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

Computes small-sample degrees of freedom adjustment for heteroskedasticity robust standard errors, and for clustered standard errors in linear regression. See Imbens and Kolesár (2016) <doi:10.1162/REST_a_00552> for a discussion of these adjustments.

This package provides methods for distance covariance and distance correlation (Szekely, et al. (2007) <doi:10.1214/009053607000000505>), generalized version thereof (Sejdinovic, et al. (2013) <doi:10.1214/13-AOS1140>) and corresponding tests (Berschneider, Bottcher (2018) <doi:10.48550/arXiv.1808.07280>. Distance standard deviation methods (Edelmann, et al. (2020) <doi:10.1214/19-AOS1935>) and distance correlation methods for survival endpoints (Edelmann, et al. (2021) <doi:10.1111/biom.13470>) are also included.

This package provides methods for fitting nonstationary Gaussian process models by spatial deformation, as introduced by Sampson and Guttorp (1992) <doi:10.1080/01621459.1992.10475181>, and by dimension expansion, as introduced by Bornn et al. (2012) <doi:10.1080/01621459.2011.646919>. Low-rank thin-plate regression splines, as developed in Wood, S.N. (2003) <doi:10.1111/1467-9868.00374>, are used to either transform co-ordinates or create new latent dimensions.

Allows humanitarian community, academia, media, government, and non-governmental organizations to utilize the data collected by the Displacement Tracking Matrix (<https://dtm.iom.int>), a unit in the International Organization for Migration. This also provides non-sensitive Internally Displaced Person figures, aggregated at the country, Admin 1 (states, provinces, or equivalent), and Admin 2 (smaller administrative areas) levels.

This package implements a generalized linear model approach for detecting differentially expressed genes across treatment groups in count data. The package supports both quasi-Poisson and negative binomial models to handle over-dispersion, ensuring robust identification of differential expression. It allows for the inclusion of treatment effects and gene-wise covariates, as well as normalization factors for accurate scaling across samples. Additionally, it incorporates statistical significance testing with options for p-value adjustment and log2 fold range thresholds, making it suitable for RNA-seq analysis as described in by Xu et al., (2024) <doi:10.1371/journal.pone.0300565>.

Data sets and sample analyses from Jay L. Devore (2008), "Probability and Statistics for Engineering and the Sciences (7th ed)", Thomson.

Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.

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

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>.

First using dada2 R tools to analyse metabarcode data, the DBTC package then uses the BLAST algorithm to search unknown sequences against local databases, and then takes reduced matched results and provides best taxonomic assignments.

Estimate the Deterministic Input, Noisy "And" Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi:10.3102/1076998615595403>.

An anonymization algorithm to resist neighbor label attack in a dynamic network.

DataSHIELD is an infrastructure and series of R packages that enables the remote and non-disclosive analysis of sensitive research data. This package is the DataSHIELD interface implementation to analyze data shared on a MOLGENIS Armadillo server. MOLGENIS Armadillo is a light-weight DataSHIELD server using a file store and an RServe server.

Three global value chain (GVC) decompositions are implemented. The Leontief decomposition derives the value added origin of exports by country and industry as in Hummels, Ishii and Yi (2001). The Koopman, Wang and Wei (2014) decomposition splits country-level exports into 9 value added components, and the Wang, Wei and Zhu (2013) decomposition splits bilateral exports into 16 value added components. Various GVC indicators based on these decompositions are computed in the complimentary gvc package. --- References: --- Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of vertical specialization in world trade. Journal of international Economics, 54(1), 75-96. Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double counting in gross exports. American Economic Review, 104(2), 459-94. Wang, Z., Wei, S. J., & Zhu, K. (2013). Quantifying international production sharing at the bilateral and sector levels (No. w19677). National Bureau of Economic Research.

Open, read data from and modify Data Packages. Data Packages are an open standard for bundling and describing data sets (<https://datapackage.org>). When data is read from a Data Package care is taken to convert the data as much a possible to R appropriate data types. The package can be extended with plugins for additional data types.

This package provides a collection of data-limited management procedures that can be evaluated with management strategy evaluation with the MSEtool package, or applied to fishery data to provide management recommendations.

This package implements a likelihood-based method for genome polarization, identifying which alleles of SNV markers belong to either side of a barrier to gene flow. The approach co-estimates individual assignment, barrier strength, and divergence between sides, with direct application to studies of hybridization. Includes VCF-to-diem conversion and input checks, support for mixed ploidy and parallelization, and tools for visualization and diagnostic outputs. Based on diagnostic index expectation maximization as described in Baird et al. (2023) <doi:10.1111/2041-210X.14010>.

It provides the subset operator for dist objects and a function to compute medoid(s) that are fully parallelized leveraging the RcppParallel package. It also provides functions for package developers to easily implement their own parallelized dist() function using a custom C++'-based distance function.

Mimics the demo functionality for Shiny apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic).

Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials. For details of the methodology, please refer to D.O. Dixon and R. Simon (1991), Biometrics, 47: 871-881.

Demonstration code showing how (univariate) kernel density estimates are computed, at least conceptually, and allowing users to experiment with different kernels, should they so wish. The method used follows directly the definition, but gains efficiency by replacing the observations by frequencies in a very fine grid covering the sample range. A canonical reference is B. W. Silverman, (1998) <doi: 10.1201/9781315140919>. NOTE: the density function in the stats package uses a more sophisticated method based on the fast Fourier transform and that function should be used if computational efficiency is a prime consideration.

Draw, manipulate, and evaluate directed acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777.

Simulates demic diffusion building on models previously developed for the expansion of Neolithic and other food-producing economies during the Holocene (Fort et al. (2012) <doi:10.7183/0002-7316.77.2.203>, Souza et al. (2021) <doi:10.1098/rsif.2021.0499>). Growth and emigration are modelled as density-dependent processes using logistic growth and an asymptotic threshold model. Environmental and terrain layers, which can change over time, affect carrying capacity, growth and mobility. Multiple centres of origin with their respective starting times can be specified.