piey27.r-universe.dev

[piey27] pkpd.Release 0.1.0

pacmanlapaz@gmail.com (Paul Angelo C. Manlapaz) — Tue, 27 Jan 2026 08:40:02 GMT

Provides a comprehensive framework for model fitting and simulation of drug release kinetics, pharmacokinetics (PK), and pharmacodynamics (PD). The package implements widely used mechanistic and empirical models for in vitro drug release, including zero-order, first-order, Higuchi, Korsmeyer-Peppas, Hixson-Crowell, and Weibull models. Pharmacokinetic functionality includes linear and nonlinear functions for one- and two-compartment models for intravenous bolus and oral administration, Michaelis-Menten kinetics, and non-compartmental analysis (NCA). Pharmacodynamic and dose-response modeling is supported through Emax-based models, including stimulatory (sigmoid Emax) and inhibitory (sigmoid Imax) Hill models, four- and five-parameter logistic models, as well as median toxic dose (TD50) and lethal dose (LD50) models. The package is intended to support parameter estimation, simulation, and model comparison in pharmaceutical research, drug development, and pharmacometrics education. For more details, see Gabrielsson & Weiner (2000) , Holford & Sheiner (1981) , and Manlapaz (2025) .

[piey27] suRface.analytics 0.1.0

pacmanlapaz@gmail.com (Paul Angelo C. Manlapaz) — Thu, 31 Jul 2025 10:20:02 GMT

A collection of functions for statistical and multivariate analysis of surface-related data, with a focus on antimicrobial activity and omniphobicity. Designed to support materials scientists and researchers in exploring structure–function relationships in surface-engineered materials through reproducible and interpretable workflows. For more details, see Li et al. (2021) , and Kwon et al. (2020) .

[piey27] adsoRptionCMF 0.1.1

pacmanlapaz@gmail.com (Paul Angelo C. Manlapaz) — Sat, 28 Jun 2025 08:40:02 GMT

Provides tools for classical parameter estimation of adsorption isotherm models, including both linear and nonlinear forms of the Freundlich, Langmuir, and Temkin isotherms. This package allows users to fit these models to experimental data, providing parameter estimates along with fit statistics such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Error metrics are computed to evaluate model performance, and the package produces model fit plots with bootstrapped 95% confidence intervals. Additionally, it generates residual plots for diagnostic assessment of the models. Researchers and engineers in material science, environmental engineering, and chemical engineering can rigorously analyze adsorption behavior in their systems using this straightforward, non-Bayesian approach. For more details, see Harding (1907) .

[piey27] adsoRptionCV 0.1.0

pacmanlapaz@gmail.com (Paul Angelo C. Manlapaz) — Tue, 03 Jun 2025 13:00:06 GMT

Provides cross-validation tools for adsorption isotherm models, supporting both linear and non-linear forms. Current methods cover commonly used isotherms including the Freundlich, Langmuir, and Temkin models. This package implements K-fold and leave-one-out cross-validation (LOOCV) with optional clustering-based fold assignment to preserve underlying data structures during validation. Model predictive performance is assessed using mean squared error (MSE), with optional graphical visualization of fold-wise MSEs to support intuitive evaluation of model accuracy. This package is intended to facilitate rigorous model validation in adsorption studies and aid researchers in selecting robust isotherm models. For more details, see Montgomery et al. (2012) , Lumumba et al. (2024) , and Yates et al. (2022) .

[piey27] adsoRptionMCMC 0.1.0

pacmanlapaz@gmail.com (Paul Angelo C. Manlapaz) — Fri, 30 May 2025 09:30:12 GMT

Provides tools for Bayesian parameter estimation of adsorption isotherm models using Markov Chain Monte Carlo (MCMC) methods. This package enables users to fit non-linear and linear adsorption isotherm models—Freundlich, Langmuir, and Temkin—within a probabilistic framework, capturing uncertainty and parameter correlations. It provides posterior summaries, 95% credible intervals, convergence diagnostics (Gelman-Rubin), and visualizations through trace and density plots. With this R package, researchers can rigorously analyze adsorption behavior in environmental and chemical systems using robust Bayesian inference. For more details, see Gilks et al. (1995) , and Gamerman & Lopes (2006) .

[piey27] ProteinPCA 0.1.0

pacmanlapaz@gmail.com (Paul Angelo C. Manlapaz) — Sat, 12 Apr 2025 08:30:05 GMT

Analysis of protein expression data can be done through Principal Component Analysis (PCA), and this R package is designed to streamline the analysis. This package enables users to perform PCA and it generates biplot and scree plot for advanced graphical visualization. Optionally, it supports grouping/clustering visualization with PCA loadings and confidence ellipses. With this R package, researchers can quickly explore complex protein datasets, interpret variance contributions, and visualize sample clustering through intuitive biplots. For more details, see Jolliffe (2001) , Gabriel (1971) , Zhang et al. (2024) , and Anandan et al. (2022) .