Network analysis usually requires estimating the uncertainty of graph statistics. Through this package, we provide tools to bootstrap various networks via local bootstrap procedure. Additionally, it includes functions for generating probability matrices, creating network adjacency matrices from probability matrices, and plotting network structures. The reference will be updated soon.

Analyze graph/network data using L1 centrality and prestige. Functions for deriving global, local, and group L1 centrality/prestige are provided. Routines for visual inspection of a graph/network are also provided. Details are in Kang and Oh (2025a) <doi:10.1080/01621459.2025.2520467>, Kang and Oh (2025b) <doi:10.1080/00031305.2025.2563730>, and Kang (2025) <doi:10.23170/snu.000000188358.11032.0001856>.

Constructs likelihood models from heterogeneous observation types by composing named contributions. Each observation type (exact, left-censored, right-censored, interval-censored, or custom) contributes independently to the total log-likelihood, which is summed under an i.i.d. assumption. Provides contr_name() for standard R distributions and contr_fn() for user-defined contributions, composed via likelihood_contr() into objects compatible with the likelihood.model inference framework.

Constructs tree for continuous longitudinal data and survival data using baseline covariates as partitioning variables according to the LongCART and SurvCART algorithm, respectively. Later also included functions to calculate conditional power and predictive power of success based on interim results and probability of success for a prospective trial.

This package produces high resolution, publication ready linkage maps and quantitative trait loci maps. Input can be output from R/qtl', simple text or comma delimited files. Output is currently a portable document file.

My PhD supervisor once told me that everyone doing newspaper analysis starts by writing code to read in files from the LexisNexis newspaper archive (retrieved e.g., from <https://www.lexisnexis.com/> or any of the partner sites). However, while this is a nice exercise I do recommend, not everyone has the time. This package takes files downloaded from the newspaper archive of LexisNexis', reads them into R and offers functions for further processing.

This package provides functions to fits simple linear regression models with log normal errors and identity link, i.e. taking the responses on the original scale. See Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.

Libreria di dati, scripts e funzioni che accompagna il libro "Ricerca sociale con R. Concetti e funzioni base per la ricerca sociale".

The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.

In addition to modeling the expectation (location) of an outcome, mixed effects location scale models (MELSMs) include submodels on the variance components (scales) directly. This allows models on the within-group variance with mixed effects, and between-group variances with fixed effects. The MELSM can be used to model volatility, intraindividual variance, uncertainty, measurement error variance, and more. Multivariate MELSMs (MMELSMs) extend the model to include multiple correlated outcomes, and therefore multiple locations and scales. The latent multivariate MELSM (LMMELSM) further includes multiple correlated latent variables as outcomes. This package implements two-level mixed effects location scale models on multiple observed or latent outcomes, and between-group variance modeling. Williams, Martin, Liu, and Rast (2020) <doi:10.1027/1015-5759/a000624>. Hedeker, Mermelstein, and Demirtas (2008) <doi:10.1111/j.1541-0420.2007.00924.x>.

Interpretability of complex machine learning models is a growing concern. This package helps to understand key factors that drive the decision made by complicated predictive model (so called black box model). This is achieved through local approximations that are either based on additive regression like model or CART like model that allows for higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>. More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.

Logger to keep track of informational events and errors useful for debugging.

This package provides functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.

Build complex LaTeX mathematical expressions using intuitive R functions. Replace error-prone LaTeX syntax with readable, modular functions that make mathematical typesetting straightforward and maintainable.

Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) <DOI:10.1016/j.ejor.2018.02.009>).

Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on the boundary of space. Exact method utilizes the property of boundary vectors to re-parametrize the model without losing any information, and fits the model on the standard fitting algorithm with no convergence issues.

Identifying latent genetic interactions in genome-wide association studies using the Latent Interaction Testing (LIT) framework. LIT is a flexible kernel-based approach that leverages information across multiple traits to detect latent genetic interactions without specifying or observing the interacting variable (e.g., environment). LIT accepts standard PLINK files as inputs to analyze large genome-wide association studies.

This package performs likelihood-based inference for stationary time series extremes. The general approach follows Fawcett and Walshaw (2012) <doi:10.1002/env.2133>. Marginal extreme value inferences are adjusted for cluster dependence in the data using the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood for the model parameters. A log-likelihood for the extremal index is produced using the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make inferences about extreme values. Both maximum likelihood and Bayesian approaches are available.

This package implements the kK-NN algorithm, an adaptive k-nearest neighbor classifier that adjusts the neighborhood size based on local data curvature. The method estimates local Gaussian curvature by approximating the shape operator of the data manifold. This approach aims to improve classification performance, particularly in datasets with limited samples.

Impute observed values below the limit of detection (LOD) via censored likelihood multiple imputation (CLMI) in single-pollutant models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>. CLMI handles exposure detection limits that may change throughout the course of exposure assessment. lodi provides functions for imputing and pooling for this method.

This package provides an interactive shiny application to construct stage-structured life tables from tabular input data. The application includes input validation, demographic calculations, visualization tools, and export of tables and figures to support reproducible workflows in ecological and entomological studies. Methods for life table construction follow classical demographic approaches described in Martinez (2015) <doi:10.13140/RG.2.2.21333.65760>.

Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. To overcome these shortcomings, we establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference and graphics. In this package, the deviance tests and comparison density plots are performed to conduct the LP smoothed inference, where the letter L denotes nonparametric methods based on quantiles and P stands for polynomials. Simulations methods are used to perform variance estimation, inference and post-selection adjustments. Algeri S. and Zhang X. (2020) <arXiv:2005.13011>.

This package provides tools for performing Bayesian inference on epidemiological data to estimate the time-varying reproductive number and other related metrics. These methods were published in Li and White (2021) <doi:10.1371/journal.pcbi.1009210>. This package supports analyses based on aggregated case count data and individual line list data, facilitating enhanced surveillance and intervention planning for infectious diseases like COVID-19.

Navigating the shift of clinical laboratory data from primary everyday clinical use to secondary research purposes presents a significant challenge. Given the substantial time and expertise required for lab data pre-processing and cleaning and the lack of all-in-one tools tailored for this need, we developed our algorithm lab2clean as an open-source R-package. lab2clean package is set to automate and standardize the intricate process of cleaning clinical laboratory results. With a keen focus on improving the data quality of laboratory result values and units, our goal is to equip researchers with a straightforward, plug-and-play tool, making it smoother for them to unlock the true potential of clinical laboratory data in clinical research and clinical machine learning (ML) model development. Functions to clean & validate result values (Version 1.0) are described in detail in Zayed et al. (2024) <doi:10.1186/s12911-024-02652-7>. Functions to standardize & harmonize result units (added in Version 2.0) are described in detail in Zayed et al. (2025) <doi:10.1016/j.ijmedinf.2025.106131>.