Download and read data on lobbying in the United States Congress. Data is queried from the Senate's Application Programming Interface (<https://lda.senate.gov/api/>). This supports filings since 2008. Functions exist for all primary data endpoints, including queries by filings, contributions, registrations, clients, and lobbyists.

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

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.

Compute and visualize using the visNetwork package all the bivariate correlations of a dataframe. Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau, distance correlation, maximal information coefficient and equal-freq discretization-based maximal normalized mutual information) are used according to the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).

Due to lack of proper inference procedure and software, the ordinary linear regression model is seldom used in practice for the analysis of right censored data. This paper presents an S-Plus/R program that implements a recently developed inference procedure (Jin, Lin and Ying, 2006) <doi:10.1093/biomet/93.1.147> for the accelerated failure time model based on the least-squares principle.

This package provides tools to authenticate with LOBSTER (Limit Order Book System - The Efficient Reconstruction, <https://app.lobsterdata.com/>), request, download, and process high-frequency limit order book data. Streamlines the end-to-end workflow from data request to analysis-ready datasets. For advanced high-frequency econometric analysis, see the highfrequency package.

Fit linear models based on periodic splines, moderate model coefficients using multivariate adaptive shrinkage, then compute properties of the moderated curves.

Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2020) <doi:10.1186/s12859-019-3324-1>.

An approach to analyzing Likert response items, with an emphasis on visualizations. The stacked bar plot is the preferred method for presenting Likert results. Tabular results are also implemented along with density plots to assist researchers in determining whether Likert responses can be used quantitatively instead of qualitatively. See the likert(), summary.likert(), and plot.likert() functions to get started.

Render nested process execution as a live, colored tree in the console, with tree connectors, status glyphs, and elapsed time per step. Nesting depth is tracked via frame exit handlers so it never desynchronizes, even when a step errors. Builds on the cli package for console rendering.

We provide a solution for performing permutation tests on linear and mixed linear regression models. It allows users to obtain accurate p-values without making distributional assumptions about the data. By generating a null distribution of the test statistics through repeated permutations of the response variable, permutation tests provide a powerful alternative to traditional parameter tests (Holt et al. (2023) <doi:10.1007/s10683-023-09799-6>). In this early version, we focus on the permutation tests over observed t values of beta coefficients, i.e.original t values generated by parameter tests. After generating a null distribution of the test statistic through repeated permutations of the response variable, each observed t values would be compared to the null distribution to generate a p-value. To improve the efficiency,a stop criterion (Anscombe (1953) <doi:10.1111/j.2517-6161.1953.tb00121.x>) is adopted to force permutation to stop if the estimated standard deviation of the value falls below a fraction of the estimated p-value. By doing so, we avoid the need for massive calculations in exact permutation methods while still generating stable and accurate p-values.

This package implements the latent true-score and target-population anchored geometric standardized mean difference (LTG-SMD) framework for two-group effect-size analysis. Provides plug-in estimation, analytic delta-method confidence intervals using a sample fourth-moment plug-in, bias-corrected and bias-corrected-accelerated nonparametric bootstrap confidence intervals with study x group stratification, denominator-sensitivity profiles, and multi-site meta-analytic wrappers. Includes denominator-diagnostic reporting, a pluggable reliability estimator interface, and an explicit interface for specifying the target reference distribution. Companion software to Nakamura (2026) "The Denominator Chooses the Estimand: A Target-Population True-Score Framework for Standardized Mean Differences" <doi:10.1037/met0000875>.

Whole-buffer DEFLATE-based compression and decompression of raw vectors using the libdeflate library (see <https://github.com/ebiggers/libdeflate>). Provides the user with additional control over the speed and the quality of DEFLATE compression compared to the fixed level of compression offered in R's memCompress() function. Also provides the libdeflate static library and C headers along with a CMake target and packageâ config file that ease linking of libdeflate in packages that compile and statically link bundled libraries using CMake'.

This package provides a fast, lightweight, object-oriented approach to logging based on the widely-emulated log4j system and etymology. Loggers write messages to one or more destinations, such as the console, a file, or a remote server, using a format you control. Messages below the chosen severity threshold are discarded cheaply, so logging calls can be left in production code.

Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs), distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and maximum product spacings estimation are available. Right-tail and left-tail LM censoring by threshold or indicator variable are available. LMs of residual (resid) and reversed (rev) residual life are implemented along with 13 quantile operators for reliability analyses. Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available. Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L], Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L], 3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L], Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L].

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 implements the letter value boxplot which extends the standard boxplot to deal with both larger and smaller number of data points by dynamically selecting the appropriate number of letter values to display.

This package contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>.

Extracts decision rules from tree- and rule-based models fitted in R'. Rules are expressed as logical predicates that identify paths to terminal nodes, making model behavior more transparent and interpretable. Provides conversion methods to partykit party objects for a wide range of model types. The partykit infrastructure is described in Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.

This package contains different algorithms and construction methods for optimal Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Detailed comparisons and summary of all the algorithms and construction methods in this package can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments.

Companion toolbox for structural equation models fitted with lavaan'. Provides post-estimation diagnostics and graphics that operate directly on a fitted object using its estimates and covariance, and refits auxiliary models when needed. The package relies on lavaan (Rosseel, 2012) <doi:10.18637/jss.v048.i02>.

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>.

Scoring functions for widely used loneliness measures, with response keys and reverse-scoring drawn from the original scale manuals. Covers the UCLA Loneliness Scale Version 3 and its three-item short form, the de Jong Gierveld 11- and 6-item scales, the Children's Loneliness and Social Dissatisfaction Scale, and the short Social and Emotional Loneliness Scale for Adults. Out-of-range responses raise a warning, and missing-data rules follow each scale's manual.

Curated datasets from US Long Term Ecological Research sites.