Finite Mixture of Censored Regression Models with Skewed Distributions

Provides an implementation of finite mixture regression models for censored data under four distributional families: Normal (FM-NCR), Student t (FM-TCR), skew-Normal (FM-SNCR), and skew-t (FM-STCR). The package enables flexible modeling of skewness and heavy tails often observed in real-world data, while explicitly accounting for censoring. Functions are included for parameter estimation via the Expectation-Maximization (EM) algorithm, computation of standard errors, and model comparison criteria such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Efficient Determination Criterion (EDC). The underlying methodology is described in Park et al. (2024) .

FMCensSkewReg

FMCensSkewReg implements finite mixture censored regression models under four distributional families: Normal (FM-NCR), Student-t (FM-TCR), Skew-Normal (FM-SNCR), Skew-t (FM-STCR)

Installation

You can install the development version of FMCensSkewReg from GitHub:

# install.packages("remotes")
remotes::install_github("jpark335/FMCensSkewReg")

Example

Here is a minimal working example with simulated left-censored data generated from a two-component Skew-t mixture (both shape parameters > 0):

set.seed(1)

n <- 150
x <- cbind(1, runif(n), rnorm(n))  # design matrix (n x 3)

# Mixture parameters (both shapes positive)
pi  <- c(0.6, 0.4)
nu  <- 4
b1  <- c(0.5,  1.0, -1.0);  sigma1 <- 1; shape1 <- 2
b2  <- c(1.0, -0.5,  0.5);  sigma2 <- 2; shape2 <- 3
mu1 <- drop(x %*% b1)
mu2 <- drop(x %*% b2)

# Generate from Skew-t mixture via mixsmsn::rmix (observation-wise params)
draw1 <- function(i) {
  arg1 <- list(mu = mu1[i], sigma2 = sigma1, shape = shape1, nu = nu)
  arg2 <- list(mu = mu2[i], sigma2 = sigma2, shape = shape2, nu = nu)
  mixsmsn::rmix(1, pi, "Skew.t", list(arg1, arg2), cluster = FALSE)
}
y_true <- vapply(seq_len(n), draw1, numeric(1))

# Left-censor ~20% at the 20th percentile
cutoff <- unname(quantile(y_true, 0.20))
cc     <- as.integer(y_true <= cutoff)       # 1 = censored, 0 = observed
y      <- ifelse(cc == 1, cutoff, y_true)

# Fit two-component mixture (fast: Normal); ST variant shown but commented
fitN  <- EM.skewCens.mixR(cc, y, x, g = 2, family = "Normal", iter.max = 100)
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fitN$loglik
#> [1] -247.8763

# fitST <- EM.skewCens.mixR(cc, y, x, g = 2, family = "ST", nu = 4, iter.max = 150)
# c(N = fitN$loglik, ST = fitST$loglik)