This package provides a set of functions for the detection of spatial clusters of disease using count data. Bootstrap is used to estimate sampling distributions of statistics.

Connect to the DocuSign Rest API <https://www.docusign.com/p/RESTAPIGuide/RESTAPIGuide.htm>, which supports embedded signing, and sending of documents.

Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of a bottom-up model for PCR amplification. This model is developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.

This package provides an interactive viewer for data.frame and tibble objects using shiny <https://shiny.posit.co/> and DT <https://rstudio.github.io/DT/>. It supports complex filtering, column selection, and automatic generation of reproducible dplyr <https://dplyr.tidyverse.org/> code for data manipulation. The package is designed for ease of use in data exploration and reporting workflows.

Implementing Function-on-Scalar Regression model in which the response function is dichotomized and observed sparsely. This package provides smooth estimations of functional regression coefficients and principal components for the dichotomized functional response regression (dfrr) model.

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>.

Detects and filters damaged cells in single-cell RNA sequencing (scRNA-seq) data using a novel approach inspired by DoubletFinder'. Damage is detected by measuring the extent to which cells deviate from artificially damaged profiles of themselves, simulated through the probabilistic escape of cytoplasmic RNA. As output, a damage score ranging from 0 to 1 is given for each cell providing an intuitive scale for filtering that is standardised across cell types, samples, and experiments.

This package implements the distribution-free goodness-of-fit regression test for the mean structure of parametric models introduced in Khmaladze (2021) <doi:10.1007/s10463-021-00786-3>. The test is implemented for general functions with minimal distributional assumptions as well as common models (e.g., lm, glm) with the usual assumptions.

This package provides functions for interacting with all sections of the official Danish Address Web API (also known as DAWA') <https://api.dataforsyningen.dk>. The development of this package is completely independent from the government agency, Klimadatastyrelsen, who maintains the API.

An interactive editor built on rhandsontable to allow the interactive viewing, entering, filtering and editing of data in R <https://dillonhammill.github.io/DataEditR/>.

Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See Valente et al. (2015) <doi:10.1111/ele.12461>.

While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

Estimation of the total population size from capture-recapture data efficiently and with low bias implementing the methods from Das M, Kennedy EH, and Jewell NP (2021) <arXiv:2104.14091>. The estimator is doubly robust against errors in the estimation of the intermediate nuisance parameters. Users can choose from the flexible estimation models provided in the package, or use any other preferred model.

Construction and analysis of matrix population models in R.

Retrieves code comment decorations for C++ languages of the form \\ [[xyz]]', which are used for automated wrapping of C++ functions.

Direction analysis is a set of tools designed to identify combinatorial effects of multiple treatments/conditions on pathways and kinases profiled by microarray, RNA-seq, proteomics, or phosphoproteomics data. See Yang P et al (2014) <doi:10.1093/bioinformatics/btt616>; and Yang P et al. (2016) <doi:10.1002/pmic.201600068>.

An implementation of distributional random forests as introduced in Cevid & Michel & Naf & Meinshausen & Buhlmann (2022) <doi:10.48550/arXiv.2005.14458>.

This package provides a thin wrapper around the Datorama API. Ideal for analyzing marketing data from <https://datorama.com>.

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 dynamicpv is to provide a simple way to calculate (net) present values and outputs from health economic models (especially cost-effectiveness and budget impact) in discrete time that reflect dynamic pricing and dynamic uptake. Dynamic pricing is also known as life cycle pricing; dynamic uptake is also known as multiple or stacked cohorts, or dynamic disease prevalence. Shafrin (2024) <doi:10.1515/fhep-2024-0014> provides an explanation of dynamic value elements, in the context of Generalized Cost Effectiveness Analysis, and Puls (2024) <doi:10.1016/j.jval.2024.03.006> reviews challenges of incorporating such dynamic value elements. This package aims to reduce those challenges.

Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of data.table efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.

Package including an interactive Shiny application for plotting common univariate distributions.

Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).

This package provides functions for analyzing dichotomous choice contingent valuation (CV) data. It provides functions for estimating parametric and nonparametric models for single-, one-and-one-half-, and double-bounded CV data. For details, see Aizaki et al. (2022) <doi:10.1007/s42081-022-00171-1>.