Facilitates access to the Comparative Legislators Database (CLD). The CLD includes political, sociodemographic, career, online presence, public attention, and visual information for over 67,000 contemporary and historical politicians from 16 countries.

The leaflet JavaScript library provides many plugins some of which are available in the core leaflet package, but there are many more. It is not possible to support them all in the core leaflet package. This package serves as an add-on to the leaflet package by providing extra functionality via leaflet plugins.

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

Exact significance tests for a changepoint in linear or multiple linear regression. Confidence regions with exact coverage probabilities for the changepoint. Based on Knowles, Siegmund and Zhang (1991) <doi:10.1093/biomet/78.1.15>.

This package provides the tables from the Sean Lahman Baseball Database as a set of R data.frames. It uses the data on pitching, hitting and fielding performance and other tables from 1871 through 2024, as recorded in the 2025 version of the database. Documentation examples show how many baseball questions can be investigated.

Computes the probability density function, the cumulative distribution function, the hazard rate function, the quantile function and random generation for Lindley Power Series distributions, see Nadarajah and Si (2018) <doi:10.1007/s13171-018-0150-x>.

Reads raw files from Li-COR gas analyzers and produces a dataframe that can directly be used with fluxible <https://cran.r-project.org/package=fluxible>.

This package provides a graph proposed by Rosenbaum is useful for checking some properties of various sorts of latent scale, this program generates commands to obtain the graph using dot from graphviz'.

Automatically install, update, and load CRAN', GitHub', and Bioconductor packages in a single function call. By accepting bare unquoted names for packages, it's easy to add or remove packages from the list.

Data sets for Chirok Han (2024, ISBN:979-11-303-1964-3, "Lectures on Econometrics"). Students, teachers, and self-learners will find the data sets essential for replicating the results in the book.

Adds smoothing spline modelling capability to nlme. Fits smoothing spline terms in Gaussian linear and nonlinear mixed-effects models.

The least-squares Monte Carlo (LSM) simulation method is a popular method for the approximation of the value of early and multiple exercise options. LSMRealOptions provides implementations of the LSM simulation method to value American option products and capital investment projects through real options analysis. LSMRealOptions values capital investment projects with cash flows dependent upon underlying state variables that are stochastically evolving, providing analysis into the timing and critical values at which investment is optimal. LSMRealOptions provides flexibility in the stochastic processes followed by underlying assets, the number of state variables, basis functions and underlying asset characteristics to allow a broad range of assets to be valued through the LSM simulation method. Real options projects are further able to be valued whilst considering construction periods, time-varying initial capital expenditures and path-dependent operational flexibility including the ability to temporarily shutdown or permanently abandon projects after initial investment has occurred. The LSM simulation method was first presented in the prolific work of Longstaff and Schwartz (2001) <doi:10.1093/rfs/14.1.113>.

Spatial and spatio-temporal modelling of point patterns using the log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal, multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.

Implementations of most of the existing proximity-based methods of link prediction in graphs. Among the 20 implemented methods are e.g.: Adamic L. and Adar E. (2003) <doi:10.1016/S0378-8733(03)00009-1>, Leicht E., Holme P., Newman M. (2006) <doi:10.1103/PhysRevE.73.026120>, Zhou T. and Zhang Y (2009) <doi:10.1140/epjb/e2009-00335-8>, and Fouss F., Pirotte A., Renders J., and Saerens M. (2007) <doi:10.1109/TKDE.2007.46>.

Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2019) <doi:10.1080/01621459.2018.1520115>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.

Allows researchers to conduct multivariate statistical analyses of survey data with list experiments. This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, Blair and Imai (2012) <doi:10.1093/pan/mpr048>, Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>, Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, Chou, Imai, and Rosenfeld (2017) <doi:10.1177/0049124117729711>, and Blair, Chou, and Imai (2018) <https://imai.fas.harvard.edu/research/files/listerror.pdf>. This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, a method for combining list experiments with direct questions, and methods for diagnosing and adjusting for response error. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions.

This package provides tools to generate and analyze 3-level linear factorial block designs, including complete factorial layouts, fractional factorial layouts, confounded factorial layouts, and design-characteristic summaries. The package includes utilities for recursive ternary construction, defining-contrast identification, alias/confounding summaries, incidence matrix construction, and design optimality diagnostics.The methodological framework follows foundational work on Gupta (1983) <doi:10.1111/j.2517-6161.1983.tb01253.x>. These methods assist in selecting, comparing, and studying factorial and fractional factorial block designs for large experimental situations.

The leader clustering algorithm provides a means for clustering a set of data points. Unlike many other clustering algorithms it does not require the user to specify the number of clusters, but instead requires the approximate radius of a cluster as its primary tuning parameter. The package provides a fast implementation of this algorithm in n-dimensions using Lp-distances (with special cases for p=1,2, and infinity) as well as for spatial data using the Haversine formula, which takes latitude/longitude pairs as inputs and clusters based on great circle distances.

This package implements the Linear Approach to Threshold with Ergodic Rate (LATER) model, which predicts distributions of reaction times and summarises them with as little as two free parameters. Allows for easy visualisation and comparison of datasets, along with fitting of datasets using the LATER model.

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>.

This package provides methods for fitting log-link GLMs and GAMs to binomial data, including EM-type algorithms with more stable convergence properties than standard methods.

Life and Fertility Tables are appropriate to study the dynamics of arthropods populations. This package provides utilities for constructing Life Tables and Fertility Tables, related demographic parameters, and some simple graphs of interest. It also offers functions to transform the obtained data into a known format for better manipulation. In addition, two methods for obtaining the confidence interval are included.

This package performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.

Lake morphometry metrics are used by limnologists to understand, among other things, the ecological processes in a lake. Traditionally, these metrics are calculated by hand, with planimeters, and increasingly with commercial GIS products. All of these methods work; however, they are either outdated, difficult to reproduce, or require expensive licenses to use. The lakemorpho package provides the tools to calculate a typical suite of these metrics from an input elevation model and lake polygon. The metrics currently supported are: fetch, major axis, minor axis, major/minor axis ratio, maximum length, maximum width, mean width, maximum depth, mean depth, shoreline development, shoreline length, surface area, and volume.