Non-parametric prediction of survival outcomes for mixture data that incorporates covariates and a landmark time. Details are described in Garcia (2021) <doi:10.1093/biostatistics/kxz052>.

This package implements tree-based methods for longitudinal data. The package constructs decision trees that evaluate both the main effect of a covariate and its interaction with time through a weighted splitting criterion. It supports single-tree construction, bootstrap-based multiple-tree selection, and tree visualisation. For methodological details, see Obata and Sugimoto (2026) <doi:10.1007/s11634-025-00665-2>.

Real-time quantitative polymerase chain reaction (qPCR) data sets by Lievens et al. (2012) <doi:10.1093/nar/gkr775>. Provides one single tabular tidy data set in long format, encompassing three dilution series, targeted against the soybean Lectin endogene. Each dilution series was assayed in one of the following PCR-efficiency-modifying conditions: no PCR inhibition, inhibition by isopropanol and inhibition by tannic acid. The inhibitors were co-diluted along with the dilution series. The co-dilution series consists of a five-point, five-fold serial dilution. For each concentration there are 18 replicates. Each amplification curve is 60 cycles long. Original raw data file is available at the Supplementary Data section at Nucleic Acids Research Online <doi:10.1093/nar/gkr775>.

Extracts and creates an analysis pipeline for the JSON data files from Brain Sense sessions using Medtronic's Deep Brain Stimulation surgery electrode implants.

This package provides functions for normalizing standard laboratory measurements (e.g. hemoglobin, cholesterol levels) according to age and sex, based on the algorithms described in "Personalized lab test models to quantify disease potentials in healthy individuals" (Netta Mendelson Cohen, Omer Schwartzman, Ram Jaschek, Aviezer Lifshitz, Michael Hoichman, Ran Balicer, Liran I. Shlush, Gabi Barbash & Amos Tanay, <doi:10.1038/s41591-021-01468-6>). Allows users to easily obtain normalized values for standard lab results, and to visualize their distributions. See more at <https://tanaylab.weizmann.ac.il/labs/>.

This package contains functions to help create log files. The package aims to overcome the difficulty of the base R sink() command. The log_print() function will print to both the console and the file log, without interfering in other write operations.

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.

Build powerful, linked-view dashboards in shiny applications. With a declarative, one-line setup, you can create bidirectional links between interactive components. When a user interacts with one element (e.g., clicking a map marker), all linked components (such as DT tables or other charts) instantly update. Supports leaflet maps, DT tables, plotly charts, and spatial data via sf objects out-of-the-box, with an extensible API for custom components.

An emulator designed for rapid sequential emulation (e.g., Markov chain Monte Carlo applications). Works via extension of the laGP approach by Gramacy and Apley (2015 <doi:10.1080/10618600.2014.914442>). Details are given in Rumsey et al. (2023 <doi:10.1002/sta4.576>).

This package provides tools for model specification in the latent variable framework (add-on to the lava package). The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models.

Enables the generation of Laplace factor models across diverse Laplace distributions and facilitates the application of Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods and Farm Test methods to these models. Evaluates the efficacy of these methods within the context of Laplace factor models by scrutinizing parameter estimation accuracy, mean square error, and the degree of sparsity.

Companion R package for the course "Statistical analysis of correlated and repeated measurements for health science researchers" taught by the section of Biostatistics of the University of Copenhagen. It implements linear mixed models where the model for the variance-covariance of the residuals is specified via patterns (compound symmetry, toeplitz, unstructured, ...). Statistical inference for mean, variance, and correlation parameters is performed based on the observed information and a Satterthwaite approximation of the degrees of freedom. Normalized residuals are provided to assess model misspecification. Statistical inference can be performed for arbitrary linear or non-linear combination(s) of model coefficients. Predictions can be computed conditional to covariates only or also to outcome values.

Use of this package is deprecated. It has been renamed to LifeInsureR'.

This package provides a very simple implementation of a class for longitudinal data.

Bayesian model averaging (BMA) algorithms for univariate link latent Gaussian models (ULLGMs). For detailed information, refer to Steel M.F.J. & Zens G. (2024) "Model Uncertainty in Latent Gaussian Models with Univariate Link Function" <doi:10.48550/arXiv.2406.17318>. The package supports various g-priors and a beta-binomial prior on the model space. It also includes auxiliary functions for visualizing and tabulating BMA results. Currently, it offers an out-of-the-box solution for model averaging of Poisson log-normal (PLN) and binomial logistic-normal (BiL) models. The codebase is designed to be easily extendable to other likelihoods, priors, and link functions.

Repairs malformed JSON strings, particularly those generated by Large Language Models. Handles missing quotes, trailing commas, unquoted keys, and other common JSON syntax errors.

The goal of this package is to cover the most common steps in Loss Given Default (LGD) rating model development. The main procedures available are those that refer to bivariate and multivariate analysis. In particular two statistical methods for multivariate analysis are currently implemented â OLS regression and fractional logistic regression. Both methods are also available within different blockwise model designs and both have customized stepwise algorithms. Descriptions of these customized designs are available in Siddiqi (2016) <doi:10.1002/9781119282396.ch10> and Anderson, R.A. (2021) <doi:10.1093/oso/9780192844194.001.0001>. Although they are explained for PD model, the same designs are applicable for LGD model with different underlying regression methods (OLS and fractional logistic regression). To cover other important steps for LGD model development, it is recommended to use LGDtoolkit package along with PDtoolkit', and monobin (or monobinShiny') packages. Additionally, LGDtoolkit provides set of procedures handy for initial and periodical model validation.

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>.

Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.

This package provides tools for fast and accurate evaluation of skew stable distributions (CDF, PDF and quantile functions), random number generation, and parameter estimation. This is libstableR as per Royuela del Val, Simmross-Wattenberg, and Alberola López (2017) <doi:10.18637/jss.v078.i01> under a new maintainer.

Fast and accurate inference of gene-environment associations (GEA) in genome-wide studies (Caye et al., 2019, <doi:10.1093/molbev/msz008>). We developed a least-squares estimation approach for confounder and effect sizes estimation that provides a unique framework for several categories of genomic data, not restricted to genotypes. The speed of the new algorithm is several times faster than the existing GEA approaches, then our previous version of the LFMM program present in the LEA package (Frichot and Francois, 2015, <doi:10.1111/2041-210X.12382>).

Lights Out is a puzzle game consisting of a grid of lights that are either on or off. Pressing any light will toggle it and its adjacent lights. The goal of the game is to switch all the lights off. This package provides an interface to play the game on different board sizes, both through the command line or with a visual application. Puzzles can also be solved using the automatic solver included. View a demo online at <https://daattali.com/shiny/lightsout/>.

This package provides a modular end-to-end framework for dynamic risk prediction based on time-to-event and longitudinal data. This allows flexible specifications for the longitudinal and survival sub-models. The landmaRk package enables reproducible benchmarks of different model choices, including cross-validation to assess out-of-sample predictive performance. Methods are described in Velasco-Pardo, Constantine-Cooke, Lees and Vallejos (2026, manuscript under preparation) Landmarking with Latent Class Mixed Models for Dynamic Prediction of Time-to-event Data with Heterogeneous Biomarker Trajectories'.

Given a postulated model and a set of data, the comparison density is estimated and the deviance test is implemented in order to assess if the data distribution deviates significantly from the postulated model. Finally, the results are summarized in a CD-plot as described in Algeri S. (2019) <arXiv:1906.06615>.