This package provides functions for vectorised conditional recoding of variables. case_when() enables you to vectorise multiple if and else statements (like CASE WHEN in SQL'). if_else() is a stricter and more predictable version of ifelse() in base that preserves attributes. These functions are forked from dplyr with all package dependencies removed and behave identically to the originals.

Client for programmatic access to the Lake Multi-scaled Geospatial and Temporal database <https://lagoslakes.org>, with functions for accessing lake water quality and ecological context data for the US.

Implementation of a theoretically supported alternative to k-nearest neighbors for functional data to solve problems of estimating unobserved segments of a partially observed functional data sample, functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. The package allows the implementation of the methodology and the replication of the results in Elà as, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.

An HTML widget that randomly tours 2D projections of numerical data. A random walk through projections of the data is shown. The user can manipulate the plot to use specified axes, or turn on Guided Tour mode to find an informative projection of the data. Groups within the data can be hidden or shown, as can particular axes. Points can be brushed, and the selection can be linked to other widgets using crosstalk. The underlying method to produce the random walk and projection pursuit uses Langevin dynamics. The widget can be used from within R, or included in a self-contained R Markdown or Quarto document or presentation, or used in a Shiny app.

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>.

This package provides a wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.

Studies that report shifts in species distributions may be biased by the shape of the study area. The main functionality of this package is to calculate the Latitudinal Bias Index (LBI) for any given shape. The LBI is bounded between +1 (100% probability to exclusively record latitudinal shifts, i.e., range shifts data sampled along a perfectly South-North oriented straight line) and -1 (100% probability to exclusively record longitudinal shifts, i.e., range shifts data sampled along a perfectly East-West oriented straight line).

This package provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions.

The primary purpose of lavaan.mi is to extend the functionality of the R package lavaan', which implements structural equation modeling (SEM). When incomplete data have been multiply imputed, the imputed data sets can be analyzed by lavaan using complete-data estimation methods, but results must be pooled across imputations (Rubin, 1987, <doi:10.1002/9780470316696>). The lavaan.mi package automates the pooling of point and standard-error estimates, as well as a variety of test statistics, using a familiar interface that allows users to fit an SEM to multiple imputations as they would to a single data set using the lavaan package.

Change-point detection algorithm with label constraints and a penalty for each change outside of labels. Read TD Hocking, A Srivastava (2023) <doi:10.1007/s00180-022-01238-z> for details.

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'.

Plots empty Lexis grids, adds lifelines and highlights certain areas of the grid, like cohorts and age groups.

Estimation of the local false discovery rate using the method of moments.

This package provides a shiny application to construct age-specific life tables and fertility schedules from individual female daily egg records. The application computes age-specific survival and fertility functions and estimates key demographic parameters including the net reproductive rate, mean generation time, intrinsic rate of increase, finite rate of increase and doubling time. Optional confidence intervals can be obtained using percentile bootstrap or delete-1 jackknife resampling at the female level. Methods and definitions follow Stevens (2009) <doi:10.1007/978-0-387-89882-7> and Rossini et al. (2024) <doi:10.1371/journal.pone.0299598>.

This package provides a set of functions to locate some programs available on the user machine. The package provides functions to locate Node.js', npm', LibreOffice', Microsoft Word', Microsoft PowerPoint', Microsoft Excel', Python', pip', Mozilla Firefox and Google Chrome'. User can test the availability of a program with eventually a version and call it with function system2() or system(). This allows the use of a single function to retrieve the path to a program regardless of the operating system and its configuration.

This package provides functions for the longitudinal genetic random field method (He et al., 2015, <doi:10.1111/biom.12310>) to test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region.

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.

Likelihood-based estimation of individual growth and sexual maturity models for organisms, usually fish and invertebrates. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions.

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>.

Implementation of several phenotype-based family genetic risk scores with unified input data and data preparation functions to help facilitate the required data preparation and management. The implemented family genetic risk scores are the extended liability threshold model conditional on family history from Pedersen (2022) <doi:10.1016/j.ajhg.2022.01.009> and Pedersen (2023) <https://www.nature.com/articles/s41467-023-41210-z>, Pearson-Aitken Family Genetic Risk Scores from Krebs (2024) <doi:10.1016/j.ajhg.2024.09.009>, and family genetic risk score from Kendler (2021) <doi:10.1001/jamapsychiatry.2021.0336>.

When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.

This package provides functions to calculate lunar and other related environmental covariates.

This package provides a unified interface to large language models across multiple providers. Supports text generation, structured output with optional JSON Schema validation, and embeddings. Includes tidyverse-friendly helpers, chat session, consistent error handling, and parallel batch tools.

Rapid satellite data streams in operational applications have clear benefits for monitoring land cover, especially when information can be delivered as fast as changing surface conditions. Over the past decade, remote sensing has become a key tool for monitoring and predicting environmental variables by using satellite data. This package presents the main applications in remote sensing for land surface monitoring and land cover mapping (soil, vegetation, water...). Tomlinson, C.J., Chapman, L., Thornes, E., Baker, C (2011) <doi:10.1002/met.287>.