This package provides a distance density clustering (DDC) algorithm in R. DDC uses dynamic time warping (DTW) to compute a similarity matrix, based on which cluster centers and cluster assignments are found. DDC inherits dynamic time warping (DTW) arguments and constraints. The cluster centers are centroid points that are calculated using the DTW Barycenter Averaging (DBA) algorithm. The clustering process is divisive. At each iteration, cluster centers are updated and data is reassigned to cluster centers. Early stopping is possible. The output includes cluster centers and clustering assignment, as described in the paper (Ma et al (2017) <doi:10.1109/ICDMW.2017.11>).

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 for handling dates.

Prepare the results of a DCE to be analysed through choice models.'DCEmgmt reshapes DCE data from wide to long format considering the special characteristics of a DCE. DCEmgmt includes the function DCEestm which estimates choice models once the database has been reshaped with DCEmgmt'.

Decomposing value added growth into explanatory factors. A cost constrained value added function is defined to specify the production frontier. Industry estimates can also be aggregated using a weighted average approach. Details about the methodology and data can be found in Diewert and Fox (2018) <doi:10.1093/oxfordhb/9780190226718.013.19> and Zeng, Parsons, Diewert and Fox (2018) <https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/Documents/emg2018-6_SZeng_EMG-Slides.pdf>.

This package provides functions for computing the density, distribution, and random generation of the Decision Diffusion model (DDM), a widely used cognitive model for analysing choice and response time data. The package allows model specification, including the ability to fix, constrain, or vary parameters across experimental conditions. While it does not include a built-in optimiser, it supports likelihood evaluation and can be integrated with external tools for parameter estimation. Functions for simulating synthetic datasets are also provided. This package is intended for researchers modelling speeded decision-making in behavioural and cognitive experiments. For more information, see Voss, Rothermund, and Voss (2004) <doi:10.3758/BF03196893>, Voss and Voss (2007) <doi:10.3758/BF03192967>, and Ratcliff and McKoon (2008) <doi:10.1162/neco.2008.12-06-420>.

The desirable Dietary Pattern (DDP)/ PPH score measures the variety of food consumption. The (weighted) score is calculated based on the type of food. This package is intended to calculate the DDP/ PPH score that is faster than traditional method via a manual calculation by BKP (2017) <http://bkp.pertanian.go.id/storage/app/uploads/public/5bf/ca9/06b/5bfca906bc654274163456.pdf> and is simpler than the nutrition survey <http://www.nutrisurvey.de>. The database to create weights and baseline values is the Indonesia national survey in 2017.

Scientific and technical article format for the web. Distill articles feature attractive, reader-friendly typography, flexible layout options for visualizations, and full support for footnotes and citations.

Ecological Metadata Language or EML is a long-established format for describing ecological datasets to facilitate sharing and re-use. Because EML is effectively a modified xml schema, however, it is challenging to write and manipulate for non-expert users. delma supports users to write metadata statements in R Markdown or Quarto markdown format, and parse them to EML and (optionally) back again.

The method of synthetic controls is a widely-adopted tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) <doi:10.3982/ECTA18260>, that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them.

Works as a virtual CRAN snapshot for source packages. It automatically downloads and installs tar.gz files with dependencies, all of which were available on a specific day.

This package provides a tool to calculate the correlation boundary for the correlation between the response rate and the log-rank test statistic for the binary surrogate endpoint and the time-to-event primary endpoint, as well as conduct simulation studies to obtain design operating characteristics of the drop-the-losers design.

Modifies dot plots to have different sizes of dots mimicking violin plots and identifies modes or peaks for them based on frequency and kernel density estimates (Rosenblatt, 1956) <doi:10.1214/aoms/1177728190> (Parzen, 1962) <doi:10.1214/aoms/1177704472>.

This package provides a set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings. Compatible with and based on the R grid package.

Bayesian networks with continuous and/or discrete variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.

R interface for the Google Cloud Services Document AI API <https://cloud.google.com/document-ai> with additional tools for output file parsing and text reconstruction. Document AI is a powerful server-based OCR service that extracts text and tables from images and PDF files with high accuracy. daiR gives R users programmatic access to this service and additional tools to handle and visualize the output. See the package website <https://dair.info/> for more information and examples.

Allows users to quickly and easily detect data containing Personally Identifiable Information (PII) through convenience functions.

This package provides a software package for using DEXi models. DEXi models are hierarchical qualitative multi-criteria decision models developed according to the method DEX (Decision EXpert, <https://dex.ijs.si/documentation/DEX_Method/DEX_Method.html>), using the program DEXi (<https://kt.ijs.si/MarkoBohanec/dexi.html>) or DEXiWin (<https://dex.ijs.si/dexisuite/dexiwin.html>). A typical workflow with DEXiR consists of: (1) reading a .dxi file, previously made using the DEXi software (function read_dexi()), (2) making a data frame containing input values of one or more decision alternatives, (3) evaluating those alternatives (function evaluate()), (4) analyzing alternatives (selective_explanation(), plus_minus(), compare_alternatives()), (5) drawing charts. DEXiR is restricted to using models produced externally by the DEXi software and does not provide functionality for creating and/or editing DEXi models directly in R'.

Dynamic path analysis with estimation of the corresponding direct, indirect, and total effects, based on Fosen et al., (2006) <doi:10.1007/s10985-006-9004-2>. The main outcome of interest is a counting process from survival analysis (or recurrent events) data. At each time of event, ordinary linear regression is used to estimate the relation between the covariates, while Aalen's additive hazard model is used for the regression of the counting process on the covariates.

R codes for distance based cell lineage reconstruction. Our methods won both sub-challenges 2 and 3 of the Allen Institute Cell Lineage Reconstruction DREAM Challenge in 2020. References: Gong et al. (2021) <doi:10.1016/j.cels.2021.05.008>, Gong et al. (2022) <doi:10.1186/s12859-022-04633-x>.

DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015). Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions. Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) <doi:10.1016/j.cogpsych.2015.02.005>. Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs), a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008). Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common features from within standard diffusion models with the addition of superimposed controlled and automatic activation. The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality to fit the model to observed data. Further details are provided in the following paper: Mackenzie, I.G., & Dudschig, C. (2021). DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction time and error rate data. Methods in Psychology, 100074. <doi:10.1016/j.metip.2021.100074>.

Construction and analysis of matrix population models in R.

An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.

Go beyond standard probability distributions such as the Normal or Exponential by combining, shifting, maximizing, and otherwise transforming distributions with simple, verb-based functions. Provides easy access to a broader space of distributions more representative of real-world systems such as river flows or insurance claims. Part of the probaverse framework of packages to support advanced statistical modeling and simulations with an intuitive workflow.