Calculates 3D lacunarity from voxel data. It is designed for use with point clouds generated from Light Detection And Ranging (LiDAR) scans in order to measure the spatial heterogeneity of 3-dimensional structures such as forest stands. It provides fast C++ functions to efficiently bin point cloud data into voxels and calculate lacunarity using different variants of the gliding-box algorithm originated by Allain & Cloitre (1991) <doi:10.1103/PhysRevA.44.3552>.

Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data. In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) <doi:10.1177/0962280220946080> Random forests for high-dimensional longitudinal data.

Location and scale hypothesis testing using the LePage test and variants of its as proposed by Hussain A. and Tsagris M. (2025), <doi:10.48550/arXiv.2509.19126>.

Allows to install the R languageserver with all dependencies into a separate library and use that independent installation automatically when R is instantiated as a language server process. Useful for making language server seamless to use without running into package version conflicts.

This package provides classes and methods for spatially explicit land use change modelling in R.

Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details.

European Commission's Labour Market Policy (LMP) database (<https://webgate.ec.europa.eu/empl/redisstat/databrowser/explore/all/lmp?lang=en&display=card&sort=category>) provides information on labour market interventions, which are government actions to help and support the unemployed and other disadvantaged groups in the transition from unemployment or inactivity to work. It covers the EU countries and Norway. This package provides functions for downloading and importing the LMP data and metadata (codelists).

R6 classes to model traditional life insurance contracts like annuities, whole life insurances or endowments. Such life insurance contracts provide a guaranteed interest and are not directly linked to the performance of a particular investment vehicle, but they typically provide (discretionary) profit participation. This package provides a framework to model such contracts in a very generic (cash-flow-based) way and includes modelling profit participation schemes, dynamic increases or more general contract layers, as well as contract changes (like sum increases or premium waivers). All relevant quantities like premium decomposition, reserves and benefits over the whole contract period are calculated and potentially exported to Excel'. Mortality rates are given using the MortalityTables package.

Estimates marginal likelihood from a posterior sample using the method described in Wang et al. (2023) <doi:10.1093/sysbio/syad007>, which does not require evaluation of any additional points and requires only the log of the unnormalized posterior density for each sampled parameter vector.

Implementation of the algorithm introduced in Shah, R. D. (2016) <https://www.jmlr.org/papers/volume17/13-515/13-515.pdf>. Data with thousands of predictors can be handled. The algorithm performs sequential Lasso fits on design matrices containing increasing sets of candidate interactions. Previous fits are used to greatly speed up subsequent fits, so the algorithm is very efficient.

An extendable toolkit for interactive data visualization and exploration.

Implement tour algorithms in interactive graphical system loon'.

Implementation of the three-step approach of (latent transition) cognitive diagnosis model (CDM) with covariates. This approach can be used for single time-point situations (cross-sectional data) and multiple time-point situations (longitudinal data) to investigate how the covariates are associated with attribute mastery. For multiple time-point situations, the three-step approach of latent transition CDM with covariates allows researchers to assess changes in attribute mastery status and to evaluate the covariate effects on both the initial states and transition probabilities over time using latent logistic regression. Because stepwise approaches often yield biased estimates, correction for classification error probabilities (CEPs) is considered in this approach. The three-step approach for latent transition CDM with covariates involves the following steps: (1) fitting a CDM to the response data without covariates at each time point separately, (2) assigning examinees to latent states at each time point and computing the associated CEPs, and (3) estimating the latent transition CDM with the known CEPs and computing the regression coefficients. The method was proposed in Liang et al. (2023) <doi:10.3102/10769986231163320> and demonstrated using mental health data in Liang et al. (in press; annotated R code and data utilized in this example are available in Mendeley data) <doi:10.17632/kpjp3gnwbt.1>.

We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.

Generate concentration-time profiles from linear pharmacokinetic (PK) systems, possibly with first-order absorption or zero-order infusion, possibly with one or more peripheral compartments, and possibly under steady-state conditions. Single or multiple doses may be specified. Secondary (derived) PK parameters (e.g. Cmax, Ctrough, AUC, Tmax, half-life, etc.) are computed.

Latent binary Bayesian neural networks (LBBNNs) are implemented using torch', an R interface to the LibTorch backend. Supports mean-field variational inference as well as flexible variational posteriors using normalizing flows. The standard LBBNN implementation follows Hubin and Storvik (2024) <doi:10.3390/math12060788>, using the local reparametrization trick as in Skaaret-Lund et al. (2024) <https://openreview.net/pdf?id=d6kqUKzG3V>. Input-skip connections are also supported, as described in Høyheim et al. (2025) <doi:10.48550/arXiv.2503.10496>.

Supervised classification methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in PK Josephine et. al., (2021) <doi:10.59176/kjcs.v1i1.1259>; and datasets to test them on, which highlight the strengths and weaknesses of each technique.

Imports a data frame containing a single time resolved laser ablation mass spectrometry analysis of a foraminifera (or other carbonate shell), then detects when the laser has burnt through the foraminifera test as a function of change in signal over time.

This is an extension package to logrx', which is a log creation program focused on Clinical Reporting within the Pharma Industry. This package enables a simple shiny-based Add-in that provides a point and click interface to produce a log for a single program.

Routines for fitting Logic Regression models. Logic Regression is described in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005) <DOI:10.1002/gepi.20042>.

The "Manual on Low-flow Estimation and Prediction" (Gustard & Demuth (2009, ISBN:978-92-63-11029-9)), published by the World Meteorological Organisation, gives a comprehensive summary on how to analyse stream flow data focusing on low-flows. This packages provides functions to compute the described statistics and produces plots similar to the ones in the manual.

Fast calculation of Area Under Curve (AUC) metric of a Receiver Operating Characteristic (ROC) curve, using the algorithm of Fawcett (2006) <doi:10.1016/j.patrec.2005.10.010>. Therefore it is appropriate for large-scale AUC metric calculations.

An interface to LuaJIT <https://luajit.org>, a just-in-time compiler for the Lua scripting language <https://www.lua.org>. Allows users to run Lua code from R'.

This package provides tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.3150/23-BEJ1711>.