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caplime: Conic Adaptive Piecewise LIME

caplime provides an educational R implementation of piecewise ridge regression with first- or second-degree hinge bases, and local surrogate explanations. The available local explanation modes are LIME and OPTILIME only. The main explanation function is CAP\_LIME().

Installation from the package folder

In RStudio, install devtools once if necessary. Then open the package project folder (the one containing DESCRIPTION) and run:

install.packages("devtools")
devtools::install("/path/to/caplime", dependencies = FALSE)
library(caplime)

Or build the CRAN source archive by running R CMD build caplime from the parent directory, and install the resulting caplime\_0.1.0.tar.gz.

Local explanations: LIME and OPTILIME

library(caplime)
set.seed(1)
x <- matrix(rnorm(300), ncol = 3)
colnames(x) <- c("a", "b", "c")
f <- function(newx) newx\[, 1] + newx\[, 2]^2

lime\_result <- CAP\_LIME(x, x\[1, ], f, method = "LIME", num\_samples = 200)
opti\_result <- CAP\_LIME(x, x\[1, ], f, method = "OPTILIME", num\_samples = 200)
print(head(lime\_result$effects))
print(head(opti\_result$effects))
plot\_CAP\_LIME(lime\_result)

Piecewise model: both degree settings

set.seed(2)
y <- x\[, 1]^2 + x\[, 2]
model\_degree1 <- fit\_CAP\_model(x, y, num\_knots = 2, degree = 1)
model\_degree2 <- fit\_CAP\_model(x, y, num\_knots = 2, degree = 2)
head(predict\_CAP\_model(model\_degree1, x))
head(predict\_CAP\_model(model\_degree2, x))

Testing

Run devtools::check("/path/to/caplime", cran = TRUE) in RStudio. The test scripts under tests/ exercise both degree = 1 and degree = 2, and both local explanation modes.

Scientific scope

The piecewise model uses ridge-penalized least squares with hinge basis functions, not an explicit conic solver. The OPTILIME mode searches a fixed set of bandwidth values to meet a local R-squared target; it is a simplified variant and does not reproduce the complete published OptiLIME procedure. CAP\_stability() returns a descriptive coefficient-variation table (CSI), not an inferential confidence-interval-based CSI.

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.