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clis

clis provides scalable, statistically calibrated influence diagnostics for zero-or-one inflated beta (BIc) regression models with variable dispersion. It turns the conformal normal curvature of an observation into a non-conformity score, then wraps it in a split-conformal testing procedure so that the declared influential set has its false discovery rate controlled at a level you choose.

Why clis?

Classical local influence analysis (Cook, 1986; Poon & Poon, 1999) ranks observations by curvature and asks you to eyeball an index plot. That does not scale to large data and gives no error guarantee. clis fixes both:

• Finite-sample FDR control via conformal \(p\)-values and Benjamini-Hochberg (Bates et al., 2023).
• Linear time per observation after a single model fit, so it scales to large \(n\).
• Block decomposition that attributes each observation’s influence to the inflation-probability submodel or the conditional-mean/precision submodel.

Installation

install.packages("clis_0.3.6.tar.gz", repos = NULL, type = "source")

Quick start

library(clis)
library(gamlss)

vaccination <- load_vaccination()

fit <- gamlss(
  dtp3 ~ ln_gdp + urb,
  sigma.formula = ~ ln_gdp + ln_pop,
  nu.formula    = ~ hdi,
  family  = gamlss.dist::BEOI,
  data    = vaccination,
  control = gamlss.control(trace = FALSE)
)

res <- clis_screen(fit, alpha = 0.1, seed = 1)
res
plot_clis(res)

Learn more

See vignette("clis-intro") for a full walkthrough. The scripts under data-raw/ reproduce every table and figure of the accompanying paper; data-raw/README.md is the runbook.

References

• Bates, S., Candès, E., Lei, L., Romano, Y., & Sesia, M. (2023). Testing for outliers with conformal p-values. The Annals of Statistics, 51(1), 149–178.
• Ospina, R., & Ferrari, S. L. P. (2012). A general class of zero-or-one inflated beta regression models. Computational Statistics & Data Analysis, 56(6), 1609–1623.
• Poon, W.-Y., & Poon, Y. S. (1999). Conformal normal curvature and assessment of local influence. JRSS-B, 61(1), 51–61.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.