An implementation of deliberative reasoning index (DRI) and related tools for analysis of deliberation survey data. Calculation of DRI, plot of intersubjective correlations (IC), generation of large-language model (LLM) survey data, and permutation tests are supported. Example datasets and a graphical user interface (GUI) are also available to support analysis. For more information, see Niemeyer and Veri (2022) <doi:10.1093/oso/9780192848925.003.0007>. For an alternative version of this dataset, see Niemeyer et al. (2024) <doi:10.1017/S0003055423000023>.

This package provides efficient Markov chain Monte Carlo (MCMC) algorithms for dynamic shrinkage processes, which extend global-local shrinkage priors to the time series setting by allowing shrinkage to depend on its own past. These priors yield locally adaptive estimates, useful for time series and regression functions with irregular features. The package includes full MCMC implementations for trend filtering using dynamic shrinkage on signal differences, producing locally constant or linear fits with adaptive credible bands. Also included are models with static shrinkage and normal-inverse-Gamma priors for comparison. Additional tools cover dynamic regression with time-varying coefficients and B-spline models with shrinkage on basis differences, allowing for flexible curve-fitting with unequally spaced data. Some support for heteroscedastic errors, outlier detection, and change point estimation. Methods in this package are described in Kowal et al. (2019) <doi:10.1111/rssb.12325>, Wu et al. (2024) <doi:10.1080/07350015.2024.2362269>, Schafer and Matteson (2024) <doi:10.1080/00401706.2024.2407316>, and Cho and Matteson (2024) <doi:10.48550/arXiv.2408.11315>.

This package provides a user-friendly wrapper for fitting Distributed Single-Index Models with Multiple Links ('SIMML'). It estimates a linear combination (single-index) of baseline covariates X and models the treatment-specific outcome y via treatment-specific, non-parametrically estimated link functions. This allows flexible modelling of complex relationships between covariates, treatments and outcomes.

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.

Several quality measurements for investigating the performance of dimensionality reduction methods are provided here. In addition a new quality measurement called Gabriel classification error is made accessible, which was published in Thrun, M. C., Märte, J., & Stier, Q: "Analyzing Quality Measurements for Dimensionality Reduction" (2023), Machine Learning and Knowledge Extraction (MAKE), <DOI:10.3390/make5030056>.

This package implements Design-Robust Meta-Analysis (DR-Meta), a variance-function random-effects framework in which between-study heterogeneity is modelled as a function of a study-level design robustness index, allowing heterogeneity to depend systematically on study quality or design strength rather than being treated as a single nuisance parameter. The package provides profiled restricted maximum likelihood (REML) estimation of the overall effect and variance-function parameters, study-specific weights, heterogeneity diagnostics (tau-squared, I-squared), influence and leave-one-out analysis, and graphical tools including forest plots and influence plots. The DR-Meta framework nests classical fixed-effects and standard random-effects meta-analysis as special cases, making it a strict generalisation of existing approaches.

This package creates define.xml documents used for regulatory submissions based on spreadsheet metadata. Can also help create metadata and generate HTML data explorer.

This package performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.

This package provides functions for fitting Cox proportional hazards models for grouped time-to-event data, where the shared group-specific frailties have a discrete nonparametric distribution. The methods proposed in the package is described by Gasperoni, F., Ieva, F., Paganoni, A. M., Jackson, C. H., Sharples, L. (2018) <doi:10.1093/biostatistics/kxy071>. There are also functions for simulating from these models, with a nonparametric or a parametric baseline hazard function.

This package provides a system for combining two diagnostic tests using various approaches that include statistical and machine-learning-based methodologies. These approaches are divided into four groups: linear combination methods, non-linear combination methods, mathematical operators, and machine learning algorithms. See the <https://biotools.erciyes.edu.tr/dtComb/> website for more information, documentation, and examples.

Enhances decision tree visualization by incorporating Generalized Association Plots (GAP) through matrix-based visualizations including confusion matrix maps, decision tree matrix maps, and predicted class membership maps based on supervised correlation and distance metrics.

The Data Driven I-V Feature Extraction is used to extract Current-Voltage (I-V) features from I-V curves. I-V curves indicate the relationship between current and voltage for a solar cell or Photovoltaic (PV) modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) contain important information of the performance for PV modules. The traditional method uses the single diode model to model I-V curves and extract I-V features. This package does not use the diode model, but uses data-driven a method which select different linear parts of the I-V curves to extract I-V features. This method also uses a sampling method to calculate uncertainties when extracting I-V features. Also, because of the partially shaded array, "steps" occurs in I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves. This material is based upon work supported by the U.S. Department of Energyâ s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140. Further information can be found in the following paper. [1] Ma, X. et al, 2019. <doi:10.1109/JPHOTOV.2019.2928477>.

This package provides a facility to generate efficient designs for order-of-additions experiments under pair-wise-order model, see Dennis K. J. Lin and Jiayu Peng (2019)."Order-of-addition experiments: A review and some new thoughts". Quality Engineering, 31:1, 49-59, <doi:10.1080/08982112.2018.1548021>. It also provides a facility to generate component orthogonal arrays under component position model, see Jian-Feng Yang, Fasheng Sun & Hongquan Xu (2020): "A Component Position Model, Analysis and Design for Order-of-Addition Experiments". Technometrics, <doi:10.1080/00401706.2020.1764394>.

This package provides a toolkit for parsing dice notation, analyzing rolls, calculating success probabilities, and plotting outcome distributions.

Analysis and visualization of dropout between conditions in surveys and (online) experiments. Features include computation of dropout statistics, comparing dropout between conditions (e.g. Chi squared), analyzing survival (e.g. Kaplan-Meier estimation), comparing conditions with the most different rates of dropout (Kolmogorov-Smirnov) and visualizing the result of each in designated plotting functions. Article published in _Behavior Research Methods_ on dropR by the authors: Dropout analysis: A method for data from Internet-based research and dropR', an R-based web app and package to analyze and visualize dropout. (2025) <doi:10.3758/s13428-025-02730-2>. Sources: Andrea Frick, Marie-Terese Baechtiger & Ulf-Dietrich Reips (2001) <doi:10.5167/uzh-19758>; Ulf-Dietrich Reips (2002) <doi:10.1026//1618-3169.49.4.243>.

Includes functions for the construction of matched samples that are balanced and representative by design. Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs. Also, they can be used for the design of randomized experiments, for example, for matching before randomization. By default, designmatch uses the highs optimization solver, but its performance is greatly enhanced by the Gurobi optimization solver and its associated R interface. For their installation, please follow the instructions at <https://www.gurobi.com/getting-started/> and <https://docs.gurobi.com/projects/optimizer/en/current/reference/r/setup.html>. We have also included directions in the gurobi_installation file in the inst folder.

Using a Gaussian copula approach, this package generates simulated data mimicking a target real dataset. It supports normal, Poisson, empirical, and DESeq2 (negative binomial with size factors) marginal distributions. It uses an low-rank plus diagonal covariance matrix to efficiently generate omics-scale data. Methods are described in: Yang, Grant, and Brooks (2025) <doi:10.1101/2025.01.31.634335>.

An efficient and convenient set of functions to perform differential network estimation through the use of alternating direction method of multipliers optimization with a variety of different loss functions.

This package contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.

Work within the dplyr workflow to add random variates to your data frame. Variates can be added at any level of an existing column. Also, bounds can be specified for simulated variates.

An RStudio addin for teaching and learning data manipulation using the dplyr package. You can learn each steps of data manipulation by clicking your mouse without coding. You can get resultant data (as a tibble') and the code for data manipulation.

Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization. Yu and Rosenbaum (2019) <doi:10.1111/biom.13098>.

Estimation of dark diversity and site-specific species pools using species co-occurrences. It includes implementations of probabilistic dark diversity based on the Hypergeometric distribution, as well as estimations based on the Beals index, which can be transformed to binary predictions using different thresholds, or transformed into a favorability index. All methods include the possibility of using a calibration dataset that is used to estimate the indication matrix between pairs of species, or to estimate dark diversity directly on a single dataset. See De Caceres and Legendre (2008) <doi:10.1007/s00442-008-1017-y>, Lewis et al. (2016) <doi:10.1111/2041-210X.12443>, Partel et al. (2011) <doi:10.1016/j.tree.2010.12.004>, Real et al. (2017) <doi:10.1093/sysbio/syw072> for further information.

Produce publication quality graphics from output of GGobi describe display plugin.