User Guide# 1. Aggregative Quantification 1.1. Using Aggregative Quantification Methods 1.2. General Concept 1.2.1. Examples 1.3. Counters For Quantification 1.3.1. Classify and Count 1.3.2. Probabilistic Classify and Count 1.4. Adjust Counting 1.4.1. Threshold Adjustment 1.4.2. Matrix Adjustment 1.5. Likelihood-Based Quantification 1.5.1. Maximum Likelihood Prevalence Estimation (MLPE) 1.5.2. Expectation Maximization for Quantification (EMQ) 1.6. Mixture Models 1.6.1. DyS: Distribution y-Similarity Framework 1.6.2. HDy: Hellinger Distance y-Similarity 1.6.3. SMM: Sample Mean Matching 1.6.4. SORD: Sample Ordinal Distance 1.7. Nearest Neighbors 1.7.1. PWK: Pair-wise Weighted K-Nearest Neighbors 1.8. Kernel Density Estimation 1.8.1. KDEy: Kernel Density Estimation y-Similarity 2. Non Aggregative Quantification 2.1. Mixture Models for Non-Aggregative Quantification 2.1.1. HDx: Hellinger Distance x-Similarity 3. Meta Quantification 3.1. Ensemble for Quantification 3.2. Bootstrap in Quantification 3.3. QuaDapt: Drift-Resilient Score Adaptation 4. Model Selection and Evaluation 4.1. Protocols for Quantification 4.1.1. Artificial-Prevalence Protocol (APP) 4.1.2. Natural-Prevalence Protocol (NPP) 4.1.3. Uniform Prevalence Protocol (UPP) 4.1.4. Personalized Prevalence Protocol (PPP) 4.2. Tuning Hyperparameters 4.3. References 4.4. Evaluation Metrics 4.4.1. Single Label Quantification (SLQ) Metrics 4.4.2. Regression-Based Quantification (RQ) Metrics 4.4.3. Ordinal Quantification (OQ) Metrics 5. Confidence Intervals 5.1. General Concept 5.2. Percentile-Based Confidence Intervals 5.3. Confidence Ellipse in Simplex 5.4. Confidence Ellipse in CLR Space 5.4.1. References 6. Building a Quantifier 6.1. General Quantifiers 6.2. Aggregative Quantifiers 6.3. Aggregation Types 6.4. Binary Quantifiers 6.5. Validation Utilities 6.6. Parameter Constraints
