We provide a list of functions for replicating the results of the Monte Carlo simulations and empirical application of Jiang et al. (2022). In particular, we provide corresponding functions for generating the three types of random data described in this paper, as well as all the estimation strategies. Detailed information about the data generation process and estimation strategy can be found in Jiang et al. (2022) <doi:10.48550/arXiv.2201.13004>.

This package provides a tool developed with the Golem framework which provides an easier way to check cells differences between two data frames. The user provides two data frames for comparison, selects IDs variables identifying each row of input data, then clicks a button to perform the comparison. Several R package functions are used to describe the data and perform the comparison in the server of the application. The main ones are comparedf() from arsenal and skim() from skimr'. For more details see the description of comparedf() from the arsenal package and that of skim() from the skimr package.

The distributed online expectation maximization algorithms are used to solve parameters of Poisson mixture models. The philosophy of the package is described in Guo, G. (2022) <doi:10.1080/02664763.2022.2053949>.

An R DataBase Interface ('DBI') compatible interface to various database platforms ('PostgreSQL', Oracle', Microsoft SQL Server', Amazon Redshift', Microsoft Parallel Database Warehouse', IBM Netezza', Apache Impala', Google BigQuery', Snowflake', Spark', SQLite', and InterSystems IRIS'). Also includes support for fetching data as Andromeda objects. Uses either Java Database Connectivity ('JDBC') or other DBI drivers to connect to databases.

The DImodels package is suitable for analysing data from biodiversity and ecosystem function studies using the Diversity-Interactions (DI) modelling approach introduced by Kirwan et al. (2009) <doi:10.1890/08-1684.1>. Suitable data will contain proportions for each species and a community-level response variable, and may also include additional factors, such as blocks or treatments. The package can perform data manipulation tasks, such as computing pairwise interactions (the DI_data() function), can perform an automated model selection process (the autoDI() function) and has the flexibility to fit a wide range of user-defined DI models (the DI() function).

This package provides tools to simulate genetic distance matrices, align and compare them via multidimensional scaling (MDS) and Procrustes, and evaluate imputation with the Bootstrapping Evaluation for Structural Missingness Imputation (BESMI) framework. Methods align with Zhu et al. (2025) <doi:10.3389/fpls.2025.1543956> and the associated software resource Zhu (2025) <doi:10.26188/28602953>.

Create and customize interactive collapsible D3 trees using the D3 JavaScript library and the htmlwidgets package. These trees can be used directly from the R console, from RStudio', in Shiny apps and R Markdown documents. When in Shiny the tree layout is observed by the server and can be used as a reactive filter of structured data.

This function provides an interface between Matlab and R in facilitating fast processing for reading and saving DICOM images.

This package provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.

This package implements a Bayesian algorithm for overcoming weak separation in Bayesian latent class analysis. Reference: Li et al. (2023) <arXiv:2306.04700>.

This package implements maximum likelihood methods for evaluating the durability of vaccine efficacy in a randomized, placebo-controlled clinical trial with staggered enrollment of participants and potential crossover of placebo recipients before the end of the trial. Lin, D. Y., Zeng, D., and Gilbert, P. B. (2021) <doi:10.1093/cid/ciab226> and Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) <doi:10.1093/cid/ciab630>.

The data consist of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides tools to create or manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.

This package performs distance sampling simulations. dsims repeatedly generates instances of a user defined population within a given survey region. It then generates realisations of a survey design and simulates the detection process. The data are then analysed so that the results can be compared for accuracy and precision across all replications. This process allows users to optimise survey designs for their specific set of survey conditions. The effects of uncertainty in population distribution or parameters can be investigated under a number of simulations so that users can be confident that they have achieved a robust survey design before deploying vessels into the field. The distance sampling designs used in this package from dssd are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). General distance sampling methods are detailed in Introduction to Distance Sampling: Estimating Abundance of Biological Populations, Buckland et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating animal/plant abundance with distance sampling at <https://distancesampling.org/>.

Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset.

This package creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Provides diverse quality criteria. Provides utility functions for the class design, which is also used by other packages for designed experiments.

Visualize contact tracing data using a shiny app and estimate the incubation or latency time of an infectious disease respecting the following characteristics in the analysis; (i) doubly interval censoring with (partly) overlapping or distinct windows; (ii) an infection risk corresponding to exponential growth; (iii) right truncation allowing for individual truncation times; (iv) different choices concerning the family of the distribution. For our earlier work, we refer to Arntzen et al. (2023) <doi:10.1002/sim.9726>. A paper describing our approach in detail will follow.

This package implements the algorithm described in Jun Li and Alicia T. Lamere, "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes" (Unpublished). Detects not only genes that show different average expressions ("differential expression", DE), but also genes that show different diversities of expressions in different groups ("differentially dispersed", DD). DD genes can be important clinical markers. DiPhiSeq uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise. Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi. (2) Added a function for estimating the proportion of outliers in the data. (3) Modified the input parameter names for clarity, and modified the output format for the main function.

Represents the content of a directory as an interactive collapsible tree. Offers the possibility to assign a text (e.g., a Readme.txt') to each folder (represented as a clickable node), so that when the user hovers the pointer over a node, the corresponding text is displayed as a tooltip.

The goal of dlr is to provide a friendly wrapper around the common pattern of downloading a file if that file does not already exist locally.

Graphical interface for loading datasets in RStudio from all installed (including unloaded) packages, also includes command line interfaces.

This package provides a HTML widget that shows differences between files (text, images, and data frames).

Model estimation, dispersion testing and diagnosis of hyper-Poisson Saez-Castillo, A.J. and Conde-Sanchez, A. (2013) <doi:10.1016/j.csda.2012.12.009> and Conway-Maxwell-Poisson Huang, A. (2017) regression models.

Tests for modality of data using its spacing. The main approach evaluates features (peaks, flats) using a combination of parametric models and non-parametric tests, either after smoothing the spacing by a low-pass filter or by looking over larger intervals. The library can also use any changepoint detectors available to look for transitions between features in the spacing. The Suggested list of libraries is misnamed. It contains all supported changepoint detectors, and all are considered optional. A good minimal set would be the first three entries. Some of the suggestions may no longer be current on CRAN, with the last source tarball found in its archives or at the additional repository. These packages will be used if they are already installed on older installations, but will not be available to newer.

Calculates the desparsified lasso as originally introduced in van de Geer et al. (2014) <doi:10.1214/14-AOS1221>, and provides inference suitable for high-dimensional time series, based on the long run covariance estimator in Adamek et al. (2020) <arXiv:2007.10952>. Also estimates high-dimensional local projections by the desparsified lasso, as described in Adamek et al. (2022) <arXiv:2209.03218>.