R lists, especially nested lists, can be very difficult to visualize or represent. Sometimes str() is not enough, so this suite of htmlwidgets is designed to help see, understand, and maybe even modify your R lists. The function reactjson() requires a package reactR that can be installed from CRAN or <https://github.com/timelyportfolio/reactR>.

Estimate model parameters to determine whether two compounds have synergy, antagonism, or Loewe's Additivity.

This package provides a LaTeX Letter class for rmarkdown', using the pandoc-letter template adapted for use with markdown'.

Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) or penalized least squares (PLS). For details, please see Huang (2020) <doi:10.18637/jss.v093.i07>.

Local partial likelihood estimation by Fan, Lin and Zhou(2006)<doi:10.1214/009053605000000796> and simultaneous confidence band is a set of tools to test the covariates-biomarker interaction for survival data. Test for the covariates-biomarker interaction using the bootstrap method and the asymptotic method with simultaneous confidence band (Liu, Jiang and Chen (2015)<doi:10.1002/sim.6563>).

Connect to the Less Annoying CRM API with ease to get your crm data in a clean and tidy format. Less Annoying CRM is a simple CRM built for small businesses, more information is available on their website <https://www.lessannoyingcrm.com/>.

This package provides a framework that allows for easy logging of changes in data. Main features: start tracking changes by adding a single line of code to an existing script. Track changes in multiple datasets, using multiple loggers. Add custom-built loggers or use loggers offered by other packages. <doi:10.18637/jss.v098.i01>.

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 produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography.

This package provides the method for computing the local partial autocorrelation function for locally stationary wavelet time series from Killick, Knight, Nason, Eckley (2020) <doi:10.1214/20-EJS1748>.

Fits look-up tables by filling entries with the mean or median values of observations fall in partitions of the feature space. Partitions can be determined by user of the package using input argument feature.boundaries, and dimensions of the feature space can be any combination of continuous and categorical features provided by the data set. A Predict function directly fetches corresponding entry value, and a default value is defined as the mean or median of all available observations. The table and other components are represented using the S4 class lookupTable.

This package provides tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).

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 provides a wrapper around the LIBLINEAR C/C++ library for machine learning (available at <https://www.csie.ntu.edu.tw/~cjlin/liblinear/>). LIBLINEAR is a simple library for solving large-scale regularized linear classification and regression. It currently supports L2-regularized classification (such as logistic regression, L2-loss linear SVM and L1-loss linear SVM) as well as L1-regularized classification (such as L2-loss linear SVM and logistic regression) and L2-regularized support vector regression (with L1- or L2-loss). The main features of LiblineaR include multi-class classification (one-vs-the rest, and Crammer & Singer method), cross validation for model selection, probability estimates (logistic regression only) or weights for unbalanced data. The estimation of the models is particularly fast as compared to other libraries.

This package provides a collection of functions that calculate the log likelihood (support) for a range of statistical tests. Where possible the likelihood function and likelihood interval for the observed data are displayed. The evidential approach used here is based on the book "Likelihood" by A.W.F. Edwards (1992, ISBN-13 : 978-0801844430), "Statistical Evidence" by R. Royall (1997, ISBN-13 : 978-0412044113), S.N. Goodman & R. Royall (2011) <doi:10.2105/AJPH.78.12.1568>, "Understanding Psychology as a Science" by Z. Dienes (2008, ISBN-13 : 978-0230542310), S. Glover & P. Dixon <doi:10.3758/BF03196706> and others. This package accompanies "Evidence-Based Statistics" by P. Cahusac (2020, ISBN-13 : 978-1119549802) <doi:10.1002/9781119549833>.

An effortless ndjson (newline-delimited JSON') logger, with two primary log-writing interfaces. It provides a set of wrappings for base R's message(), warning(), and stop() functions that maintain identical functionality, but also log the handler message to an ndjson log file. loggit also exports its internal loggit() function for powerful and configurable custom logging. No change in existing code is necessary to use this package, and should only require additions to fully leverage the power of the logging system. loggit also provides a log reader for reading an ndjson log file into a data frame, log rotation, and live echo of the ndjson log messages to terminal stdout for log capture by external systems (like containers). loggit is ideal for Shiny apps, data pipelines, modeling work flows, and more. Please see the vignettes for detailed example use cases.

This package provides a system for accurately designing complex light regimes using LEDs. Takes calibration data and user-defined target irradiances and it tells you what intensities to use. For more details see Vong et al. (2025) <doi:10.1101/2025.06.06.658293>.

This package provides a collection of helper functions for multiple regression models fitted by lm(). Most of them are simple functions for simple tasks which can be done with coding, but may not be easy for occasional users of R. Most of the tasks addressed are those sometimes needed when using the manymome package (Cheung and Cheung, 2023, <doi:10.3758/s13428-023-02224-z>) and stdmod package (Cheung, Cheung, Lau, Hui, and Vong, 2022, <doi:10.1037/hea0001188>). However, they can also be used in other scenarios.

Linear model functions using permutation tests.

Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format.

This package contains LUE_BIOMASS(),LUE_BIOMASS_VPD(), LUE_YIELD() and LUE_YIELD_VPD() to estimate aboveground biomass and crop yield firstly by calculating the Absorbed Photosynthetically Active Radiation (APAR) and secondly the actual values of light use efficiency with and without vapour presure deficit Shi et al.(2007) <doi:10.2134/agronj2006.0260>.

This package provides a classification tree method that uses Uncorrelated Linear Discriminant Analysis (ULDA) for variable selection, split determination, and model fitting in terminal nodes. It automatically handles missing values and offers visualization tools. For more details, see Wang (2024) <doi:10.48550/arXiv.2410.23147>.

My PhD supervisor once told me that everyone doing newspaper analysis starts by writing code to read in files from the LexisNexis newspaper archive (retrieved e.g., from <https://www.lexisnexis.com/> or any of the partner sites). However, while this is a nice exercise I do recommend, not everyone has the time. This package takes files downloaded from the newspaper archive of LexisNexis', reads them into R and offers functions for further processing.

This package provides a Low Rank Correction Variational Bayesian algorithm for high-dimensional multi-source heterogeneous quantile linear models. More details have been written up in a paper submitted to the journal Statistics in Medicine, and the details of variational Bayesian methods can be found in Ray and Szabo (2021) <doi:10.1080/01621459.2020.1847121>. It simultaneously performs parameter estimation and variable selection. The algorithm supports two model settings: (1) local models, where variable selection is only applied to homogeneous coefficients, and (2) global models, where variable selection is also performed on heterogeneous coefficients. Two forms of parameter estimation are output: one is the standard variational Bayesian estimation, and the other is the variational Bayesian estimation corrected with low-rank adjustment.