Extracts features from amplification curve data of quantitative 
    Polymerase Chain Reactions (qPCR) according to Pabinger et al. 2014 
    <doi:10.1016/j.bdq.2014.08.002> for machine learning purposes. Helper 
    functions prepare the amplification curve data for processing as functional 
    data (e.g., Hausdorff distance) or enable the plotting of amplification 
    curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() 
    functions of Burdukiewicz et al. (2018) <doi:10.1016/j.bdq.2018.08.001> 
    can be used to predict amplification curves with an hook effect-like 
    curvature. The pcrfit_single() function can be used to extract features 
    from an amplification curve.

Provides a unified framework for principal component analysis (PCA) and principal component regression (PCR), including standard PCA, sparse PCA, robust PCA, and supervised PCA. The package supports automatic selection of the number of components using cumulative variance and elbow methods and integrates PCA with regression modelling through PCR models. It includes tools for PCA suitability assessment using Bartlett's test of sphericity and the Kaiser-Meyer-Olkin (KMO) measure. Visualisation utilities such as scree plots and biplots are provided for interpretation. The methods are designed to handle multicollinearity, outliers, and high-dimensional data, making them suitable for applied statistical modelling and data analysis. The methodology is based on established approaches described in Jolliffe (2002) <doi:10.1007/b98835>, Zou et al. (2006) <doi:10.1111/j.1467-9868.2005.00503.x>, and Hubert et al. (2005) <doi:10.1198/004017004000000563>.

Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations?  This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.

Implements simulation, numerical maximum likelihood estimation 
    via fourth-order Runge-Kutta path optimization, and high-speed Bayesian 
    Markov Chain Monte Carlo (MCMC) samplers for Weibull lifetimes under 
    progressive censoring setups with competitive latent risks. Both point 
    estimation and interval estimation are provided for the model parameters.