Make your shiny application as executable program. Users do not need to install R and shiny on their system.

This package provides a comprehensive, vectorised toolkit for estimating glomerular filtration rate (eGFR) and creatinine clearance from serum creatinine, cystatin C, or both. Implements adult, paediatric, and neonatal equations, including the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations (2009, 2012, 2021), the Modification of Diet in Renal Disease (MDRD) Study equation, Cockcroft-Gault, the European Kidney Function Consortium (EKFC) equations, the Full Age Spectrum (FAS) equations, the Lund-Malmoe equations, the Berlin Initiative Study (BIS) equations, the Schwartz bedside equation, the Chronic Kidney Disease in Children Under 25 (CKiD U25) equations, the Caucasian, Asian, Paediatric, and Adult (CAPA) cystatin C equation, and a neonatal equation. Helpers for body surface area, chronic kidney disease (CKD) staging following the Kidney Disease: Improving Global Outcomes (KDIGO) guideline, and unit conversions are included. Methods are described in Levey et al. (2009) <doi:10.7326/0003-4819-150-9-200905050-00006>, Inker et al. (2021) <doi:10.1056/NEJMoa2102953>, and Pottel et al. (2021) <doi:10.7326/M20-4366>. Inspired by the kidney.epi package.

Gene information from Ensembl genome builds GRCh38.p14 and GRCh37.p13 to use with the topr package. The datasets were originally downloaded from <https://ftp.ensembl.org/pub/current/gtf/homo_sapiens/Homo_sapiens.GRCh38.111.gtf.gz> and <https://ftp.ensembl.org/pub/grch37/current/gtf/homo_sapiens/Homo_sapiens.GRCh37.87.gtf.gz> and converted into the format required by the topr package. See <https://github.com/totajuliusd/topr?tab=readme-ov-file#how-to-use-topr-with-other-species-than-human> to see the required format.

The purpose of Early Warning Systems (EWS) is to detect accurately the occurrence of a crisis, which is represented by a binary variable which takes the value of one when the event occurs, and the value of zero otherwise. EWS are a toolbox for policymakers to prevent or attenuate the impact of economic downturns. Modern EWS are based on the econometric framework of Kauppi and Saikkonen (2008) <doi:10.1162/rest.90.4.777>. Specifically, this framework includes four dichotomous models, relying on a logit approach to model the relationship between yield spreads and future recessions, controlling for recession risk factors. These models can be estimated in a univariate or a balanced panel framework as in Candelon, Dumitrescu and Hurlin (2014) <doi:10.1016/j.ijforecast.2014.03.015>. This package provides both methods for estimating these models and a dataset covering 13 OECD countries over a period of 45 years. In addition, this package also provides methods for the analysis of the propagation mechanisms of an exogenous shock, as well as robust confidence intervals for these response functions using a block-bootstrap method as in Lajaunie (2021). This package constitutes a useful toolbox (data and functions) for scholars as well as policymakers.

The Australian Regulatory Guidelines for Prescription Medicines (ARGPM), guidance on "Stability testing for prescription medicines", recommends to predict the shelf life of chemically derived medicines from stability data by taking the worst case situation at batch release into account. Consequently, if a change over time is observed, a release limit needs to be specified. Finding a release limit and the associated shelf life is supported, as well as the standard approach that is recommended by guidance Q1E "Evaluation of stability data" from the International Council for Harmonisation (ICH).

High-performance implementation of various effect plots useful for regression and probabilistic classification tasks. The package includes partial dependence plots (Friedman, 2021, <doi:10.1214/aos/1013203451>), accumulated local effect plots and M-plots (both from Apley and Zhu, 2016, <doi:10.1111/rssb.12377>), as well as plots that describe the statistical associations between model response and features. It supports visualizations with either ggplot2 or plotly', and is compatible with most models, including Tidymodels', models wrapped in DALEX explainers, or models with case weights.

This package provides methods for estimating species niche position and niche breadth under continuous environmental gradients. The package implements canonical correspondence analysis (CCA), partial CCA (pCCA), generalized additive models (GAM), and Levins niche breadth metrics for species-level and community-level analyses. Methods are based on ter Braak (1986) <doi:10.2307/1938672>, Okie et al. (2015) <doi:10.1098/rspb.2014.2630>, Feng et al. (2020) <doi:10.1111/mec.15441>, Wood (2017) <doi:10.1201/9781315370279>, and Levins (1968, ISBN:978-0691080628).

This package provides simple functions to create constraints for small test assembly problems (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, GLPK', lpSolve', Symphony', and Gurobi are supported as solvers. The gurobi package is not available from any mainstream repository; see <https://www.gurobi.com/downloads/>.

The new yield tables developed by the Northwest German Forest Research Institute (NW-FVA) provide a forest management tool for the five main commercial tree species oak, beech, spruce, Douglas-fir and pine for northwestern Germany. The new method applied for deriving yield tables combines measurements of growth and yield trials with growth simulations using a state-of-the-art single-tree growth simulator. By doing so, the new yield tables reflect the current increment level and the recommended graduated thinning from above is the underlying management concept. The yield tables are provided along with methods for deriving the site index and for interpolating between age and site indices and extrapolating beyond age and site index ranges. The inter-/extrapolations are performed traditionally by the rule of proportion or with a functional approach.

Environmental seismology is a scientific field that studies the seismic signals, emitted by Earth surface processes. This package provides all relevant functions to read/write seismic data files, prepare, analyse and visualise seismic data, and generate reports of the processing history.

Easily load and install multiple packages from different sources, including CRAN and GitHub. The libraries function allows you to load or attach multiple packages in the same function call. The packages function will load one or more packages, and install any packages that are not installed on your system (after prompting you). Also included is a from_import function that allows you to import specific functions from a package into the global environment.

This package provides tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.

Package for data exploration and result presentation. Full epicalc package with data management functions is available at <https://medipe.psu.ac.th/epicalc/>'.

Implementation of a function which calculates the empirical excess mass for given \eqn\lambda and given maximal number of modes (excessm()). Offering powerful plot features to visualize empirical excess mass (exmplot()). This includes the possibility of drawing several plots (with different maximal number of modes / cut off values) in a single graph.

An RStudio addin for editing a data.frame or a tibble'. You can delete, add or update a data.frame without coding. You can get resultant data as a data.frame'. In the package, modularized shiny app codes are provided. These modules are intended for reuse across applications.

Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.

The production of certified reference materials (CRMs) requires various statistical tests depending on the task and recorded data to ensure that reported values of CRMs are appropriate. Often these tests are performed according to the procedures described in ISO GUIDE 35:2017'. The eCerto package contains a Shiny app which provides functionality to load, process, report and backup data recorded during CRM production and facilitates following the recommended procedures. It is described in Lisec et al (2023) <doi:10.1007/s00216-023-05099-3> and can also be accessed online <https://apps.bam.de/shn00/eCerto/> without package installation.

Allows users to model and draw inferences from extreme value inflated count data, and to evaluate these models and compare to non extreme-value inflated counterparts. The package is built to be compatible with standard presentation tools such as broom', tidy', and modelsummary'.

Automatic generation of quizzes or individual questions as (interactive) forms within rmarkdown or quarto documents based on R/exams exercises.

Facilitates univariate and multivariate analysis of evolutionary sequences of phenotypic change. The package extends the modeling framework available in the paleoTS package. Please see <https://klvoje.github.io/evoTS/index.html> for information about the package and the implemented models.

Forecasting univariate time series with different decomposition based Extreme Learning Machine models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>, Parida M, Behera MK, Nayak N (2018). <doi:10.1109/ICSESP.2018.8376723>.

This package provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions.

This package implements likelihood-based evidence ratios for unified reporting in classical statistical testing. The package reports effect estimates, uncertainty intervals, and likelihood ratios on the log 10 scale derived from a single statistical model. It applies to standard normal mean tests, contingency tables, and regression coefficients, and provides a direct evidential measure while retaining classical error guarantees. For the Evidence Ratio Reporting Standard see Lawless (2026) <doi:10.5281/zenodo.18261076>.

Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.