senseR

Quality assessment framework for proxy indicators using monotonicity, information content, responsiveness, dispersion, stagnation, ceiling effect, and stability metrics.

Installation

# install.packages("devtools")
devtools::install_github("username/senseR")

Example

library(senseR)

set.seed(42)
df <- data.frame(
  gdp = rnorm(50, 100, 10),
  ntl = rnorm(50, 50, 5) #ntl is nightime light index from Google Earth Engine (GEE)
)
senser(df, proxy = "ntl", target = "gdp") #English explanation by default

Methodological Background

The composite score integrates: (1) Spearman monotonicity; (2) R-squared information content; (3) Elasticity responsiveness; (4) Coefficient of variation; (5) Absolute change; (6) Ceiling effect; and (7) Beta stability.

Scientific References

The methodological foundation of senseR is based on established statistical and econometric literature:

1. Monotonicity (Spearman Rank Correlation)

Spearman, C. (1904). The proof and measurement of association between two things. American Journal of Psychology, 15(1), 72–101. https://doi.org/10.2307/1412159

2. Information Content (R² Interpretation)

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.

3. Elasticity and Responsiveness

Wooldridge, J. M. (2013). Introductory Econometrics: A Modern Approach (5th ed.). South-Western Cengage Learning.

4. Composite Indicator Methodology

OECD (2008). Handbook on Constructing Composite Indicators: Methodology and User Guide. OECD Publishing. https://doi.org/10.1787/9789264043466-en

5. Time Series Stability Concepts

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.

6. Structural Stability (Chow Test)

Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605. https://doi.org/10.2307/1910133