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.

This package provides methods for assessing agreement between repeated measurements obtained by two or more methods using the longitudinal concordance correlation coefficient (LCC). Polynomial mixed-effects models (via nlme') describe how concordance, Pearson correlation and accuracy evolve over time. Functions are provided for model fitting, diagnostic plots, extraction of summaries, and non-parametric bootstrap confidence intervals (including parallel computation), following Oliveira et al. (2018) <doi:10.1007/s13253-018-0321-1>.

An implementation of the Input-Output model developed by Wassily Leontief that represents the interdependencies between different sectors of a national economy or different regional economies.

Calculates Land Surface Temperature from Landsat band 10 and 11. Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data. Jimenez-Munoz JC, Cristobal J, Sobrino JA, et al (2009). <doi: 10.1109/TGRS.2008.2007125>. Land surface temperature retrieval from LANDSAT TM 5. Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004). <doi:10.1016/j.rse.2004.02.003>. Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data. Srivastava PK, Majumdar TJ, Bhattacharya AK (2009). <doi: 10.1016/j.asr.2009.01.023>. Mapping land surface emissivity from NDVI: Application to European, African, and South American areas. Valor E (1996). <doi:10.1016/0034-4257(96)00039-9>. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. Van de Griend AA, Owe M (1993). <doi:10.1080/01431169308904400>. Land Surface Temperature Retrieval from Landsat 8 TIRSâ Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method. Yu X, Guo X, Wu Z (2014). <doi:10.3390/rs6109829>. Calibration and Validation of land surface temperature for Landsat8-TIRS sensor. Land product validation and evolution. SkokoviÄ D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C, Cristóbal J. (2014).

This package provides tools for common operations on lists. Provided are short-cuts to operations like selecting and merging data stored in lists. The functions in this package are designed to be used with pipes.

This package provides methods of developing linear time series modelling. Methods are given for loglikelihood computation, forecasting and simulation.

Set of the data science tools created by various members of the Long Term Ecological Research (LTER) community. These functions were initially written largely as standalone operations and have later been aggregated into this package.

This package performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.

This package provides functions for estimating the gliding box lacunarity (GBL), covariance, and pair-correlation of a random closed set (RACS) in 2D from a binary coverage map (e.g. presence-absence land cover maps). Contains a number of newly-developed covariance-based estimators of GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and balanced estimators, proposed by Picka (2000) <http://www.jstor.org/stable/1428408>, for covariance, centred covariance, and pair-correlation. Also contains methods for estimating contagion-like properties of RACS and simulating 2D Boolean models. Binary coverage maps are usually represented as raster images with pixel values of TRUE, FALSE or NA, with NA representing unobserved pixels. A demo for extracting such a binary map from a geospatial data format is provided. Binary maps may also be represented using polygonal sets as the foreground, however for most computations such maps are converted into raster images. The package is based on research conducted during the author's PhD studies.

Convert Leaf Area Index (LAI) from the Normalized Difference Vegetation Index (NDVI) using available equations from literature. Detailed description of conversion equations in Bajocco et al. 2022 <doi:10.3390/rs14153554>.

Lights Out is a puzzle game consisting of a grid of lights that are either on or off. Pressing any light will toggle it and its adjacent lights. The goal of the game is to switch all the lights off. This package provides an interface to play the game on different board sizes, both through the command line or with a visual application. Puzzles can also be solved using the automatic solver included. View a demo online at <https://daattali.com/shiny/lightsout/>.

Enables users to handle the dataset cleaning for conducting specific analyses with the log files from two international educational assessments: the Programme for International Student Assessment (PISA, <https://www.oecd.org/pisa/>) and the Programme for the International Assessment of Adult Competencies (PIAAC, <https://www.oecd.org/skills/piaac/>). An illustration of the analyses can be found on the LOGAN Shiny app (<https://loganpackage.shinyapps.io/shiny/>) on your browser.

Access to the Greek New Testament (27 books) and the Old Testament (39 books) and allow users to do textual analysis on the data. The New and Old Testament have been provided in their original languages, Greek and Hebrew, respectively. Additionally, the Revised American Standard Bible is also provided for users who'd rather use a wordâ forâ word modern English translation.

This package provides two methods of estimating income inequality statistics from binned income data, such as the income data provided in the Census. These methods use different interpolation techniques to infer the distribution of incomes within income bins. One method is an implementation of Jargowsky and Wheeler's mean-constrained integration over brackets (MCIB). The other method is based on a new technique, Lorenz interpolation, which estimates income inequality by constructing an interpolated Lorenz curve based on the binned income data. These methods can be used to estimate three income inequality measures: the Gini (the default measure returned), the Theil, and the Atkinson's index. Jargowsky and Wheeler (2018) <doi:10.1177/0081175018782579>.

An adaption of the consensus clustering approach from ConsensusClusterPlus for longitudinal data. The longitudinal data is clustered with flexible mixture models from flexmix', while the consensus matrices are hierarchically clustered as in ConsensusClusterPlus'. By using the flexibility from flexmix and FactoMineR', one can use mixed data types for the clustering.

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>.

Converts table-like objects to stand-alone PDF or PNG. Can be used to embed tables and arbitrary content in PDF or Word documents. Provides a low-level R interface for creating LaTeX code, e.g. command() and a high-level interface for creating PDF documents, e.g. as.pdf.data.frame(). Extensive customization is available via mid-level functions, e.g. as.tabular(). See also package?latexpdf'. Support for PNG is experimental; see as.png.data.frame'. Adapted from metrumrg <https://r-forge.r-project.org/R/?group_id=1215>. Requires a compatible installation of pdflatex', e.g. <https://miktex.org/>.

Estimates group transmission assortativity coefficients from transmission trees. Group transmission assortativity coefficients measure the tendency for individuals to transmit within their own group (e.g. age group, vaccination status, or location) compared to other groups. The package requires information on who infected whom, group membership for all individuals, and the relative sizes of each group in the population. For more details see Geismar et al. (2024) <doi:10.1371/journal.pone.0313037>.

This package provides a unified latent class modeling framework that encompasses both latent class analysis (LCA) and latent profile analysis (LPA), offering a one-stop solution for latent class modeling. It implements state-of-the-art parameter estimation methods, including the expectationâ maximization (EM) algorithm, neural network estimation (NNE; requires users to have Python and its dependent libraries installed on their computer), and integration with Mplus (requires users to have Mplus installed on their computer). In addition, it provides commonly used model fit indices such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), as well as classification accuracy measures such as entropy. The package also includes fully functional likelihood ratio tests (LRT) and bootstrap likelihood ratio tests (BLRT) to facilitate model comparison, along with bootstrap-based and observed information matrix-based standard error estimation. Furthermore, it supports the standard three-step approach for LCA, LPA, and latent transition analysis (LTA) with covariates, enabling detailed covariate analysis. Finally, it includes several user-friendly auxiliary functions to enhance interactive usability.

Perform two linear combination methods for biomarkers: (1) Empirical performance optimization for specificity (or sensitivity) at a controlled sensitivity (or specificity) level of Huang and Sanda (2022) <doi:10.1214/22-aos2210>, and (2) weighted maximum score estimator with empirical minimization of averaged false positive rate and false negative rate. Both adopt the algorithms of Huang and Sanda (2022) <doi:10.1214/22-aos2210>. MOSEK solver is used and needs to be installed; an academic license for MOSEK is free.

This package provides a unified interface for interacting with Large Language Models (LLMs) through various providers including OpenAI <https://platform.openai.com/docs/api-reference>, Ollama <https://ollama.com/>, and other OpenAI-compatible APIs. Features include automatic connection testing, max_tokens limit auto-adjustment, structured JSON responses with schema validation, interactive JSON schema generation, prompt templating, and comprehensive diagnostics.

This package provides a shiny application to automate forward and back survey translation with optional reconciliation using large language models (LLMs). Supports both item-by-item and batch translation modes for optimal performance and context-aware translations. Handles multi-sheet Excel files and supports OpenAI (GPT), Google Gemini, and Anthropic Claude models. Follows the TRAPD (Translation, Review, Adjudication, Pretesting, Documentation) framework and ISPOR (International Society for Pharmacoeconomics and Outcomes Research) recommendations. See Harkness et al. (2010) <doi:10.1002/9780470609927.ch7> and Wild et al. (2005) <doi:10.1111/j.1524-4733.2005.04054.x>.

Simulate lobster catch process in a trap fishery. Factors such as lobster density on ocean floor, their movement, trap saturation and bait shrinkage rate can be modeled. Details of the methods for modeling those processes can be found in: Addison and Bell (1997) <doi:10.1071/MF97169>.

Fit the log binomial regression model (LBM) by Exact method. Limited parameter space of LBM causes trouble to find admissible estimates and fail to converge when MLE is close to or on the boundary of space. Exact method utilizes the property of boundary vectors to re-parametrize the model without losing any information, and fits the model on the standard fitting algorithm with no convergence issues.