This package provides functions to upload vectorial data and derive landscape connectivity metrics in habitat or matrix systems. Additionally, includes an approach to assess individual patch contribution to the overall landscape connectivity, enabling the prioritization of habitat patches. The computation of landscape connectivity and patch importance are very useful in Landscape Ecology research. The metrics available are: number of components, number of links, size of the largest component, mean size of components, class coincidence probability, landscape coincidence probability, characteristic path length, expected cluster size, area-weighted flux and integral index of connectivity. Pascual-Hortal, L., and Saura, S. (2006) <doi:10.1007/s10980-006-0013-z> Urban, D., and Keitt, T. (2001) <doi:10.2307/2679983> Laita, A., Kotiaho, J., Monkkonen, M. (2011) <doi:10.1007/s10980-011-9620-4>.

This package implements a partitioned Empirical Bayes Expectation Conditional Maximization (ECM) algorithm for sparse high-dimensional linear mixed modeling as described in Zgodic, Bai, Zhang, and McLain (2025) <doi:10.1007/s11222-025-10649-z>. The package provides efficient estimation and inference for mixed models with high-dimensional fixed effects.

Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) <doi:10.1111/2041-210X.12141> in a few user-friendly R functions. All functions handle data obtained with Ahmes 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes.

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.

It fits a robust linear quantile regression model using a new family of zero-quantile distributions for the error term. Missing values and censored observations can be handled as well. This family of distribution includes skewed versions of the Normal, Student's t, Laplace, Slash and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses as shown in Galarza et.al.(2020) <doi:10.1007/s13571-020-00231-0>. It provides estimates and full inference. It also provides envelopes plots for assessing the fit and confidences bands when several quantiles are provided simultaneously.

Letter Values for the course Exploratory Data Analysis at Federal University of Bahia (Brazil). The approach implemented in the package is presented in the textbook of Tukey (1977) <ISBN: 978-0201076165>.

Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.

Input latitude and longitude values or an sf/sfc POINT object and get back the time zone in which they exist. Two methods are implemented. One is very fast and uses Rcpp in conjunction with data from the Javascript library (<https://github.com/darkskyapp/tz-lookup-oss/>). This method also works outside of countries borders and in international waters, however speed comes at the cost of accuracy - near time zone borders away from populated centres there is a chance that it will return the incorrect time zone. The other method is slower but more accurate - it uses the sf package to intersect points with a detailed map of time zones from here: <https://github.com/evansiroky/timezone-boundary-builder/>. The package also contains several utility functions for helping to understand and visualize time zones, such as listing of world time zones, including information about daylight savings times and their offsets from UTC. You can also plot a time zone to visualize the UTC offset over a year and when daylight savings times are in effect.

Helps to render interlinear glossed linguistic examples in html rmarkdown documents and then semi-automatically compiles the list of glosses at the end of the document. It also provides a database of linguistic glosses.

Create tables from within R directly on Google Slides presentations. Currently supports matrix, data.frame and flextable objects.

Estimation of Latent Order Logistic (LOLOG) Models for Networks. LOLOGs are a flexible and fully general class of statistical graph models. This package provides functions for performing MOM, GMM and variational inference. Visual diagnostics and goodness of fit metrics are provided. See Fellows (2018) <doi:10.48550/arXiv.1804.04583> for a detailed description of the methods.

This package provides functions that allow for convenient working with vector space models of semantics/distributional semantic models/word embeddings. Originally built for LSA models (hence the name), but can be used for all such vector-based models. For actually building a vector semantic space, use the package lsa or other specialized software. Downloadable semantic spaces can be found at <https://sites.google.com/site/fritzgntr/software-resources>.

Lipid Maps Rest service. Researchers can access the Lipid Maps Rest service programmatically and conveniently integrate it into the current workflow or packages.

The reference implementation of model equations and default parameters for the toxicokinetic-toxicodynamic (TKTD) model of the Lemna (duckweed) aquatic plant. Lemna is a standard test macrophyte used in ecotox effect studies. The model was described and published by the SETAC Europe Interest Group Effect Modeling. It is a refined description of the Lemna TKTD model published by Schmitt et al. (2013) <doi:10.1016/j.ecolmodel.2013.01.017>.

This computes Lipinski Rule of Five parameters and offers visualization for drug discovery. It analyzes molecular properties like molecular weight, hydrogen bond donors, acceptors, and ALogP, providing histograms and pass/fail status plots for efficient compound evaluation, aiding in drug development.

Local explanations of machine learning models describe, how features contributed to a single prediction. This package implements an explanation method based on LIME (Local Interpretable Model-agnostic Explanations, see Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>) in which interpretable inputs are created based on local rather than global behaviour of each original feature.

Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.

We provide a solution for performing permutation tests on linear and mixed linear regression models. It allows users to obtain accurate p-values without making distributional assumptions about the data. By generating a null distribution of the test statistics through repeated permutations of the response variable, permutation tests provide a powerful alternative to traditional parameter tests (Holt et al. (2023) <doi:10.1007/s10683-023-09799-6>). In this early version, we focus on the permutation tests over observed t values of beta coefficients, i.e.original t values generated by parameter tests. After generating a null distribution of the test statistic through repeated permutations of the response variable, each observed t values would be compared to the null distribution to generate a p-value. To improve the efficiency,a stop criterion (Anscombe (1953) <doi:10.1111/j.2517-6161.1953.tb00121.x>) is adopted to force permutation to stop if the estimated standard deviation of the value falls below a fraction of the estimated p-value. By doing so, we avoid the need for massive calculations in exact permutation methods while still generating stable and accurate p-values.

This package produces a group screening procedure that is based on maximum Lq-likelihood estimation, to simultaneously account for the group structure and data contamination in variable screening. The methods are described in Li, Y., Li, R., Qin, Y., Lin, C., & Yang, Y. (2021) Robust Group Variable Screening Based on Maximum Lq-likelihood Estimation. Statistics in Medicine, 40:6818-6834.<doi:10.1002/sim.9212>.

The landmark approach allows survival predictions to be updated dynamically as new measurements from an individual are recorded. The idea is to set predefined time points, known as "landmark times", and form a model at each landmark time using only the individuals in the risk set. This package allows the longitudinal data to be modelled either using the last observation carried forward or linear mixed effects modelling. There is also the option to model competing risks, either through cause-specific Cox regression or Fine-Gray regression. To find out more about the methods in this package, please see <https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.

An implementation of logistic normal multinomial (LNM) clustering. It is an extension of LNM mixture model proposed by Fang and Subedi (2020) <arXiv:2011.06682>, and is designed for clustering compositional data. The package includes 3 extended models: LNM Factor Analyzer (LNM-FA), LNM Bicluster Mixture Model (LNM-BMM) and Penalized LNM Factor Analyzer (LNM-FA). There are several advantages of LNM models: 1. LNM provides more flexible covariance structure; 2. Factor analyzer can reduce the number of parameters to estimate; 3. Bicluster can simultaneously cluster subjects and taxa, and provides significant biological insights; 4. Penalty term allows sparse estimation in the covariance matrix. Details for model assumptions and interpretation can be found in papers: Tu and Subedi (2021) <arXiv:2101.01871> and Tu and Subedi (2022) <doi:10.1002/sam.11555>.

Computations related to group sequential boundaries. Includes calculation of bounds using the Lan-DeMets alpha spending function approach. Based on FORTRAN program ld98 implemented by Reboussin, et al. (2000) <doi:10.1016/s0197-2456(00)00057-x>.

The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.

The proposed method aims at predicting the longitudinal mean response trajectory by a kernel-based estimator. The kernel estimator is constructed by imposing weights based on subject-wise similarity on L2 metric space between predictor trajectories as well as time proximity. Users could also perform variable selections to derive functional predictors with predictive significance by the proposed multiplicative model with multivariate Gaussian kernels.