Phone numbers are often represented as strings because there is no obvious and suitable native representation for them. This leads to high memory use and a lack of standard representation. The package provides integer representation of Australian phone numbers with optional raw vector calling code. The package name is an extension of au and ph'.

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.

This package provides functions designed to connect disease-related differential proteins and co-expression network. It provides the basic statics analysis included t test, ANOVA analysis. The network construction is not offered by the package, you can used WGCNA package which you can learn in Peter et al. (2008) <doi:10.1186/1471-2105-9-559>. It also provides module analysis included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and hub analysis.

Implementations of the multiple testing procedures for discrete tests described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" <doi:10.1214/18-EJS1441>. The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input the results of a test procedure from package DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with a wrapper allowing to apply discrete procedures directly to data.

The implemented methods are: Standard Bass model, Generalized Bass model (with rectangular shock, exponential shock, and mixed shock. You can choose to add from 1 to 3 shocks), Guseo-Guidolin model and Variable Potential Market model, and UCRCD model. The Bass model consists of a simple differential equation that describes the process of how new products get adopted in a population, the Generalized Bass model is a generalization of the Bass model in which there is a "carrier" function x(t) that allows to change the speed of time sliding. In some real processes the reachable potential of the resource available in a temporal instant may appear to be not constant over time, because of this we use Variable Potential Market model, in which the Guseo-Guidolin has a particular specification for the market function. The UCRCD model (Unbalanced Competition and Regime Change Diachronic) is a diffusion model used to capture the dynamics of the competitive or collaborative transition.

Data science methods used in wind energy applications. Current functionalities include creating a multi-dimensional power curve model, performing power curve function comparison, covariate matching, and energy decomposition. Relevant works for the developed functions are: funGP() - Prakash et al. (2022) <doi:10.1080/00401706.2021.1905073>, AMK() - Lee et al. (2015) <doi:10.1080/01621459.2014.977385>, tempGP() - Prakash et al. (2022) <doi:10.1080/00401706.2022.2069158>, ComparePCurve() - Ding et al. (2021) <doi:10.1016/j.renene.2021.02.136>, deltaEnergy() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, syncSize() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, imptPower() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, All other functions - Ding (2019, ISBN:9780429956508).

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>.

Distributional instrumental variable (DIV) model for estimation of the interventional distribution of the outcome Y under a do intervention on the treatment X. Instruments, predictors and targets can be univariate or multivariate. Functionality includes estimation of the (conditional) interventional mean and quantiles, as well as sampling from the fitted (conditional) interventional distribution.

Easy visualization for datasets with more than two categorical variables and additional continuous variables. The package is particularly useful for exploring complex categorical data in the context of pathway analysis across multiple conditions. This package is now in maintenance-only mode and kept for legacy compatibility; for new projects and active development, please use the successor package ggdiceplot (see <https://github.com/maflot/ggdiceplot> and <https://dice-and-domino-plot.readthedocs.io/en/latest/>).

Compute estimates and confidence intervals of weighted averages quickly and easily. Weighted averages are computed using data.table for speed. Confidence intervals are approximated using the delta method with either using known formulae or via algorithmic or numerical integration.

Decorrelates a set of summary statistics (i.e., Z-scores or P-values per SNP) via Decorrelation by Orthogonal Transformation (DOT) approach and performs gene-set analyses by combining transformed statistic values; operations are performed with algorithms that rely only on the association summary results and the linkage disequilibrium (LD). For more details on DOT and its power, see Olga (2020) <doi:10.1371/journal.pcbi.1007819>.

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

This package provides a suite of functions for analyzing and visualizing the health economic outputs of mathematical models. This package was developed with funding from the National Institutes of Allergy and Infectious Diseases of the National Institutes of Health under award no. R01AI138783. The content of this package is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The theoretical underpinnings of dampack''s functionality are detailed in Hunink et al. (2014) <doi:10.1017/CBO9781139506779>.

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.

Computing and plotting the distance covariance and correlation function of a univariate or a multivariate time series. Both versions of biased and unbiased estimators of distance covariance and correlation are provided. Test statistics for testing pairwise independence are also implemented. Some data sets are also included. References include: a) Edelmann Dominic, Fokianos Konstantinos and Pitsillou Maria (2019). An Updated Literature Review of Distance Correlation and Its Applications to Time Series'. International Statistical Review, 87(2): 237--262. <doi:10.1111/insr.12294>. b) Fokianos Konstantinos and Pitsillou Maria (2018). Testing independence for multivariate time series via the auto-distance correlation matrix'. Biometrika, 105(2): 337--352. <doi:10.1093/biomet/asx082>. c) Fokianos Konstantinos and Pitsillou Maria (2017). Consistent testing for pairwise dependence in time series'. Technometrics, 59(2): 262--270. <doi:10.1080/00401706.2016.1156024>. d) Pitsillou Maria and Fokianos Konstantinos (2016). dCovTS: Distance Covariance/Correlation for Time Series'. R Journal, 8(2):324-340. <doi:10.32614/RJ-2016-049>.

S4-distribution classes based on package distr for distributions from packages fBasics and fGarch'.

Gives you the ability to use arbitrary Docker images (including custom ones) to process Rmarkdown code chunks.

Apply the Deductive Rational Method to a monthly series of flow or precipitation data to fill in missing data. The method is as described in: Campos, D.F., (1984, ISBN:9686194444).

An open, multi-algorithmic pipeline for easy, fast and efficient analysis of cellular sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: data pre-processing, cellular clustering with pseudo-temporal ordering, defining differential expressed genes and biomarker identification. More details on Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements extensions of the work published by Ghannoum et. al. (2019) <doi:10.1101/700989>.

This package provides a wrapper for the DeepL API <https://developers.deepl.com/docs>, a web service for translating texts between different languages. A DeepL API developer account is required to use the service (see <https://www.deepl.com/pro#developer>).

Data cloud geometry (DCG) applies random walks in finding community structures for social networks. Fushing, VanderWaal, McCowan, & Koehl (2013) (<doi:10.1371/journal.pone.0056259>).

You can load a schema from a DTR (data type registry) as an R object. Use this schema to write your data in JSON-LD (JavaScript Object Notation for Linked Data) format to make it machine readable.

An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.

Dynamic CUR (dCUR) boosts the CUR decomposition (Mahoney MW., Drineas P. (2009) <doi:10.1073/pnas.0803205106>) varying the k, the number of columns and rows used, and its final purposes to help find the stage, which minimizes the relative error to reduce matrix dimension. The goal of CUR Decomposition is to give a better interpretation of the matrix decomposition employing proper variable selection in the data matrix, in a way that yields a simplified structure. Its origins come from analysis in genetics. The goal of this package is to show an alternative to variable selection (columns) or individuals (rows). The idea proposed consists of adjusting the probability distributions to the leverage scores and selecting the best columns and rows that minimize the reconstruction error of the matrix approximation ||A-CUR||. It also includes a method that recalibrates the relative importance of the leverage scores according to an external variable of the user's interest.