This package provides functions to identify plausible and replicable factor structures for a set of variables via k-fold cross validation. The process combines the exploratory and confirmatory factor analytic approach to scale development (Flora & Flake, 2017) <doi:10.1037/cbs0000069> with a cross validation technique that maximizes the available data (Hastie, Tibshirani, & Friedman, 2009) <isbn:978-0-387-21606-5>. Also available are functions to determine k by drawing on power analytic techniques for covariance structures (MacCallum, Browne, & Sugawara, 1996) <doi:10.1037/1082-989X.1.2.130>, generate model syntax, and summarize results in a report.

Density, distribution function, quantile function and random generation for the K-distribution. A plotting function that plots data on Weibull paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc.

To test if a tensor time series following a Tucker-decomposition factor model has a Kronecker product structure. Supplementary functions for tensor reshape and its reversal are also included.

Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.

This package provides functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <arXiv:2112.06266>.

Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <doi:10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <doi:10.1016/j.socnet.2009.04.001>.

This package provides a framework for integrating Large Language Models (LLMs) with R programming through workflow automation. Built on the ReAct (Reasoning and Acting) architecture, enables bi-directional communication between LLMs and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management.

Summarizes characteristics of linear mixed effects models without data or a fitted model by converting code for fitting lmer() from lme4 and lme() from nlme into tables, equations, and visuals. Outputs can be used to learn how to fit linear mixed effects models in R and to communicate about these models in presentations, manuscripts, and analysis plans.

This package provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book <https://book-data-science-r.netlify.app>, making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver", symbolizing deep and immersive engagement with knowledge.

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

This package implements Latent Unknown Clusters By Integrating Multi-omics Data (LUCID; Peng (2019) <doi:10.1093/bioinformatics/btz667>) for integrative clustering with exposures, multi-omics data, and health outcomes. Supports three integration strategies: early, parallel, and serial. Provides model fitting and tuning, lasso-type regularization for exposure and omics feature selection, handling of missing data, including both sporadic and complete-case patterns, prediction, and g-computation for estimating causal effects of exposures, bootstrap inference for uncertainty estimation, and S3 summary and plot methods. For the multi-omics integration framework, see Jia (2024) <https://journal.r-project.org/articles/RJ-2024-012/RJ-2024-012.pdf>. For the missing-data imputation mechanism, see Jia (2024) <doi:10.1093/bioadv/vbae123>.

Maximum likelihood estimation of log-binomial regression with special functionality when the MLE is on the boundary of the parameter space.

Classical hierarchical clustering algorithms, agglomerative and divisive clustering. Algorithms are implemented as a theoretical way, step by step. It includes some detailed functions that explain each step. Every function allows options to get different results using different techniques. The package explains non expert users how hierarchical clustering algorithms work.

Estimating causal parameters in the presence of treatment spillover is of great interest in statistics. This package provides tools for instrumental variables estimation of average causal effects under network interference of unknown form. The target parameters are the local average direct effect, the local average indirect effect, the local average overall effect, and the local average spillover effect. The methods are developed by Hoshino and Yanagi (2023) <doi:10.48550/arXiv.2108.07455>.

User-friendly and generalized tools for the calculation of luck -- moments of variation in metrics like lifespan and lifetime reproductive output. We provide tools for calculating those moments and also performing decompositions into contributions from, for example, individual traits, environmental impacts, and luck (also called individual stochasticity). The functions included here are based on Snyder and Ellner (2024) <doi:10.1086/730557>, Cochran and Ellner (1992) <https://www.jstor.org/stable/2937115>, and Hernandez et al. (2024) <doi:10.1111/ele.14390>.

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 contains functions to estimate a penalized regression model using 3CoSE algorithm, see Weber, Striaukas, Schumacher Binder (2018) <doi:10.2139/ssrn.3211163>.

Combines Latent Dirichlet Allocation (LDA) and Bayesian multinomial time series methods in a two-stage analysis to quantify dynamics in high-dimensional temporal data. LDA decomposes multivariate data into lower-dimension latent groupings, whose relative proportions are modeled using generalized Bayesian time series models that include abrupt changepoints and smooth dynamics. The methods are described in Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993>, Western and Kleykamp (2004) <doi:10.1093/pan/mph023>, Venables and Ripley (2002, ISBN-13:978-0387954578), and Christensen et al. (2018) <doi:10.1002/ecy.2373>.

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 classes and methods for objects, whose indexing naturally starts from zero. Subsetting, indexing and mathematical operations are defined naturally between lagged objects and lagged and base R objects. Recycling is not used, except for singletons. The single bracket operator doesn't drop dimensions by default.

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>.

Identifying latent genetic interactions in genome-wide association studies using the Latent Interaction Testing (LIT) framework. LIT is a flexible kernel-based approach that leverages information across multiple traits to detect latent genetic interactions without specifying or observing the interacting variable (e.g., environment). LIT accepts standard PLINK files as inputs to analyze large genome-wide association studies.

In the generalized Roy model, the marginal treatment effect (MTE) can be used as a building block for constructing conventional causal parameters such as the average treatment effect (ATE) and the average treatment effect on the treated (ATT). Given a treatment selection equation and an outcome equation, the function mte() estimates the MTE via the semiparametric local instrumental variables method or the normal selection model. The function mte_at() evaluates MTE at different values of the latent resistance u with a given X = x, and the function mte_tilde_at() evaluates MTE projected onto the estimated propensity score. The function ace() estimates population-level average causal effects such as ATE, ATT, or the marginal policy relevant treatment effect.