---
title: "Categorized Function List"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Function List}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

DSLD supports two related tasks: estimating differences between sensitive groups
and limiting sensitive-attribute influence in predictive models.

| Area | Functions |
| --- | --- |
| Adjusted group comparisons | `dsldLinear`, `dsldLogit`, `dsldML` |
| Confounder and proxy exploration | `dsldCHunting`, `dsldOHunting`, `dsldTakeALookAround` |
| Group frequency and density summaries | `dsldFrequencyByS`, `dsldDensityByS`, `dsldConfounders` |
| Multivariate graphics | `dsldFreqPCoord`, `dsldScatterPlot3D`, `dsldConditDisparity` |
| Matching and structure discovery | `dsldMatchedATE`, `dsldIamb` |
| fairml models | `dsldFrrm`, `dsldFgrrm`, `dsldNclm`, `dsldZlm`, `dsldZlrm` |
| Explicitly deweighted features | `dsldQeFairKNN`, `dsldQeFairRF`, `dsldQeFairRidgeLin`, `dsldQeFairRidgeLog` |
| Cross-validated parameter comparison | `dsldFairUtils` |

Use `summary()` for the linear/logistic group comparisons and `predict()` for
new data. Each function's help page describes its arguments and return values.

The fair-ML fields `trainAcc` and `testAcc` are prediction errors, despite their
historical names: mean absolute error for regression and misclassification rate
for binary classification. Smaller errors indicate better prediction. Signed
correlations describe association with each sensitive attribute or factor level;
they are not a universal fairness guarantee.

The optional packages `Matching`, `bnlearn`, `randomForest`, `cccp`, and `CVXR`
are required only by their corresponding methods. Consult each function's help
page for its inputs, return values, and examples.