Extracts zip files, converts Word', Excel', and html'/'htm files to pdf format. Word and Excel conversion uses VBScript', while html'/'htm conversion uses webshot and PhantomJS'. Additionally, the package merges pdf files into a single document. This package is only supported on Windows due to VBScript dependencies.

The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.

This package provides methods for linear regression in the presence of missing data, including missingness in covariates and responses. The package implements two estimators: oss_estimator(), a low-dimensional semi-supervised method, and dantzig_missing(), a high-dimensional approach. The tuning parameter can be selected automatically via cv_dantzig_missing(). See Risebrow and Berrett (2026) <doi:10.48550/arXiv.2602.13729>. Optional support for the gurobi optimizer via the gurobi R package (available from Gurobi, see <https://docs.gurobi.com/projects/optimizer/en/current/reference/r.html>).

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.

Fast implementations to compute the genetic covariance matrix, the Jaccard similarity matrix, the s-matrix (the weighted Jaccard similarity matrix), and the (classic or robust) genomic relationship matrix of a (dense or sparse) input matrix (see Hahn, Lutz, Hecker, Prokopenko, Cho, Silverman, Weiss, and Lange (2020) <doi:10.1002/gepi.22356>). Full support for sparse matrices from the R-package Matrix'. Additionally, an implementation of the power method (von Mises iteration) to compute the largest eigenvector of a matrix is included, a function to perform an automated full run of global and local correlations in population stratification data, a function to compute sliding windows, and a function to invert minor alleles and to select those variants/loci exceeding a minimal cutoff value. New functionality in locStra allows one to extract the k leading eigenvectors of the genetic covariance matrix, Jaccard similarity matrix, s-matrix, and genomic relationship matrix via fast PCA without actually computing the similarity matrices. The fast PCA to compute the k leading eigenvectors can now also be run directly from bed'+'bim'+'fam files.

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.

Affords an alternative, vector-based syntax to lavaan', as well as other convenience functions such as naming paths and defining indirect links automatically, in addition to convenience formatting optimized for a publication and script sharing workflow.

Dieses R-Paket stellt Zusatzmaterial in Form von Daten, Funktionen und R-Hilfe-Seiten für den Herausgeberband Breit, S. und Schreiner, C. (Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung." Wien: facultas. (ISBN: 978-3-7089-1343-8, <https://www.iqs.gv.at/themen/bildungsforschung/publikationen/veroeffentlichte-publikationen>) zur Verfügung.

Short for linear binning', the linbin package provides functions for manipulating, binning, and plotting linearly referenced data. Although developed for data collected on river networks, it can be used with any interval or point data referenced to a 1-dimensional coordinate system. Flexible bin generation and batch processing makes it easy to compute and visualize variables at multiple scales, useful for identifying patterns within and between variables and investigating the influence of scale of observation on data interpretation.

Estimate and confidence/credible intervals for an unknown regressor x0 given an observed y0.

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>.

LineUp is an interactive technique designed to create, visualize and explore rankings of items based on a set of heterogeneous attributes. This is a htmlwidget wrapper around the JavaScript library LineUp.js'. It is designed to be used in R Shiny apps and R Markddown files. Due to an outdated webkit version of RStudio it won't work in the integrated viewer.

Conducts a cointegration test for high-dimensional vector autoregressions (VARs) of order k based on the large N,T asymptotics of Bykhovskaya and Gorin, 2022 (<doi:10.48550/arXiv.2202.07150>). The implemented test is a modification of the Johansen likelihood ratio test. In the absence of cointegration the test converges to the partial sum of the Airy-1 point process. This package contains simulated quantiles of the first ten partial sums of the Airy-1 point process that are precise up to the first three digits.

Handling, processing, and analyzing geographic data on species distributions and environmental variables. Read Vilela & Villalobos (2015) <doi:10.1111/2041-210X.12401> for details.

Given a postulated model and a set of data, the comparison density is estimated and the deviance test is implemented in order to assess if the data distribution deviates significantly from the postulated model. Finally, the results are summarized in a CD-plot as described in Algeri S. (2019) <arXiv:1906.06615>.

This package provides R bindings to the llama.cpp library for running large language models. The package uses a lightweight architecture where the C++ backend library is downloaded at runtime rather than bundled with the package. Package features include text generation, reproducible generation, and parallel inference.

The Length-Biased Power Garima distribution for computes the probability density, the cumulative density distribution and the quantile function of the distribution, and generates sample values with random variables based on Kittipong and Sirinapa(2021)<DOI: 10.14456/sjst-psu.2021.89>.

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.

Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding. However, classical methods rely on strong assumptions such as the exclusion criterion, which states that instrumental effects must be entirely mediated by treatments. In the so-called "leaky" IV setting, candidate instruments are allowed to have some direct influence on outcomes, rendering the average treatment effect (ATE) unidentifiable. But with limits on the amount of information leakage, we may still recover sharp bounds on the ATE, providing partial identification. This package implements methods for ATE bounding in the leaky IV setting with linear structural equations. For details, see Watson et al. (2024) <doi:10.48550/arXiv.2404.04446>.

Determining consensus seriations for binary incidence matrices, using a two-step process of Procrustes-fit correspondence analysis for heuristic selection of partial seriations and iterative regression to establish a single consensus. Contains the Lakhesis Calculator, a graphical platform for identifying seriated sequences. Collins-Elliott (2026) "Lakhesis: Consensus Seriation via Iterative Regression of Partial Rankings for Binary Data" <doi:10.1080/02664763.2026.2672564>.

This package provides flexible but lightweight logging facilities for R scripts. Supports priority levels for logs and messages, flagging messages, capturing script output, switching logs, and logging to files or connections.

An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.

Routines for fitting Logic Regression models. Logic Regression is described in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005) <DOI:10.1002/gepi.20042>.

This package provides functions for summarizing, visualizing, and analyzing Likert-scale survey data. Includes support for computing descriptive statistics, Relative Importance Index (RII), reliability analysis (Cronbach's Alpha), and response distribution plots.