KWELA

KWELA extends RT-QuIC (Real-Time Quaking-Induced Conversion) statistical analysis to complex environmental matrices through hierarchical adaptive classification.

KWELA is named after a deity of the Fore people of Papua New Guinea, among whom Kuru, a notable human prion disease, was identified.

Installation

# Install from CRAN
install.packages("KWELA")

# Or install development version from GitHub
# install.packages("devtools")
devtools::install_github("RFeissIV/KWELA")

6-Layer Architecture

KWELA implements a hierarchical adaptive classification system with dual-mode operation:

Layer	Function	Description
1	Hard Gate	Biological constraint filter with stochastic rescue (research mode)
2	Per-Well Scoring	Profile-dependent adaptive transforms
3	Adaptive Combination	Separation-aware score combiner
4	Adaptive Cutoff	Youden-optimized threshold per plate
5	Replicate Consensus	Treatment-level classification
6	Instability Detection	Matrix interference override

Quick Start

library(KWELA)

# Diagnostic mode (default) — deterministic, no stochastic rescue
result <- kwela_analyze(your_data)

# Research mode — full adaptive architecture
result <- kwela_analyze(your_data, mode = "research")

# Get treatment-level summary
summary <- kwela_summarize(result)

# View diagnostics (includes instability flags)
diag <- kwela_diagnostics(result)

Dual-Mode Operation

Feature	Diagnostic (default)	Research
Stochastic rescue	Disabled	Enabled
Stochastic score in combiner	Excluded	Included
TTT/MP/RAF scoring	Full	Full
Instability detection	Enabled	Enabled

Profiles

Profile	When to Use	Cohen’s d
`standard`	Clean assay, strong separation	> 3.0
`sensitive`	Spiked samples, moderate separation	1.5 - 3.0
`matrix_robust`	Environmental matrices, poor separation	< 1.5
`auto`	Let KWELA decide based on data	-

Consensus Rules

Rule	Classification Criteria
`strict`	All wells must be positive
`majority`	>50% of wells positive (default)
`flexible`	Any well positive
`threshold`	Mean score >= threshold

Key Features

Dual-mode operation: diagnostic (deterministic) vs research (adaptive)
Instability detection: 6 deterministic metrics for matrix interference
Auto-profile selection based on PC/NC separation quality
RAF integration for artifact detection and quality scoring
Group-aware mixed-assay support (RT-QuIC + Nano-QuIC)
Matrix-aware baseline correction with per-group local controls
Comprehensive metrics: CRPS, Wasserstein, Energy distance, SSMD, JSD

Citation

If you use KWELA in your research, please cite:

Feiss RA IV (2026). KWELA: Hierarchical Adaptive RT-QuIC Classification.
R package version 1.0.0. https://CRAN.R-project.org/package=KWELA

Note

This package implements methodology currently under peer review. Please contact the author before publication using this approach.

Development & AI Disclosure

Development followed an iterative human-machine collaboration where all algorithmic design, statistical methodologies, and biological validation logic were conceptualized, tested, and iteratively refined by Richard A. Feiss through repeated cycles of running experimental data, evaluating analytical outputs, and selecting among candidate algorithms and approaches. AI systems (Anthropic Claude and OpenAI GPT) served as coding assistants and analytical sounding boards under continuous human direction. The selection of statistical methods, evaluation of biological plausibility, and all final methodology decisions were made by the human author. AI systems did not independently originate algorithms, statistical approaches, or scientific methodologies.