"Lessons in Statistical Thinking" D.T. Kaplan (2014) <https://dtkaplan.github.io/Lessons-in-statistical-thinking/> is a textbook for a first or second course in statistics that embraces data wrangling, causal reasoning, modeling, statistical adjustment, and simulation. LSTbook supports the student-centered, tidy, pipeline-oriented computing style featured in the book.

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.

Various plots and functions that make use of the lattice/trellis plotting framework. The plots, which include loaPlot(), loaMapPlot() and trianglePlot(), and use panelPal(), a function that extends lattice and hexbin package methods to automate plot subscript and panel-to-panel and panel-to-key synchronization/management.

This package produces a PDF diff of two rmarkdown', quarto', Sweave or TeX files, using the external latexdiff utility.

"Learning with Subset Stacking" is a supervised learning algorithm that is based on training many local estimators on subsets of a given dataset, and then passing their predictions to a global estimator. You can find the details about LESS in our manuscript at <arXiv:2112.06251>.

An implementation of the Log Cumulative Probability Model (LCPM) and Proportional Probability Model (PPM) for which the Maximum Likelihood Estimates are determined using constrained optimization. This implementation accounts for the implicit constraints on the parameter space. Other features such as standard errors, z tests and p-values use standard methods adapted from the results based on constrained optimization.

This package provides a graphical user interface with an integrated diagrammer for latent variable models from the lavaan package. It offers two core functions: first, lavaangui() launches a web application that allows users to specify models by drawing path diagrams, fitting them, assessing model fit, and more; second, plot_lavaan() creates interactive path diagrams from models specified in lavaan'. Karch (2024) <doi: 10.1080/10705511.2024.2420678> contains a tutorial.

This package provides functions to estimate the intensity function and its derivative of a given order of a multiplicative counting process using the local polynomial method.

Generates quotes from Lero Lero', a database for meaningless sentences filled with corporate buzzwords, intended to be used as corporate lorem ipsum (see <http://www.lerolero.com/> for more information). Unfortunately, quotes are currently portuguese-only.

High dimensional longitudinal data analysis with Markov Chain Monte Carlo(MCMC). Currently support mixed effect regression with or without missing observations by considering covariance structures. It provides estimates by missing at random and missing not at random assumptions. In this R package, we present Bayesian approaches that statisticians and clinical researchers can easily use. The functions methodology is based on the book "Bayesian Approaches in Oncology Using R and OpenBUGS" by Bhattacharjee A (2020) <doi:10.1201/9780429329449-14>.

In Latent Space Item Response Models, subjects and items are embedded in a multidimensional Euclidean latent space. As such, interactions among persons, items, and person-item combinations can be revealed that are unmodelled in more conventional item response theory models. This package implements the methods from Molenaar & Jeon (in press) and can be used to fit Latent Space Item Response Models to data using joint maximum likelihood estimation. The package can handle binary data, ordinal data, and data with mixed scales. The package incorporates facilities for data simulation, rotation of the latent space, and K-fold cross-validation to select the number of dimensions of the latent space.

Estimate linear quantile mixtures based on Time-Constant (TC) and/or Time-Varying (TV), discrete, random coefficients.

Analyze graph/network data using L1 centrality and prestige. Functions for deriving global, local, and group L1 centrality/prestige are provided. Routines for visual inspection of a graph/network are also provided. Details are in Kang and Oh (2025a) <doi:10.1080/01621459.2025.2520467>, Kang and Oh (2025b) <doi:10.1080/00031305.2025.2563730>, and Kang (2025) <doi:10.23170/snu.000000188358.11032.0001856>.

This package implements the kK-NN algorithm, an adaptive k-nearest neighbor classifier that adjusts the neighborhood size based on local data curvature. The method estimates local Gaussian curvature by approximating the shape operator of the data manifold. This approach aims to improve classification performance, particularly in datasets with limited samples.

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>.

This package implements a logistic box-cox model. This model is fully described in Xing, L. et al. (2021) <doi:10.1002/cjs.11587>.

Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.

Impute observed values below the limit of detection (LOD) via censored likelihood multiple imputation (CLMI) in single-pollutant models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>. CLMI handles exposure detection limits that may change throughout the course of exposure assessment. lodi provides functions for imputing and pooling for this method.

Helps to render interlinear glossed linguistic examples in html rmarkdown documents and then semi-automatically compiles the list of glosses at the end of the document. It also provides a database of linguistic glosses.

Palettes generated from limnology based field and laboratory photos. Palettes can be used to generate color values to be used in any functions that calls for a color (i.e. ggplot(), plot(), flextable(), etc.).

Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc. REFERENCES i. Akdeniz and Kaciranlar (1995) <doi:10.1080/03610929508831585> ii. Druilhet and Mom (2008) <doi:10.1016/j.jmva.2006.06.011> iii. Imdadullah, Aslam, and Saima (2017) iv. Liu (1993) <doi:10.1080/03610929308831027> v. Liu (2001) <doi:10.1016/j.jspi.2010.05.030>.

Convenient aliases for common ways of misspelling the base R function length(). These include every permutation of the final three letters.

Input latitude and longitude values or an sf/sfc POINT object and get back the time zone in which they exist. Two methods are implemented. One is very fast and uses Rcpp in conjunction with data from the Javascript library (<https://github.com/darkskyapp/tz-lookup-oss/>). This method also works outside of countries borders and in international waters, however speed comes at the cost of accuracy - near time zone borders away from populated centres there is a chance that it will return the incorrect time zone. The other method is slower but more accurate - it uses the sf package to intersect points with a detailed map of time zones from here: <https://github.com/evansiroky/timezone-boundary-builder/>. The package also contains several utility functions for helping to understand and visualize time zones, such as listing of world time zones, including information about daylight savings times and their offsets from UTC. You can also plot a time zone to visualize the UTC offset over a year and when daylight savings times are in effect.

This package provides a bioinformatics pipeline for performing taxonomic assignment of DNA metabarcoding sequence data while considering geographic location. A detailed tutorial is available at <https://urodelan.github.io/Local_Taxa_Tool_Tutorial/>. A manuscript describing these methods is in preparation.