New package: GrowthParameterEstimation v0.2.2

[JuliaRegistrator]

• Registering package: GrowthParameterEstimation
• Repository: https://github.com/kadinelbak/GrowthParameterEstimation.jl
• Created by: @kadinelbak
• Version: v0.2.2
• Commit: cdc3003645a2a0e60c91478724824fe346981435
• Reviewed by: @kadinelbak
• Reference: New package kadinelbak/GrowthParameterEstimation.jl#29
• Description: Exciting simple ode solver that allows for clean outputs in a jupyter notebooks.
• Release notes:

GrowthParamEst v0.2.2 - Initial Release
🎉 New Package Features
GrowthParamEst is a comprehensive Julia package for fitting and analyzing ordinary differential equation (ODE) models to biological growth data.

📊 Core Functionality
Parameter Estimation: Robust ODE parameter fitting using BlackBoxOptim.jl
Model Comparison: Statistical comparison of multiple growth models with BIC scoring
Cross-Validation: K-fold and leave-one-out validation for model reliability assessment
Sensitivity Analysis: Parameter sensitivity analysis for understanding model behavior
Residual Analysis: Diagnostic tools for model validation and outlier detection
🧬 Available Growth Models (8 total)
Logistic Growth: du/dt = ru(1 - u/K)
Logistic with Death: du/dt = ru(1 - u/K) - δu
Gompertz Growth: du/dt = rulog(K/u)
Gompertz with Death: du/dt = rulog(K/u) - δu
Exponential Growth: du/dt = r*u
Exponential with Delay: Exponential growth with lag time
Logistic with Delay: Logistic growth with lag time
Exponential with Death and Delay: Combined death and lag effects
🔧 Key Functions
run_single_fit(): Fit single ODE model to data
compare_models(): Compare two models on same dataset
fit_three_datasets(): Analyze multiple experimental replicates
k_fold_cross_validation(): Validate model reliability
parameter_sensitivity_analysis(): Assess parameter importance
residual_analysis(): Diagnostic analysis for model quality
enhanced_bic_analysis(): Comprehensive model selection
📦 Dependencies
Julia ≥ 1.10
DifferentialEquations.jl for ODE solving
BlackBoxOptim.jl for optimization
StatsBase.jl for statistical analysis
No plotting dependencies (clean statistical analysis focus)
🎯 Target Applications
Microbial growth analysis
Cell culture dynamics
Population modeling
Biological time series analysis
Parameter estimation for growth experiments
📖 Usage Example
using GrowthParamEst

Fit logistic growth to experimental data
time_data = [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
measurement_data = [100.0, 150.0, 230.0, 350.0, 520.0, 720.0]

result = run_single_fit(time_data, measurement_data, [0.5, 1000.0])
println("Growth rate: ", result.params[1])
println("Carrying capacity: ", result.params[2])

NOTE THE ODD NAMING SCHEME IS BECAUSE I WAS HAVING PROBLEMS WITH TAGGING AND RELEAESS

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