R: Semi-Supervised Learning under a Mixed-Missingness Mechanism in Finite Mixture Models

# Semi-Supervised Learning under a Mixed-Missingness Mechanism in Finite Mixture Models

## Documentation for package ‘SSLfmm’ version 0.1.0

- DESCRIPTION file

## Help Pages

bayesclassifier | Bayes' Rule Classifier
compute_d2 | Squared Discriminant Score for Two-Group LDA (Equal Covariance)
EM_FMM_SemiSupervised | EM for Semi-Supervised FMM with a Mixed-Missingness Mechanism (MCAR + entropy-based MAR)
EM_FMM_SemiSupervised_Complete_Initial | Complete-Data Warm-Up Initialization for Semi-Supervised FMM with a Mixed-Missingness Mechanism
EM_FMM_SemiSupervised_Initial | Quick Initializer for alpha, xi, and Mixture Parameters
error_beta_classification | Compute Theoretical Bayes' Error for a Binary Gaussian Mixture
get_clusterprobs | Posterior Cluster Probabilities for a Gaussian Mixture
get_entropy | Per-Row Entropy of Posterior Cluster Probabilities
initialestimate | Initialize Parameters for a FMM from Labeled Subset
logsumexp | Numerically Stable Log-Sum-Exp
neg_loglik | Negative Log-Likelihood for Semi-Supervised FMM with a Mixed-Missingness Mechanism
normalise_logprob | Normalise Log-Probabilities
pack_theta | Pack FMM Parameters into a Vector
rmix | Draw from a Gaussian Mixture Model
simulate_mixed_missingness | Simulate a Gaussian Mixture Dataset with a Mixed-Missingness Mechanism (MAR + MCAR)
unpack_theta | Unpack FMM Parameter Vector