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>.

This package provides a Shiny Input for date-ranges, which pops up two calendars for selecting dates, times, or predefined ranges like "Last 30 Days". It wraps the JavaScript library daterangepicker which is available at <https://www.daterangepicker.com>.

This package provides programmatic access to the Dark Sky API <https://darksky.net/dev/docs>, which provides current or historical global weather conditions.

The FBED and mmpc variable selection algorithms have been implemented using the distance correlation. The references include: Tsamardinos I., Aliferis C. F. and Statnikov A. (2003). "Time and sample efficient discovery of Markovblankets and direct causal relations". In Proceedings of the ninth ACM SIGKDD international Conference. <doi:10.1145/956750.956838>. Borboudakis G. and Tsamardinos I. (2019). "Forward-backward selection with early dropping". Journal of Machine Learning Research, 20(8): 1--39. <doi:10.48550/arXiv.1705.10770>. Huo X. and Szekely G.J. (2016). "Fast computing for distance covariance". Technometrics, 58(4): 435--447. <doi:10.1080/00401706.2015.1054435>.

This package provides external JAR dependencies for the DatabaseConnector package.

Distributed estimation method is based on a Laplace factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.

Easily create descriptive and comparative tables. It makes use and integrates directly with the tidyverse family of packages, and pipes. Tables are produced as (nested) dataframes for easy manipulation.

The DImodels package is suitable for analysing data from biodiversity and ecosystem function studies using the Diversity-Interactions (DI) modelling approach introduced by Kirwan et al. (2009) <doi:10.1890/08-1684.1>. Suitable data will contain proportions for each species and a community-level response variable, and may also include additional factors, such as blocks or treatments. The package can perform data manipulation tasks, such as computing pairwise interactions (the DI_data() function), can perform an automated model selection process (the autoDI() function) and has the flexibility to fit a wide range of user-defined DI models (the DI() function).

Access diverse ggplot2'-compatible color palettes for simplified data visualization.

This package provides a collection of functions that perform jump regression and image analysis such as denoising, deblurring and jump detection. The implemented methods are based on the following research: Qiu, P. (1998) <doi:10.1214/aos/1024691468>, Qiu, P. and Yandell, B. (1997) <doi: 10.1080/10618600.1997.10474746>, Qiu, P. (2009) <doi: 10.1007/s10463-007-0166-9>, Kang, Y. and Qiu, P. (2014) <doi: 10.1080/00401706.2013.844732>, Qiu, P. and Kang, Y. (2015) <doi: 10.5705/ss.2014.054>, Kang, Y., Mukherjee, P.S. and Qiu, P. (2018) <doi: 10.1080/00401706.2017.1415975>, Kang, Y. (2020) <doi: 10.1080/10618600.2019.1665536>.

This package provides an easy to use implementation of life expectancy decomposition formulas for age bands, derived from Ponnapalli, K. (2005). A comparison of different methods for decomposition of changes in expectation of life at birth and differentials in life expectancy at birth. Demographic Research, 12, pp.141â 172. <doi:10.4054/demres.2005.12.7> In addition, there is a decomposition function for disease cause breakdown and a couple helpful plot functions.

The framework provides functions to generate ODEs of reaction networks, parameter transformations, observation functions, residual functions, etc. The framework follows the paradigm that derivative information should be used for optimization whenever possible. Therefore, all major functions produce and can handle expressions for symbolic derivatives. The methods used in dMod were published in Kaschek et al, 2019, <doi:10.18637/jss.v088.i10>.

This package contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis.

Identifies code blocks that have a high level of similarity within a set of R files.

Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).

Base DataSHIELD functions for the server side. DataSHIELD is a software package which allows you to do non-disclosive federated analysis on sensitive data. DataSHIELD analytic functions have been designed to only share non disclosive summary statistics, with built in automated output checking based on statistical disclosure control. With data sites setting the threshold values for the automated output checks. For more details, see citation("dsBase")'.

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.

This package provides functions to facilitate access to the DKAN API (<https://dkan.readthedocs.io/en/latest/apis/index.html>), including the DKAN REST API (metadata), and the DKAN datastore API (data). Includes functions to list, create, retrieve, update, and delete datasets and resources nodes. It also includes functions to search and retrieve data from the DKAN datastore.

Designed for network analysis, leveraging the personalized PageRank algorithm to calculate node scores in a given graph. This innovative approach allows users to uncover the importance of nodes based on a customized perspective, making it particularly useful in fields like bioinformatics, social network analysis, and more.

This package provides an operator for assigning nested components of a list to names via a concise pattern matching syntax. This is especially convenient for assigning individual names to the multiple values that a function may return in the form of a list, and for extracting deeply nested list components.

Data quality assessments guided by a data quality framework introduced by Schmidt and colleagues, 2021 <doi:10.1186/s12874-021-01252-7> target the data quality dimensions integrity, completeness, consistency, and accuracy. The scope of applicable functions rests on the availability of extensive metadata which can be provided in spreadsheet tables. Either standardized (e.g. as html5 reports) or individually tailored reports can be generated. For an introduction into the specification of corresponding metadata, please refer to the package website <https://dataquality.qihs.uni-greifswald.de/VIN_Annotation_of_Metadata.html>.

The gap statistic approach is extended to estimate the number of clusters for categorical response format data. This approach and accompanying software is designed to be used with the output of any clustering algorithm and with distances specifically designed for categorical (i.e. multiple choice) or ordinal survey response data.

To calculate the sensitivity and specificity in the absence of gold standard using the Bayesian method. The Bayesian method can be referenced at Haiyan Gu and Qiguang Chen (1999) <doi:10.3969/j.issn.1002-3674.1999.04.004>.

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>.