Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. The ggscidca package adds coloured bars of discriminant relevance to the traditional decision curve. Improved practicality and aesthetics. This method was described by Balachandran VP (2015) <doi:10.1016/S1470-2045(14)71116-7>.

This package provides functions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in igraph objects. Intended to extend mvtnorm to take igraph structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses.

Evaluate and validate the Geboes score for histological assessment of inflammation in ulcerative colitis. The original Geboes score from Geboes, et al. (2000) <doi:10.1136/gut.47.3.404>, binary version from Li, et al. (2019) <doi:10.1093/ecco-jcc/jjz022>, and continuous version from Magro, et al. (2020) <doi:10.1093/ecco-jcc/jjz123> are all described and implemented.

Constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications. The method is described in Drozdenko and Drake (2002), "Optimal Database Marketing", Chapter 11.

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 set up, train, store, load, investigate and analyze generative neural networks. In particular, functionality for generative moment matching networks is provided.

Define and compute with generalized spherical distributions - multivariate probability laws that are specified by a star shaped contour (directional behavior) and a radial component. The methods are described in Nolan (2016) <doi:10.1186/s40488-016-0053-0>.

Plot density and distribution functions with automatic selection of suitable regions. Numerically invert (compute quantiles) distribution functions. Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages.

This package provides a collection of Geoms for R's ggplot2 library. geom_shadowpath(), geom_shadowline(), geom_shadowstep() and geom_shadowpoint() functions draw a shadow below lines to make busy plots more aesthetically pleasing. geom_glowpath(), geom_glowline(), geom_glowstep() and geom_glowpoint() add a neon glow around lines to get a steampunk style.

GPU'/CPU Benchmarking on Debian-package based systems This package benchmarks performance of a few standard linear algebra operations (such as a matrix product and QR, SVD and LU decompositions) across a number of different BLAS libraries as well as a GPU implementation. To do so, it takes advantage of the ability to plug and play different BLAS implementations easily on a Debian and/or Ubuntu system. The current version supports - Reference BLAS ('refblas') which are un-accelerated as a baseline - Atlas which are tuned but typically configure single-threaded - Atlas39 which are tuned and configured for multi-threaded mode - Goto Blas which are accelerated and multi-threaded - Intel MKL which is a commercial accelerated and multithreaded version. As for GPU computing, we use the CRAN package - gputools For Goto Blas', the gotoblas2-helper script from the ISM in Tokyo can be used. For Intel MKL we use the Revolution R packages from Ubuntu 9.10.

This package provides a Gaussian or Student's t copula-based procedure for generating samples from discrete random variables with prescribed correlation matrix and marginal distributions.

This package provides functions to compute generalized eigenvalues and eigenvectors, the generalized Schur decomposition and the generalized Singular Value Decomposition of a matrix pair, using Lapack routines.

Allows user to have graphical user interface to perform analysis of Agricultural experimental data. On using the functions in this package a Interactive User Interface will pop up. Apps Works by simple upload of files in CSV format.

Plot brain atlases as interactive 3D meshes using Three.js via htmlwidgets', or render publication-quality static images through rgl and rayshader'. A pipe-friendly API lets you map data onto brain regions, control camera angles, toggle region edges, overlay glass brains, and snapshot or ray-trace the result. Additional atlases are available through the ggsegverse r-universe. Mowinckel & Vidal-Piñeiro (2020) <doi:10.1177/2515245920928009>.

Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.

Simple package to download Google Sheets using just the sharing link. Spreadsheets can be downloaded as a data frame, or as plain text to parse manually. Google Sheets is the new name for Google Docs Spreadsheets <https://www.google.com/sheets/about>.

Computes the gravitational and magnetic anomalies generated by 3-D vertical rectangular prisms at specific observation points using the method of Plouff (1976) <doi:10.1190/1.1440645>.

Reads data collected from wearable acceleratometers as used in sleep and physical activity research. Currently supports file formats: binary data from GENEActiv <https://activinsights.com/>, .bin-format from GENEA devices (not for sale), and .cwa-format from Axivity <https://axivity.com>. Further, it has functions for reading text files with epoch level aggregates from Actical', Fitbit', Actiwatch', ActiGraph', and PhilipsHealthBand'. Primarily designed to complement R package GGIR <https://CRAN.R-project.org/package=GGIR>.

Simulate and analyze multistate models with general hazard functions. gems provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed.

This package provides the standard operations for signal processing on graphs: graph Fourier transform, spectral graph wavelet transform, visualization tools. It also implements a data driven method for graph signal denoising/regression, for details see De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>. The package also provides an interface to the SuiteSparse Matrix Collection, <https://sparse.tamu.edu/>, a large and widely used set of sparse matrix benchmarks collected from a wide range of applications.

Efficient computation of likelihoods in design-based choice response time models, including the Decision Diffusion Model, is supported. The package enables rapid evaluation of likelihood functions for both single- and multi-subject models across trial-level data. It also offers fast initialisation of starting parameters for genetic sampling with many Markov chains, facilitating estimation in complex models typically found in experimental psychology and behavioural science. These optimisations help reduce computational overhead in large-scale model fitting tasks.

This package provides a mechanism to plot a Google Map from R and overlay it with shapes and markers. Also provides access to Google Maps APIs, including places, directions, roads, distances, geocoding, elevation and timezone.

Homogenize GNSS (Global Navigation Satellite System) time-series. The general model is a segmentation in the mean model including a periodic function and considering monthly variances, see Quarello (2020) <arXiv:2005.04683>.

This package provides a wrapper of different standard estimation methods for gravity models. This package provides estimation methods for log-log models and multiplicative models.