Gives access to data visualisation methods that are relevant from the statistician's point of view. Using D3''s existing data visualisation tools to empower R language and environment. The throw chart method is a line chart used to illustrate paired data sets (such as before-after, male-female).

Hash an expression with its dependencies and store its value, reloading it from a file as long as both the expression and its dependencies stay the same.

Interactively train neural networks on Numerai, <https://numer.ai/>, data. Generate tournament predictions and write them to a CSV.

It is sometimes necessary to create documentation for all files in a directory. Doing so by hand can be very tedious. This task is made fast and reproducible using the functionality of documenter'. It aggregates all text files in a directory and its subdirectories into a single word document in a semi-automated fashion.

Preferred methods for common analytical tasks that are undertaken across the Department, including number formatting, project templates and curated reference data.

Estimation and testing methods for dependently truncated data. Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>, and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>. Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>. A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>. Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.

Traditional phasing programs are limited to diploid organisms. Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haplotype searches in a multiple infection setting. This package is primarily developed as part of the Pf3k project, which is a global collaboration using the latest sequencing technologies to provide a high-resolution view of natural variation in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from patient blood sample, which often contains more than one parasite strain, with unknown proportions. This package is used for deconvoluting mixed haplotypes, and reporting the mixture proportions from each sample.

This package implements the deflist class, a read-only list-like object that accesses its elements via a function. The deflist class can be used to model deferred access to data or computations by routing indexed list access to a function. This approach is particularly useful when sequential list-like access to data is required but holding all the data in memory at once is not feasible. The package also provides utilities for memoisation and caching to optimize access to frequently requested elements.

Dynamic simulations and graphical depictions of autoregressive relationships.

This package provides statistical tools for analyzing net and relative survival, with a key feature of relaxing the assumption of independent censoring and incorporating the effect of dependent competing risks. It employs a copula-based methodology, specifically the Archimedean copula, to simulate data, conduct survival analysis, and offer comparisons with other methods. This approach is detailed in the work of Adatorwovor et al. (2022) <doi:10.1515/ijb-2021-0016>.

This package provides a two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.

This package provides a unified framework to building Area Deprivation Index (ADI), Social Vulnerability Index (SVI), and Neighborhood Deprivation Index (NDI) deprivation measures and accessing related data from the U.S. Census Bureau such as Gini coefficient data. Tools are also available for calculating percentiles, quantiles, and for creating clear map breaks for data visualization.

Create shareable data sets from raw data files that contain protected elements. Relying on master crosswalk files that list restricted variables, package functions warn users about possible violations of data usage agreement and prevent writing protected elements.

This package implements the dynamic panel models described by Allison, Williams, and Moral-Benito (2017 <doi:10.1177/2378023117710578>) in R. This class of models uses structural equation modeling to specify dynamic (lagged dependent variable) models with fixed effects for panel data. Additionally, models may have predictors that are only weakly exogenous, i.e., are affected by prior values of the dependent variable. Options also allow for random effects, dropping the lagged dependent variable, and a number of other specification choices.

Provee una versión traducida de los siguientes conjuntos de datos: airlines', airports', AwardsManagers', babynames', Batting', credit_data', diamonds', faithful', fueleconomy', Fielding', flights', gapminder', gss_cat', iris', Managers', mpg', mtcars', atmos', palmerpenguins', People, Pitching', planes', presidential', table1', table2', table3', table4a', table4b', table5', vehicles', weather', who'. English: It provides a Spanish translated version of the datasets listed above.

This package provides functions to pipe data from R to DataGraph', a graphing and analysis application for mac OS. Create a live connection using either .dtable or .dtbin files that can be read by DataGraph'. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://community.visualdatatools.com/datagraph/knowledge-base/r-package/>.

Build a Dockerfile straight from your R session. dockerfiler allows you to create step by step a Dockerfile, and provide convenient tools to wrap R code inside this Dockerfile.

Calculates the desparsified lasso as originally introduced in van de Geer et al. (2014) <doi:10.1214/14-AOS1221>, and provides inference suitable for high-dimensional time series, based on the long run covariance estimator in Adamek et al. (2020) <arXiv:2007.10952>. Also estimates high-dimensional local projections by the desparsified lasso, as described in Adamek et al. (2022) <arXiv:2209.03218>.

This package provides methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

Fits disaggregation regression models using TMB ('Template Model Builder'). When the response data are aggregated to polygon level but the predictor variables are at a higher resolution, these models can be useful. Regression models with spatial random fields. The package is described in detail in Nandi et al. (2023) <doi:10.18637/jss.v106.i11>.

Transform newswire and earnings call transcripts as PDF obtained from Nexis Uni to R data frames. Various newswires and FairDisclosure earnings call formats are supported. Further, users can apply several pre-defined dictionaries on the data based on Graffin et al. (2016)<doi:10.5465/amj.2013.0288> and Gamache et al. (2015)<doi:10.5465/amj.2013.0377>.

Client for programmatic access to the South Florida Water Management District's DBHYDRO database at <https://www.sfwmd.gov/science-data/dbhydro>, with functions for accessing hydrologic and water quality data.

Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).

Fits, bootstraps, and evaluates two-component normal and lognormal mixture models. Includes diagnostic plots and statistical evaluation of mixture model fits using differential evolution optimization.