This package provides a drop-in replacement for flexdashboard Rmd documents, which implements an after-knit-hook to split the generated single page application in one document per main section to reduce rendering load in the web browser displaying the document. Put all JavaScript stuff needed in all sections before the first headline featuring navigation menu attributes. This package is experimental and maybe replaced by a solution inside flexdashboard'.

The HJ-Biplot is a multivariate method that represents high-dimensional data in a low-dimensional subspace, capturing most of the informationâ s variability in just a few dimensions. This package implements three new regularized versions of the HJ-Biplot: Ridge, LASSO, and Elastic Net. These versions introduce restrictions that shrink or zero-out variable weights to improve interpretability based on regularization theory. All methods provide graphical representations using ggplot2'.

This package provides a system to simulate clinical trials with time to event endpoints. Event simulation is based on Cox models allowing for covariates in addition to the treatment or group factor. Specific drop-out rates (separate from administrative censoring) can be controlled in the simulation. Other features include stratified randomization, non-proportional hazards, different accrual patterns, and event projection (timing to reach the target event) based on interim data.

This package provides a comprehensive and user-friendly interface for accessing, manipulating, and analyzing country-level data from around the world. It allows users to retrieve detailed information on countries, including names, regions, continents, populations, currencies, calling codes, and more, all in a tidy data format. The package is designed to work seamlessly within the tidyverse ecosystem, making it easy to filter, arrange, and visualize country-level data in R.

This package provides a set of tools for network analysis using mass spectrometry-based proteomics data and network databases. The package takes as input the output of MSstats differential abundance analysis and provides functions to perform enrichment analysis and visualization in the context of prior knowledge from past literature. Notably, this package integrates with INDRA, which is a database of biological networks extracted from the literature using text mining techniques.

Analysis workflow for finding geographic boundaries of ecological or landscape traits and comparing the placement of geographic boundaries of two traits. If data are trait values, trait data are transformed to boundary intensities based on approximate first derivatives across latitude and longitude. The package includes functions to create custom null models based on the input data. The boundary statistics are described in: Fortin, Drapeau, and Jacquez (1996) <doi:10.2307/3545584>.

This package provides tools to process the information obtained from experiments conducted in the Barnes Maze. These tools enable the detection of trajectories generated by subjects during trials, as well as the acquisition of precise coordinates and relevant statistical data regarding the results. Through this approach, it aims to facilitate the analysis and interpretation of observed behaviors, thereby contributing to a deeper understanding of learning and memory processes in such experiments.

Analysis of preprocessed dramatic texts, with respect to literary research. The package provides functions to analyze and visualize information about characters, stage directions, the dramatic structure and the text itself. The dramatic texts are expected to be in CSV format, which can be installed from within the package, sample texts are provided. The package and the reasoning behind it are described in Reiter et al. (2017) <doi:10.18420/in2017_119>.

Imports time series data from the Quandl database <https://data.nasdaq.com/>. The package uses the json api at <https://data.nasdaq.com/search>, local caching ('memoise package) and the tidy format by default. Also allows queries of databases, allowing the user to see which time series are available for each database id. In short, it is an alternative to package Quandl', with faster data importation in the tidy/long format.

This package provides tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients and the relationship matrix (includes code for pedigrees from self-pollinated species). The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict breeding values. The relationship matrix between the individuals can be derived from pedigree structure ('Vazquez et al., 2010') <doi:10.2527/jas.2009-1952>.

The package provides a comprehensive mapping table of metabolites and proteins linked to PathBank pathways. The tables include HMDB, KEGG, ChEBI, CAS, Drugbank, Uniprot IDs. The tables are provided for each of the 10 species ("Homo sapiens", "Escherichia coli", "Mus musculus", "Arabidopsis thaliana", "Saccharomyces cerevisiae", "Bos taurus", "Caenorhabditis elegans", "Rattus norvegicus", "Drosophila melanogaster", and "Pseudomonas aeruginosa"). These table information can be used for Metabolite Set (and other) Enrichment Analysis.

Ensmallen is a templated C++ mathematical optimization library (by the MLPACK team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The RcppEnsmallen package includes the header files from the Ensmallen library and pairs the appropriate header files from armadillo through the RcppArmadillo package. Therefore, users do not need to install Ensmallen nor Armadillo to use RcppEnsmallen'. Note that Ensmallen is licensed under 3-Clause BSD, Armadillo starting from 7.800.0 is licensed under Apache License 2, RcppArmadillo (the Rcpp bindings/bridge to Armadillo') is licensed under the GNU GPL version 2 or later. Thus, RcppEnsmallen is also licensed under similar terms. Note that Ensmallen requires a compiler that supports C++14 and Armadillo 10.8.2 or later.

Analysis of large datasets of fixed coupon bonds, allowing for irregular first and last coupon periods and various day count conventions. With this package you can compute the yield to maturity, the modified and MacAulay durations and the convexity of fixed-rate bonds. It provides the function AnnivDates, which can be used to evaluate the quality of the data and return time-invariant properties and temporal structure of a bond.

Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2023) <doi:10.48550/arXiv.2303.11561>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

Sparklines are small plots (about one line of text high), made popular by Edward Tufte. This package is the interface from R to the LaTeX package sparklines by Andreas Loeffer and Dan Luecking (<http://www.ctan.org/pkg/sparklines>). It can work with Sweave or knitr or other engines that produce TeX. The package can be used to plot vectors, matrices, data frames, time series (in ts or zoo format).

This package provides a comprehensive and curated collection of datasets related to the lungs, respiratory system, and associated diseases. This package includes epidemiological, clinical, experimental, and simulated datasets on conditions such as lung cancer, asthma, Chronic Obstructive Pulmonary Disease (COPD), tuberculosis, whooping cough, pneumonia, influenza, and other respiratory illnesses. It is designed to support data exploration, statistical modeling, teaching, and research in pulmonary medicine, public health, environmental epidemiology, and respiratory disease surveillance.

We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620> and Lindmark (2022) <doi:10.1007/s10260-021-00611-4>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.

This package provides a range of functions for the design and analysis of disease surveillance activities. These functions were originally developed for animal health surveillance activities but can be equally applied to aquatic animal, wildlife, plant and human health surveillance activities. Utilities are included for sample size calculation and analysis of representative surveys for disease freedom, risk-based studies for disease freedom and for prevalence estimation. This package is based on Cameron A., Conraths F., Frohlich A., Schauer B., Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision making tools for implementing risk-based surveillance, Grant Number no. 310806, RISKSUR (<https://www.fp7-risksur.eu/sites/default/files/documents/Deliverables/RISKSUR_%28310806%29_D6.24.pdf>). Many of the RSurveillance functions are incorporated into the epitools website: Sergeant, ESG, 2019. Epitools epidemiological calculators. Ausvet Pty Ltd. Available at: <http://epitools.ausvet.com.au>.

Catalogues of resolution IV regular fractional factorial designs in 128 runs are provided for up to 33 2-level factors. The catalogues are complete, excluding resolution IV designs without 5-letter words, because these do not add value for a search for unblocked clear designs. The previous package version 1.0 with complete catalogues up to 24 runs (24 runs and a namespace added later) can be downloaded from the authors website.

Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS <https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast>, MeteoGalicia <https://www.meteogalicia.gal/web/modelos/threddsIndex.action>, NAM <https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale>, and RAP <https://www.ncei.noaa.gov/products/weather-climate-models/rapid-refresh-update>.

The goal of statcodelists is to promote the reuse and exchange of statistical information and related metadata with making the internationally standardized SDMX code lists available for the R user. SDMX has been published as an ISO International Standard (ISO 17369). The metadata definitions, including the codelists are updated regularly according to the standard. The authoritative version of the code lists made available in this package is <https://sdmx.org/?page_id=3215/>.

planttfhunter is used to identify plant transcription factors (TFs) from protein sequence data and classify them into families and subfamilies using the classification scheme implemented in PlantTFDB. TFs are identified using pre-built hidden Markov model profiles for DNA-binding domains. Then, auxiliary and forbidden domains are used with DNA-binding domains to classify TFs into families and subfamilies (when applicable). Currently, TFs can be classified in 58 different TF families/subfamilies.

This Python module provides line editing functions similar to the default Emacs-style ones of GNU Readline. Unlike the Python standard library's readline package, this one allows access to those capabilities in settings outside of a standard command-line interface. It is especially well-suited to interfacing with Urwid, due to a shared syntax for describing key inputs.

Currently, all stateless Readline commands are implemented. Yanking and history are not supported.

With Serverspec, you can write RSpec tests for checking your servers are configured correctly.

Serverspec tests your servers’ actual state by executing command locally, via SSH, via WinRM, via Docker API and so on. So you don’t need to install any agent softwares on your servers and can use any configuration management tools, Puppet, Ansible, CFEngine, Itamae and so on.

But the true aim of Serverspec is to help refactoring infrastructure code.