiment plans, stakeholder questions,
expected signals, and decision-readiness needs.
Shared contracts
Platform Core
Register analytical contracts, model versions,
typed artifacts, APIs, manifests, checksums,
compatibility, provenance, and trust metadata.
Outputs and publication
Portable analytical records, not disconnected charts
Export functions preserve source data, indicator definitions,
calculation traces, scenarios, uncertainty specifications,
sample results, failures, sensitivity, stress cases,
manifests, checksums, and optional archives.
Data
Data-analysis bundles
Source data, dataset manifest, source record,
quality flags, transformations, indicator values,
definitions, calculation trace, and checksums.
Comparison
Scenario-comparison bundles
Scenario contracts, terminal indicators,
deltas, ranks, scorecards, trade-offs,
Pareto results, plots, metadata, and manifests.
Uncertainty
Ensemble and stress bundles
Sample inputs, sample results, intervals,
probabilities, sensitivity, failures,
shocks, cases, comparisons, and declared boundaries.
Review
Machine- and human-readable outputs
CSV, JSON, charts, summaries, manifests,
Markdown briefs, file sizes, MD5 checksums,
and optional ZIP archives.
Reproducibility and governance
Analytical state remains explicit and reviewable
Catalyst Analytics R treats assumptions, seeds,
failures, source quality, unit compatibility,
model boundaries, package versions, and interpretation limits
as part of the analytical record.
Identity
Versioned package, model, and contracts
Preserve package version, model identity,
scenario, comparison, uncertainty, stress,
dataset, indicator, and export contract versions.
Randomness
Reproducible seeds and samples
Record integer seeds, sampling method,
distribution definitions, sample inputs,
caller-state restoration, and failed realizations.
Quality
Warnings and failures remain visible
Preserve missing data, unit concerns,
zero variance, incomplete samples,
failed runs, fragile assumptions, and review requirements.
Release
Package and repository validation
Use package tests, R CMD build and check,
schema validation, static contracts,
syntax checks, fixtures, CI, ZIP integrity, and release manifests.
Boundaries
Reproducible analytics, not automatic truth or decision authority
Catalyst Analytics R improves transparency and repeatability.
It does not determine which question should be asked,
guarantee data quality, prove causality,
select policy, or replace qualified judgment.
Not automatic truth
Reproducibility does not prove correctness
A calculation can be rerunnable and still depend
on incomplete, biased, outdated, disputed,
weakly measured, or incorrectly interpreted inputs.
Not causal proof
Scenarios and correlations remain bounded
Comparisons, sensitivity relationships,
simulations, and accounting outputs do not
by themselves establish causal effects.
Not a decision
Rankings and Pareto sets remain advisory
Scores, thresholds, dominance, probabilities,
stress results, and trade-offs expose analytical structure
but do not make the final choice.
Not full earth-system prediction
Simplified accounting models have limits
Climate, carbon, inclusive-wealth,
and natural-capital models require explicit interpretation
and should not be presented as complete physical forecasts.
Not unrestricted data use
Source rights and privacy still apply
Public, licensed, private, institutional,
proprietary, regulated, and personal data
retain their own access, reuse, security, and retention constraints.
Not professional substitution
Qualified review may be required
Financial, legal, scientific, engineering,
environmental, safety-critical, regulated,
and assurance uses require appropriate expertise.
Next step
Use Catalyst Analytics R to make analytical work inspectable
Begin with governed data and indicators,
declare assumptions and model versions,
compare scenarios and uncertainty,
preserve failures and boundaries,
and export the complete analytical trail.