Phase I/II adaptive dose-finding design for combination studies where toxicity rates are supposed to increase with both agents.

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.

This package provides several datasets used throughout the book "Sampling and Data Analysis Using R: Theory and Practice" by Islam (2025, ISBN:978-984-35-8644-5). The datasets support teaching and learning of statistical concepts such as sampling methods, descriptive analysis, estimation and basic data handling. These curated data objects allow instructors, students and researchers to reproduce examples, practice data manipulation and perform hands-on analysis using R.

This package implements common measures of diversity and spatial segregation. This package has tools to compute the majority of measures are reviewed in Massey and Denton (1988) <doi:10.2307/2579183>. Multiple common measures of within-geography diversity are implemented as well. All functions operate on data frames with a tidyselect based workflow.

The dfmirroR package allows users to input a data frame, simulate some number of observations based on specified columns of that data frame, and then outputs a string that contains the code to re-create the simulation. The goal is to both provide workable test data sets and provide users with the information they need to set up reproducible examples with team members. This package was created out of a need to share examples in cases where data are private and where a full data frame is not needed for testing or coordinating.

Given a set of predictive quantiles from a distribution, estimate the distribution and create `d`, `p`, `q`, and `r` functions to evaluate its density function, distribution function, and quantile function, and generate random samples. On the interior of the provided quantiles, an interpolation method such as a monotonic cubic spline is used; the tails are approximated by a location-scale family.

Mixed model analysis for quantitative genetics with multi-trait responses and pedigree-based partitioning of individual variation into a range of environmental and genetic variance components for individual and maternal effects. Method documented in dmmOverview.pdf; dmm is an implementation of dispersion mean model described by Searle et al. (1992) "Variance Components", Wiley, NY. Dmm() can do MINQUE', bias-corrected-ML', and REML variance and covariance component estimates.

Dynamic simulations and graphical depictions of autoregressive relationships.

Offers robust tools to identify and manage incomplete responses in survey datasets, thereby enhancing the quality and reliability of research findings.

Implementation of double machine learning (DML) algorithms in R, based on Emmenegger and Buehlmann (2021) "Regularizing Double Machine Learning in Partially Linear Endogenous Models" <arXiv:2101.12525> and Emmenegger and Buehlmann (2021) <arXiv:2108.13657> "Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements". First part: our goal is to perform inference for the linear parameter in partially linear models with confounding variables. The standard DML estimator of the linear parameter has a two-stage least squares interpretation, which can lead to a large variance and overwide confidence intervals. We apply regularization to reduce the variance of the estimator, which produces narrower confidence intervals that are approximately valid. Nuisance terms can be flexibly estimated with machine learning algorithms. Second part: our goal is to estimate and perform inference for the linear coefficient in a partially linear mixed-effects model with DML. Machine learning algorithms allows us to incorporate more complex interaction structures and high-dimensional variables.

Fit a Poisson regression to carcass distance data and integrate over the searched area at a wind farm to estimate the fraction of carcasses falling in the searched area and format the output for use as the dwp parameter in the GenEst or eoa package for estimating bird and bat mortality, following Dalthorp, et al. (2022) <arXiv:2201.10064>.

This package provides methods by Steinhauser et al. (2016) <DOI:10.1186/s12874-016-0196-1> for meta-analysis of diagnostic accuracy studies with several cutpoints.

Secant acceleration applied to derivative-free Spectral Residual Methods for solving large-scale nonlinear systems of equations. The main reference follows: E. G. Birgin and J. M. Martinez (2022) <doi:10.1137/20M1388024>.

Robust distance-based methods applied to matrices and data frames, producing distance matrices that can be used as input for various visualization techniques such as graphs, heatmaps, or multidimensional scaling configurations. See Boj and Grané (2024) <doi:10.1016/j.seps.2024.101992>.

Solves ordinary and delay differential equations, where the objective function is written in either R or C. Suitable only for non-stiff equations, the solver uses a Dormand-Prince method that allows interpolation of the solution at any point. This approach is as described by Hairer, Norsett and Wanner (1993) <ISBN:3540604529>. Support is also included for iterating difference equations.

This package provides a general framework using mixture Weibull distributions to accurately predict biomarker-guided trial duration accounting for heterogeneous population. Extensive simulations are performed to evaluate the impact of heterogeneous population and the dynamics of biomarker characteristics and disease on the study duration. Several influential parameters including median survival time, enrollment rate, biomarker prevalence and effect size are identified. Efficiency gains of biomarker-guided trials can be quantitatively compared to the traditional all-comers design. For reference, see Zhang et al. (2024) <arXiv:2401.00540>.

This package provides external JAR dependencies for the DatabaseConnector package.

Edit and validate taxonomic data in compliance with Darwin Core standards (Darwin Core Taxon class <https://dwc.tdwg.org/terms/#taxon>).

This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice.

This package implements various decision support tools related to the Econometrics & Technometrics. Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, network DEA, dynamic DEA, intertemporal budgeting, etc.

Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the bnlearn package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014) <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021) <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the visNetwork package.

While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. We present a novel network analysis pipeline, DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e., molecular differences that are the source of high differential drug scores can be retrieved. Our proposed pipeline leverages multi-omics data for differential predictions, e.g. on drug response, and includes prior information on interactions. The case study presented in the vignette uses data published by Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only to the software and explicitly not to the included data.

The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.

Discriminant Adaptive Nearest Neighbor Classification is a variation of k nearest neighbors where the shape of the neighborhood is data driven. This package implements dann and sub_dann from Hastie (1996) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.