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hanalyze-viz: Vega-Lite visualization and HTML report layer of hanalyze

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The visualization and reporting layer of the hanalyze toolkit. It turns analysis results into Vega-Lite specs and emits them either as single figures (HTML PNG SVG) -- scatter plots with regression, smoother and confidence bands, bar charts, histograms with theoretical densities, MCMC diagnostics (trace, posterior density, autocorrelation, forest, energy), Gaussian-process bands, Pareto fronts and model DAGs in Mermaid or Graphviz form -- or as composed HTML reports built from ReportSection values, covering data overviews, coefficients, residuals, MCMC diagnostics, model comparison and interactive prediction. . Module names match the umbrella package hanalyze, which re-exports everything, so downstream imports stay identical. For static SVG PDF PNG rendering see hanalyze-plot instead. See README.md for the module map and a standalone usage example.

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Versions [RSS] 0.2.0.1
Dependencies aeson (>=2.0 && <2.3), base (>=4.14 && <5), bytestring (>=0.11 && <0.13), containers (>=0.6 && <0.8), dataframe-core (>=1.1 && <1.2), dataframe-operations (>=1.1.1 && <1.2), filepath (>=1.4 && <1.6), hanalyze-bayes (==0.2.0.1), hanalyze-core (==0.2.0.1), hanalyze-design (==0.2.0.1), hanalyze-frame (==0.2.0.1), hanalyze-models (==0.2.0.1), hmatrix (>=0.20 && <0.22), hvega (>=0.12 && <0.13), process (>=1.6 && <1.8), temporary (>=1.3 && <1.4), text (>=1.2 && <2.2), vector (>=0.12 && <0.14) [details]
Tested with ghc ==9.6.7
License BSD-3-Clause
Copyright 2026 Aelysce Project (Toshiaki Honda)
Author Toshiaki Honda
Maintainer frenzieddoll@gmail.com
Uploaded by frenzieddoll at 2026-08-13T05:54:48Z
Category Math, Statistics, Numeric, Machine Learning
Distributions
Reverse Dependencies 1 direct, 2 indirect [details]
Downloads 1 total (1 in the last 30 days)
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Status Docs available [build log]
Last success reported on 2026-08-13 [all 1 reports]

Readme for hanalyze-viz-0.2.0.1

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hanalyze-viz

The visualization / reporting layer of hanalyze. It turns analysis results into Vega-Lite specs and emits them either as single figures (HTML / PNG / SVG) or as composed HTML reports — 19 modules in total.

It depends on all five lower layers (core / frame / bayes / models / design) plus 13 external packages (hvega, aeson, dataframe-core, …). It is the top of the six split layers; the umbrella package sits above it. The numerics behind a figure (regression fits, MCMC diagnostics, Pareto fronts, …) are left to the lower layers — this layer only does spec construction and HTML output.

If you want static images (SVG / PDF / PNG) drawn directly rather than Vega-Lite, use the sibling package hanalyze-plot (toPlot / Plottable). The two are complementary, not exclusive.

Main modules (all 19)

Shared infrastructure

Module Role
Viz.Core I/O helpers shared by every Viz.* module — writeSpec / openInBrowser / vlJson, and OutputFormat = HTML | PNG | SVG
Viz.PlotConfig Plot settings shared by every Viz.* module (PlotConfig / defaultConfig)
Viz.PlotData Source-agnostic intermediate representation of plot data (PlotData / ToPlotData)
Viz.PlotData.DataFrame ToPlotData instances for dataframe (numeric / text columns → PlotData)
Viz.Assets Generated module bundling the Vega / Vega-Lite / Vega-Embed JS for offline HTML

Single figures

Module Role
Viz.Scatter Scatter plots and overlays — regression line / smoother / CI band, grouping, predicted vs actual
Viz.Bar Bar charts (vertical / horizontal / stacked / grouped)
Viz.Histogram Histograms, optionally overlaid with a theoretical density
Viz.MCMC MCMC diagnostic plots — trace / posterior density / autocorrelation / forest / energy
Viz.GP Gaussian-process regression plots (training data, posterior mean, credible band)
Viz.Pareto Pareto fronts — scatter / pairs / parallel coordinates / hypervolume history / comparison
Viz.ModelGraph Model DAG rendered with Mermaid.js
Viz.ModelGraphDot Model DAG as Graphviz DOT (plate notation, equivalent to PyMC's model_to_graphviz)

HTML reports

Module Role
Viz.ReportBuilder The recommended builder — hand a list of ReportSections to renderReport
Viz.ReportInstances Reportable instances for the various fit-result types
Viz.Report Combined MCMC report (DAG, posterior summary table, diagnostics, pairs plot)
Viz.GPReport Combined GP report (data overview, model comparison, interactive prediction, appendix)
Viz.Taguchi Taguchi-method report (S/N ratios, main effects, optimal levels)
⚠ Viz.AnalysisReport Deprecated. The sum-type report dedicated to LM / GLM / GLMM / GP / HBM (~2000 lines). Superseded by Viz.ReportBuilder; kept only for compatibility with the existing CLI (hanalyze regress --report) and slated for removal

Using it standalone

build-depends: hanalyze-viz, hanalyze-frame

{-# LANGUAGE OverloadedStrings #-}
import Hanalyze.DataIO.CSV       (loadAuto)
import Hanalyze.Viz.Core         (OutputFormat (HTML))
import Hanalyze.Viz.PlotConfig   (defaultConfig)
import Hanalyze.Viz.Scatter      (scatterPlotFile)
import Hanalyze.Viz.ReportBuilder

main :: IO ()
main = do
  Right df <- loadAuto "flights.csv"
  -- a single figure, written straight to HTML
  scatterPlotFile HTML "scatter.html"
    (defaultConfig "dep_delay vs month") df "month" "dep_delay"
  -- sections composed into one HTML report
  renderReport "report.html" (defaultReportConfig "Flight delays")
    [ secDataOverview df ["month"] "dep_delay"
    , secModelOverview "Linear regression" "dep_delay = b0 + b1 * month" Nothing
    , secCoefficients [("b0", 1.2), ("b1", 2.4)] (Just ("R2", 0.96))
    ]

The *File functions (scatterPlotFile, histogramPlotFile, …) take an OutputFormat and write the file for you. If you only want the spec, use the *Plot functions (scatterPlot :: PlotConfig -> DataFrame -> Text -> Text -> VegaLite) and emit it later with writeSpec / vlJson from Viz.Core.

Besides secDataOverview / secCoefficients / secFitScatter / secResiduals, ReportBuilder ships section builders for MCMC diagnostics (secMCMCDiagnostics, secPosteriorSummary, secForestPlot), model comparison (secComparisonTable) and interactive prediction (secInteractiveLM, …).

Normally you just depend on the umbrella package hanalyze, where import Hanalyze already gives you the common plotting functions. Depend on a layer directly only when you want to minimise your dependency footprint.

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