This package provides a collection of parametric and nonparametric methods for the analysis of survival data, described in <doi:10.32614/RJ-2025-034>. Parametric families implemented include Gompertz-Makeham, exponential and generalized Pareto models and extended models. The package includes an implementation of the nonparametric maximum likelihood estimator for arbitrary truncation and censoring pattern based on Turnbull (1976) <doi:10.1111/j.2517-6161.1976.tb01597.x>, along with graphical goodness-of-fit diagnostics. Parametric models for positive random variables and peaks over threshold models based on extreme value theory are described in Rootzén and Zholud (2017) <doi:10.1007/s10687-017-0305-5>; Belzile et al. (2021) <doi:10.1098/rsos.202097> and Belzile et al. (2022) <doi:10.1146/annurev-statistics-040120-025426>.

This package provides smooth approximations to the L0 norm penalty for estimating sparse Gaussian graphical models (GGMs). Network estimation is performed using the Local Linear Approximation (LLA) framework (Fan & Li, 2001 <doi:10.1198/016214501753382273>; Zou & Li, 2008 <doi:10.1214/009053607000000802>) with five penalty functions: arctangent (Wang & Zhu, 2016 <doi:10.1155/2016/6495417>), EXP (Wang, Fan, & Zhu, 2018 <doi:10.1007/s10463-016-0588-3>), Gumbel, Log (Candes, Wakin, & Boyd, 2008 <doi:10.1007/s00041-008-9045-x>), and Weibull. Adaptive penalty parameters for EXP, Gumbel, and Weibull are estimated via maximum likelihood, and model selection uses information criteria including AIC, BIC, and EBIC (Extended BIC). Simulation functions generate multivariate normal data from GGMs with stochastic block model or small-world (Watts-Strogatz) network structures.

Detects near-duplicate images across dataset splits using perceptual hashing, reports the resulting train/validation/test contamination, and produces a corrected, leak-free split assignment. Intended for machine learning researchers who need to verify that image classification splits do not share near-duplicate samples across partitions before reporting model metrics.

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.

Implementation of the methods described in Holzmann, Klar (2024) <doi: 10.1111/sjos.12733>. Lancaster correlation is a correlation coefficient which equals the absolute value of the Pearson correlation for the bivariate normal distribution, and is equal to or slightly less than the maximum correlation coefficient for a variety of bivariate distributions. Rank and moment-based estimators and corresponding confidence intervals are implemented, as well as independence tests based on these statistics.

Estimation of Latent Order Logistic (LOLOG) Models for Networks. LOLOGs are a flexible and fully general class of statistical graph models. This package provides functions for performing MOM, GMM and variational inference. Visual diagnostics and goodness of fit metrics are provided. See Fellows (2018) <doi:10.48550/arXiv.1804.04583> for a detailed description of the methods.

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>.

This package performs adjusted inferences based on model objects fitted, using maximum likelihood estimation, by the extreme value analysis packages eva <https://cran.r-project.org/package=eva>, evd <https://cran.r-project.org/package=evd>, evir <https://cran.r-project.org/package=evir>, extRemes <https://cran.r-project.org/package=extRemes>, fExtremes <https://cran.r-project.org/package=fExtremes>, ismev <https://cran.r-project.org/package=ismev>, mev <https://cran.r-project.org/package=mev>, POT <https://cran.r-project.org/package=POT> and texmex <https://cran.r-project.org/package=texmex>. Adjusted standard errors and an adjusted loglikelihood are provided, using the chandwich package <https://cran.r-project.org/package=chandwich> and the object-oriented features of the sandwich package <https://cran.r-project.org/package=sandwich>. The adjustment is based on a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions, or for performing inferences that are robust to certain types of model misspecification. Univariate extreme value models, including regression models, are supported.

Print vectors (and data frames) of floating point numbers using a non-scientific format optimized for human readers. Vectors of numbers are rounded using significant digits, aligned at the decimal point, and all zeros trailing the decimal point are dropped. See: Wright (2016). Lucid: An R Package for Pretty-Printing Floating Point Numbers. In JSM Proceedings, Statistical Computing Section. Alexandria, VA: American Statistical Association. 2270-2279.

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.

This package provides tools are provided to expand vectors of short URLs into long URLs'. No API services are used, which may mean that this operates more slowly than API services do (since they usually cache results of expansions that every user of the service requests). You can setup your own caching layer with the memoise package if you wish to have a speedup during single sessions or add larger dependencies, such as Redis', to gain a longer-term performance boost at the expense of added complexity.

Converts output from latent variable model tools into publication-ready path diagrams and model schematics. lavaan fit objects and parameter tables are supported as a primary workflow, with graph adapters for objects from blavaan', lavaan.mi', semPlot', mirt', eRm', OpenMx', psych', poLCA', mclust', flexmix', lcmm', tidyLPA', and MplusAutomation workflows when those packages are available. Supports structural equation and confirmatory factor analysis diagrams, multilevel structural equation models, growth models, higher-order factor models, latent class and profile models, item response theory models, and common mixture outputs through a unified graph grammar with model-aware defaults, geometry diagnostics, layout quality scoring, automatic layout selection, customizable publication styles, RStudio preview, SVG/PDF/PNG export, TikZ output, and reproducible publication bundles. A local Shiny editor supports mouse dragging of nodes and coefficient labels, synchronized figure downloads, and reusable editing state.

This package provides Shiny widgets and theme that support a Library Computer Access/Retrieval System (LCARS) aesthetic for Shiny apps. The package also includes functions for adding a minimal LCARS theme to static ggplot2 graphs. More details about LCARS can be found at <https://en.wikipedia.org/wiki/LCARS>.

Models pathogen lineage frequency dynamics from genomic surveillance count data. Provides a unified interface for multinomial logistic regression, hierarchical partial-pooling models, and the Piantham approximation for relative reproduction number estimation. Features include rolling-origin backtesting, standardized forecast scoring, lineage collapsing, emergence detection, and sequencing power analysis. Designed for real-time public health surveillance of any variant-resolved pathogen. Methods described in Abousamra, Figgins, and Bedford (2024) <doi:10.1371/journal.pcbi.1012443>.

We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.

This package provides a utility to facilitate the logging and review of R programs in clinical trial programming workflows.

L1 estimation for linear regression using Barrodale and Roberts method <doi:10.1145/355616.361024> and the EM algorithm <doi:10.1023/A:1020759012226>. Estimation of mean and covariance matrix using the multivariate Laplace distribution, density, distribution function, quantile function and random number generation for univariate and multivariate Laplace distribution <doi:10.1080/03610929808832115>. Implementation of Naik and Plungpongpun <doi:10.1007/0-8176-4487-3_7> for the Generalized spatial median estimator is included.

Cleans, geocodes, validates, reviews, and exports messy location address data supplied by the user. The package sits on top of tidygeocoder': it calls geocoding services, rejects implausible coordinates with configurable region guards, applies fallback name/address matching, joins points to optional local geography with sf', and records an audit trail showing how each coordinate was produced. Outputs are designed for manual review, dashboards, and reusable location crosswalks.

Summarizes and visualizes categorical data extracted during literature reviews and evidence syntheses, starting from a data frame with one row per study. Generates publication-ready bar, stacked bar, histogram, waffle, donut, treemap, alluvial, trend, co-occurrence, UpSet', tree, and study-by-criteria matrix figures, together with world maps and formatted summary tables. Plot functions return standard ggplot2 objects that can be further customized, and an interactive Shiny application is included for building figures without writing code. Aims to help researchers report study characteristics consistently across many publications.

The landmark approach allows survival predictions to be updated dynamically as new measurements from an individual are recorded. The idea is to set predefined time points, known as "landmark times", and form a model at each landmark time using only the individuals in the risk set. This package allows the longitudinal data to be modelled either using the last observation carried forward or linear mixed effects modelling. There is also the option to model competing risks, either through cause-specific Cox regression or Fine-Gray regression. To find out more about the methods in this package, please see <https://isobelbarrott.github.io/Landmarking/articles/Landmarking>.

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.

An interface for the image processing program ImageJ', which allows a rapid digital image analysis for particle sizes. This package includes function to write an ImageJ macro which is optimized for a leaf area analysis by default.

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.

Plots contextual landmark Kaplan-Meier curves. An extension of the survminer::ggsurvplot() function that allows the specification of a landmark time and an optional label. The period before the landmark is displayed as a pooled survival curve, while curves beyond the landmark are presented according to the groups defined in the supplied survival::survfit object.