Makes it easy to engage with the Application Program Interface (API) of the TCdata360 and Govdata360 platforms at <https://tcdata360.worldbank.org/> and <https://govdata360.worldbank.org/>, respectively. These application program interfaces provide access to over 5000 trade, competitiveness, and governance indicator data, metadata, and related information from sources both inside and outside the World Bank Group. Package functions include easier download of data sets, metadata, and related information, as well as searching based on user-inputted query.

Calculate multiple or pairwise dissimilarity for orders q = 0-N (CqN; Chao et al. 2008 <doi:10/fcvn63>) for a set of species assemblages or interaction networks.

This package provides functions for (1) ranking, selecting, and prioritising genes, proteins, and metabolites from high dimensional biology experiments, (2) multivariate hit calling in high content screens, and (3) combining data from diverse sources.

This package provides a revision to the stats::ks.test() function and the associated ks.test.Rd help page. With one minor exception, it does not change the existing behavior of ks.test(), and it adds features necessary for doing one-sample tests with hypothesized discrete distributions. The package also contains cvm.test(), for doing one-sample Cramer-von Mises goodness-of-fit tests.

This package provides a set of utilities for calculating the Deficit (frailty) Index (DI) in gerontological studies. The deficit index was first proposed by Arnold Mitnitski and Kenneth Rockwood and represents a proxy measure of aging and also can be served as a sensitive predictor of survival. For more information, see (i)"Accumulation of Deficits as a Proxy Measure of Aging" by Arnold B. Mitnitski et al. (2001), The Scientific World Journal 1, <DOI:10.1100/tsw.2001.58>; (ii) "Frailty, fitness and late-life mortality in relation to chronological and biological age" by Arnold B Mitnitski et al. (2001), BMC Geriatrics2002 2(1), <DOI:10.1186/1471-2318-2-1>.

Uses species occupancy at coarse grain sizes to predict species occupancy at fine grain sizes. Ten models are provided to fit and extrapolate the occupancy-area relationship, as well as methods for preparing atlas data for modelling. See Marsh et. al. (2018) <doi:10.18637/jss.v086.c03>.

Basic routines used in scientific coding, such as timing routines, vector/array handing functions and I/O support routines.

Graphical interface for loading datasets in RStudio from all installed (including unloaded) packages, also includes command line interfaces.

In order to provide unified access to Linux distribution details in R, this package wraps the various files and commands that may exist on a system. It is similar in spirit to the lsb_release command and the Python package of the same name.

Downloads the public data available from the Brazilian Access to Information Law and and performs a search on information requests and appeals made since 2015.

An interactive image editing tool that can be added as part of the HTML in Shiny, R markdown or any type of HTML document. Often times, plots, photos are embedded in the web application/file. drawer can take screenshots of these image-like elements, or any part of the HTML document and send to an image editing space called canvas to allow users immediately edit the screenshot(s) within the same document. Users can quickly combine, compare different screenshots, upload their own images and maybe make a scientific figure.

Functionalities for analyzing high-dimensional and longitudinal biomarker data to facilitate precision medicine, using a joint model of Bayesian sparse factor analysis and dependent Gaussian processes. This paper illustrates the method in detail: J Cai, RJB Goudie, C Starr, BDM Tom (2023) <doi:10.48550/arXiv.2307.02781>.

This package provides a tool developed with the Golem framework which provides an easier way to check cells differences between two data frames. The user provides two data frames for comparison, selects IDs variables identifying each row of input data, then clicks a button to perform the comparison. Several R package functions are used to describe the data and perform the comparison in the server of the application. The main ones are comparedf() from arsenal and skim() from skimr'. For more details see the description of comparedf() from the arsenal package and that of skim() from the skimr package.

This package provides a set of algorithms based on Quinn et al. (1991) <doi:10.1002/hyp.3360050106> for processing river network and digital elevation data to build implementations of Dynamic TOPMODEL, a semi-distributed hydrological model proposed in Beven and Freer (2001) <doi:10.1002/hyp.252>. The dynatop package implements simulation code for Dynamic TOPMODEL based on the output of dynatopGIS'.

Solves quadratic programming problems using Richard L. Dykstra's cyclic projection algorithm. Routine allows for a combination of equality and inequality constraints. See Dykstra (1983) <doi:10.1080/01621459.1983.10477029> for details.

This package contains one main function deduped() which speeds up slow, vectorized functions by only performing computations on the unique values of the input and expanding the results at the end.

Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package BayesTree'.

Dose Titration Algorithm Tuning (DTAT) is a methodologic framework allowing dose individualization to be conceived as a continuous learning process that begins in early-phase clinical trials and continues throughout drug development, on into clinical practice. This package includes code that researchers may use to reproduce or extend key results of the DTAT research programme, plus tools for trialists to design and simulate a 3+3/PC dose-finding study. Please see Norris (2017a) <doi:10.12688/f1000research.10624.3> and Norris (2017c) <doi:10.1101/240846>.

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012>; iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225>; or the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses the Armadillo C++ library and the collapse package for fast estimation. A comprehensive set of methods supports interpretation and visualization, forecasting, and decomposition of the news content of macroeconomic data releases following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

This package provides functions to impute large gaps within multivariate time series based on Dynamic Time Warping methods. Gaps of size 1 or inferior to a defined threshold are filled using simple average and weighted moving average respectively. Larger gaps are filled using the methodology provided by Phan et al. (2017) <DOI:10.1109/MLSP.2017.8168165>: a query is built immediately before/after a gap and a moving window is used to find the most similar sequence to this query using Dynamic Time Warping. To lower the calculation time, similar sequences are pre-selected using global features. Contrary to the univariate method (package DTWBI'), these global features are not estimated over the sequence containing the gap(s), but a feature matrix is built to summarize general features of the whole multivariate signal. Once the most similar sequence to the query has been identified, the adjacent sequence to this window is used to fill the gap considered. This function can deal with multiple gaps over all the sequences componing the input multivariate signal. However, for better consistency, large gaps at the same location over all sequences should be avoided.

Dual Scaling, developed by Professor Shizuhiko Nishisato (1994, ISBN: 0-9691785-3-6), is a fundamental technique in multivariate analysis used for data scaling and correspondence analysis. Its utility lies in its ability to represent multidimensional data in a lower-dimensional space, making it easier to visualize and understand underlying patterns in complex data. This technique has been implemented to handle various types of data, including Contingency and Frequency data (CF), Multiple-Choice data (MC), Sorting data (SO), Paired-Comparison data (PC), and Rank-Order data (RO), providing users with a powerful tool to explore relationships between variables and observations in various fields, from sociology to ecology, enabling deeper and more efficient analysis of multivariate datasets.

This package implements fast Monte Carlo simulations for goodness-of-fit (GOF) tests for discrete distributions. This includes tests based on the Chi-squared statistic, the log-likelihood-ratio (G^2) statistic, the Freeman-Tukey (Hellinger-distance) statistic, the Kolmogorov-Smirnov statistic, the Cramer-von Mises statistic as described in Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828>, and the root-mean-square statistic, see Perkins, Tygert, and Ward (2011) <doi:10.1016/j.amc.2011.03.124>.

View 2D/3D sections, contour plots, mesh of excursion sets for computer experiments designs, surrogates or test functions.

Access data sets for demonstrating or testing diagnostic classification models. Simulated data sets can be used to compare estimated model output to true data-generating values. Real data sets can be used to demonstrate real-world applications of diagnostic models.