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.

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>.

Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write las and laz files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.

This package provides functions for genome-wide association studies (GWAS)/gene-environment-wide interaction studies (GEWIS) with longitudinal outcomes and exposures. He et al. (2017) "Set-Based Tests for Gene-Environment Interaction in Longitudinal Studies" and He et al. (2017) "Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA)".

This package provides functions for forest objects detection, structure metrics computation, model calibration and mapping with airborne laser scanning: co-registration of field plots (Monnet and Mermin (2014) <doi:10.3390/f5092307>); tree detection (method 1 in Eysn et al. (2015) <doi:10.3390/f6051721>) and segmentation; forest parameters estimation with the area-based approach: model calibration with ground reference, and maps export (Aussenac et al. (2023) <doi:10.12688/openreseurope.15373.2>); extraction of both physical (gaps, edges, trees) and statistical features useful for e.g. habitat suitability modeling (Glad et al. (2020) <doi:10.1002/rse2.117>) and forest maturity mapping (Fuhr et al. (2022) <doi:10.1002/rse2.274>).

This package provides a suite of tools for literature-based discovery in biomedical research. Provides functions for retrieving scientific articles from PubMed and other NCBI databases, extracting biomedical entities (diseases, drugs, genes, etc.), building co-occurrence networks, and applying various discovery models including ABC', AnC', LSI', and BITOLA'. The package also includes visualization tools for exploring discovered connections.

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 a diverse collection of georeferenced and spatial datasets from different domains including urban studies, housing markets, environmental monitoring, transportation, and socio-economic indicators. The package consolidates datasets from multiple open sources such as Kaggle, chopin, spData, adespatial, and bivariateLeaflet. It is designed for researchers, analysts, and educators interested in spatial analysis, geostatistics, and geographic data visualization. The datasets include point patterns, polygons, socio-economic data frames, and network-like structures, allowing flexible exploration of geospatial phenomena.

Performing impulse-response function (IRF) analysis of relevant variables of agent-based simulation models, in particular for models described in LSD format. Based on the data produced by the simulation model, it performs both linear and state-dependent IRF analysis, providing the tools required by the Counterfactual Monte Carlo (CMC) methodology (Amendola and Pereira (2024) <doi:10.2139/ssrn.4740360>), including state identification and sensitivity. CMC proposes retrieving the causal effect of shocks by exploiting the opportunity to directly observe the counterfactual in a fully controlled experimental setup. LSD (Laboratory for Simulation Development) is free software available at <https://www.labsimdev.org/>).

This package provides a collection of large language model (LLM) text analysis methods designed with psychological data in mind. Currently, LLMing (aka "lemming") includes a text anomaly detection method based on the angle-based subspace approach described by Zhang, Lin, and Karim (2015) <doi:10.1016/j.ress.2015.05.025>.

Uses approximations to compute the natural logarithm of the Gamma function for large values.

This package provides an extension to factors called lfactor that are similar to factors but allows users to refer to lfactor levels by either the level or the label.

This package provides tools to retrieve and summarize taxonomic information and synonymy data for reptile species using data scraped from The Reptile Database website (<https://reptile-database.reptarium.cz/>). Outputs include clean and structured data frames useful for ecological, evolutionary, and conservation research.

This package provides a class that links matrix-like objects (nodes) by rows or by columns while behaving similarly to a base R matrix. Very large matrices are supported if the nodes are file-backed matrices.

This package creates lowpass filters which are commonly used in ion channel recordings. It supports generation of random numbers that are filtered, i.e. follow a model for ion channel recordings, see <doi:10.1109/TNB.2018.2845126>. Furthermore, time continuous convolutions of piecewise constant signals with the kernel of lowpass filters can be computed.

Lattice-based space-filling designs with fill or separation distance properties including interleaved lattice-based minimax distance designs proposed in Xu He (2017) <doi:10.1093/biomet/asx036>, interleaved lattice-based maximin distance designs proposed in Xu He (2018) <doi:10.1093/biomet/asy069>, interleaved lattice-based designs with low fill and high separation distance properties proposed in Xu He (2024) <doi:10.1137/23M156940X>, (sliced) rotated sphere packing designs proposed in Xu He (2017) <doi:10.1080/01621459.2016.1222289> and Xu He (2019) <doi:10.1080/00401706.2018.1458655>, densest packing-based maximum projections designs proposed in Xu He (2020) <doi:10.1093/biomet/asaa057> and Xu He (2018) <doi:10.48550/arXiv.1709.02062>, maximin distance designs for mixed continuous, ordinal, and binary variables proposed in Hui Lan and Xu He (2025) <doi:10.48550/arXiv.2507.23405>, and optimized and regularly repeated lattice-based Latin hypercube designs for large-scale computer experiments proposed in Xu He, Junpeng Gong, and Zhaohui Li (2025) <doi:10.48550/arXiv.2506.04582>.

Data sets exemplifying statistical methods, and some facilitatory utility functions used in ``Analyzing Linguistic Data: A practical introduction to statistics using R'', Cambridge University Press, 2008.

Wrapper functions for the implementation of lagged weighted quantile sum regression, as per Gennings et al (2020) <doi:10.1016/j.envres.2020.109529>.

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 provides a collection of parametric and nonparametric methods for the analysis of survival data. Parametric families implemented include Gompertz-Makeham, exponential and generalized Pareto models and extended models. The package includes an implementation of the nonparametric maximum likelihood estimator for arbitrary truncation and censoring pattern based on Turnbull (1976) <doi:10.1111/j.2517-6161.1976.tb01597.x>, along with graphical goodness-of-fit diagnostics. Parametric models for positive random variables and peaks over threshold models based on extreme value theory are described in Rootzén and Zholud (2017) <doi:10.1007/s10687-017-0305-5>; Belzile et al. (2021) <doi:10.1098/rsos.202097> and Belzile et al. (2022) <doi:10.1146/annurev-statistics-040120-025426>.

This package implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution. Described further in Mantz (2016) <DOI:10.1093/mnras/stv3008>.

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.

An implementation of locally Gaussian distributions. It provides methods for implementing locally Gaussian multivariate density estimation, conditional density estimation, various independence tests for iid and time series data, a test for conditional independence and a test for financial contagion.

Software for computing a log-concave (maximum likelihood) estimator for independent and identically distributed data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).