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 -values and Benjamini-Hochberg (Bates et al., 2023).
- Linear time per observation after a single model fit, so it scales to large .
- 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.