This package provides tools to construct (or add to) cell-type signature matrices using flow sorted or single cell samples and deconvolve bulk gene expression data. Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells <doi:10.1371/journal.pone.0224693>.

Designed for optimal use in performing fast, accurate walking strides segmentation from high-density data collected from a wearable accelerometer worn during continuous walking activity.

This package provides algorithms for creating artworks in the ggplot2 language that incorporate some form of randomness.

An interactive document on the topic of one-way and two-way analysis of variance using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://kartikeyab.shinyapps.io/ANOVAShiny/>.

Computationally efficient procedures for regularized estimation with the semiparametric additive hazards regression model.

The ggarrow package is a ggplot2 extension that plots a variety of different arrow segments with many options to customize. The arrowheadr package makes it easy to create custom arrowheads and fins within the parameters that ggarrow functions expect. It has preset arrowheads and a collection of functions to create and transform data for customizing arrows.

These dataset contains daily quality air measurements in Spain over a period of 18 years (from 2001 to 2018). The measurements refer to several pollutants. These data are openly published by the Government of Spain. The datasets were originally spread over a number of files and formats. Here, the same information is contained in simple dataframe for convenience of researches, journalists or general public. See the Spanish Government website <http://www.miteco.gob.es/> for more information.

Estimate the linear and nonlinear autoregressive distributed lag (ARDL & NARDL) models and the corresponding error correction models, and test for longrun and short-run asymmetric. The general-to-specific approach is also available in estimating the ARDL and NARDL models. The Pesaran, Shin & Smith (2001) (<doi:10.1002/jae.616>) bounds test for level relationships is also provided. The ardl.nardl package also performs short-run and longrun symmetric restrictions available at Shin et al. (2014) <doi:10.1007/978-1-4899-8008-3_9> and their corresponding tests.

This package provides a collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al <doi:10.1186/s12874-021-01450-3>.

Automatically selects and runs the most appropriate statistical test for your data, returning clear, easy-to-read results. Ideal for all experience levels.

Using of the accelerated line search algorithm for simultaneously diagonalize a set of symmetric positive definite matrices.

Assists in automating the selection of terms to include in mixed models when asreml is used to fit the models. Procedures are available for choosing models that conform to the hierarchy or marginality principle, for fitting and choosing between two-dimensional spatial models using correlation, natural cubic smoothing spline and P-spline models. A history of the fitting of a sequence of models is kept in a data frame. Also used to compute functions and contrasts of, to investigate differences between and to plot predictions obtained using any model fitting function. The content falls into the following natural groupings: (i) Data, (ii) Model modification functions, (iii) Model selection and description functions, (iv) Model diagnostics and simulation functions, (v) Prediction production and presentation functions, (vi) Response transformation functions, (vii) Object manipulation functions, and (viii) Miscellaneous functions (for further details see asremlPlus-package in help). The asreml package provides a computationally efficient algorithm for fitting a wide range of linear mixed models using Residual Maximum Likelihood. It is a commercial package and a license for it can be purchased from VSNi <https://vsni.co.uk/> as asreml-R', who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>). It is not needed for functions that are methods for alldiffs and data.frame objects. The package asremPlus can also be installed from <http://chris.brien.name/rpackages/>.

This package provides tools for downloading hourly averages, daily maximums and minimums from each of the pollution, wind, and temperature measuring stations or geographic zones in the Mexico City metro area. The package also includes the locations of each of the stations and zones. See <http://aire.cdmx.gob.mx/> for more information.

An interface to the AutoDesk API Platform including the Authentication API for obtaining authentication to the AutoDesk Forge Platform, Data Management API for managing data across the platform's cloud services, Design Automation API for performing automated tasks on design files in the cloud, Model Derivative API for translating design files into different formats, sending them to the viewer app, and extracting design data, and Viewer for rendering 2D and 3D models.

This package provides a spatiotemporal model that simulates the spread of Ascochyta blight in chickpea fields based on location-specific weather conditions. This model is adapted from a model developed by Diggle et al. (2002) <doi:10.1094/PHYTO.2002.92.10.1110> for simulating the spread of anthracnose in a lupin field.

Research of subgroups in random clinical trials with binary outcome and two treatments groups. This is an adaptation of the Jared Foster method (<https://www.ncbi.nlm.nih.gov/pubmed/21815180>).

Estimates a first-price auction model with conditionally independent private values as described in MacKay (2020) <doi:10.2139/ssrn.3096534>. The model allows for unobserved heterogeneity that is common to all bidders in addition to observable heterogeneity.

Data processing and generating stratigraphic sections for volcanic deposits and tephrastratigraphy. Package was developed for studies on Alaska volcanoes ("av") where stratigraphic ("strat") figures are needed for interpreting eruptive histories, but the methods are applicable to any sediment stratigraphy project. Plotting styles inspired by "SedLog" (Zervas et al. 2009) <doi:10.1016/j.cageo.2009.02.009> but with more customizable outputs and flexible data input based on best practice recommendations for the tephra community (Wallace et al. 2022) <doi:10.1038/s41597-022-01515-y>.

This package implements a web-based graphics device for animated visualisations. Modelled on the base syntax, it extends the base graphics functions to support frame-by-frame animation and keyframes animation. The target use cases are real-time animated visualisations, including agent-based models, dynamical systems, and animated diagrams. The generated visualisations can be deployed as GIF images / MP4 videos, as Shiny apps (with interactivity) or as HTML documents through embedding into R Markdown documents.

Functionality for working with virtual machines (VMs) in Microsoft's Azure cloud: <https://azure.microsoft.com/en-us/services/virtual-machines/>. Includes facilities to deploy, startup, shutdown, and cleanly delete VMs and VM clusters. Deployment configurations can be highly customised, and can make use of existing resources as well as creating new ones. A selection of predefined configurations is provided to allow easy deployment of commonly used Linux and Windows images, including Data Science Virtual Machines. With a running VM, execute scripts and install optional extensions. Part of the AzureR family of packages.

The use of structured elicitation to inform decision making has grown dramatically in recent decades, however, judgements from multiple experts must be aggregated into a single estimate. Empirical evidence suggests that mathematical aggregation provides more reliable estimates than enforcing behavioural consensus on group estimates. aggreCAT provides state-of-the-art mathematical aggregation methods for elicitation data including those defined in Hanea, A. et al. (2021) <doi:10.1371/journal.pone.0256919>. The package also provides functions to visualise and evaluate the performance of your aggregated estimates on validation data.

We aim to deal with data with measurement error in the response and misclassification censoring status under an AFT model. This package primarily contains three functions, which are used to generate artificial data, correction for error-prone data and estimate the functional covariates for an AFT model.

This package provides a powerful tool for automating the early detection of disease outbreaks in time series data. aeddo employs advanced statistical methods, including hierarchical models, in an innovative manner to effectively characterize outbreak signals. It is particularly useful for epidemiologists, public health professionals, and researchers seeking to identify and respond to disease outbreaks in a timely fashion. For a detailed reference on hierarchical models, consult Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.

Wraps the Ace editor in a HTML widget. The Ace editor has support for many languages. It can be opened in the viewer pane of RStudio', and this provides a second source editor.