---
title: "senseR-introduction"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{senseR-introduction}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

```{r setup}
library(senseR)
```

## Introduction

`senseR` is a statistical diagnostic tool designed to evaluate whether proxy indicators can reliably represent an underlying construct that cannot be directly observed or measured.
It is intended for analytical diagnostics and policy-oriented assessment. Note that it does not perform causal inference.

## The `senser()` Function

The main function is senser(). It computes a diagnostic score for each proxy based on five components:
Monotonicity – Spearman rank correlation between proxy and target.
Information content – Proportion of variance explained (R-squared).
Stability – Sensitivity of regression coefficients across subsamples.
Distributional alignment – Similarity of standardized distributions via Kolmogorov–Smirnov test.
Bias risk – Penalization for strong nonlinearity indicating potential proxy distortion.

The overall score is the average of these five components, ranging from 0 to 1:
Suitable proxy: score >= 0.70
Conditionally suitable: 0.40 <= score < 0.70
Not suitable proxy: score < 0.40
Interpretation is automatically generated in English or Indonesian.

```{r}
# example
set.seed(123)

# Simulated dataset
df <- data.frame(
  gdp = rnorm(100, 10, 2),
  ntl = rnorm(100, 50, 10),
  road_density = rnorm(100, 3, 0.5),
  mobile_signal = rnorm(100, 70, 15)
)

# Run senser in English
senser(
  data = df,
  proxy = c("ntl", "road_density", "mobile_signal"),
  target = "gdp",
  lang = "english"
)
```

```{r}
# Indonesian language support
senser(
  data = df,
  proxy = c("ntl", "road_density"),
  target = "gdp",
  lang = "indonesia"
)

```

## Notes
Output is printed to the console; the function does not return a value invisibly. Designed for applied diagnostics and policy assessment. Always compare multiple proxies to select the most reliable indicators.

## References

Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro-level estimation of poverty and inequality. Econometrica.
Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring economic growth from outer space. American Economic Review.