Differential exon usage test for RNA-Seq data via an empirical Bayes shrinkage method for the dispersion parameter the utilizes inclusion-exclusion data to analyze the propensity to skip an exon across groups. The input data consists of two matrices where each row represents an exon and the columns represent the biological samples. The first matrix is the count of the number of reads expressing the exon for each sample. The second matrix is the count of the number of reads that either express the exon or explicitly skip the exon across the samples, a.k.a. the total count matrix. Dividing the two matrices yields proportions representing the propensity to express the exon versus skipping the exon for each sample.

This package provides a wrapper on top of the Domino Data Python SDK library. It lets you query and access Domino Data Sources directly from your R environment. Under the hood, Domino Data R SDK leverages the API provided by the Domino Data Python SDK', which must be installed as a prerequisite. Domino is a platform that makes it easy to run your code on scalable hardware, with integrated version control and collaboration features designed for analytical workflows. See <https://docs.dominodatalab.com/en/latest/api_guide/140b48/domino-data-api> for more information.

Fits, bootstraps, and evaluates two-component normal and lognormal mixture models. Includes diagnostic plots and statistical evaluation of mixture model fits using differential evolution optimization.

Utility functions used for the R package development infrastructure inside the data integration centers ('DIZ') to standardize and facilitate repetitive tasks such as setting up a database connection or issuing notification messages and to avoid redundancy.

Allows the computation of clustering coefficients for directed and weighted networks by using different approaches. It allows to compute clustering coefficients that are not present in igraph package. A description of clustering coefficients can be found in "Directed clustering in weighted networks: a new perspective", Clemente, G.P., Grassi, R. (2017), <doi:10.1016/j.chaos.2017.12.007>.

An integrated toolset for the analysis of de novo (sporadic) genetic sequence variants. denovolyzeR implements a mutational model that estimates the probability of a de novo genetic variant arising in each human gene, from which one can infer the expected number of de novo variants in a given population size. Observed variant frequencies can then be compared against expectation in a Poisson framework. denovolyzeR provides a suite of functions to implement these analyses for the interpretation of de novo variation in human disease.

This package provides tools to help the design and analysis of resilient non-inferiority trials. These include functions for sample size calculations and analyses of trials, with either a risk difference, risk ratio or arc-sine difference margin, and a function to run simulations to design a trial with the methods described in Quartagno et al. (2019) <arXiv:1905.00241>.

This package provides functions for deep learning estimation of Conditional Average Treatment Effects (CATEs) from meta-learner models and Population Average Treatment Effects on the Treated (PATT) in settings with treatment noncompliance using reticulate, TensorFlow and Keras3. Functions in the package also implements the conformal prediction framework that enables computation and illustration of conformal prediction (CP) intervals for estimated individual treatment effects (ITEs) from meta-learner models. Additional functions in the package permit users to estimate the meta-learner CATEs and the PATT in settings with treatment noncompliance using weighted ensemble learning via the super learner approach and R neural networks.

This hosts the findRFM function which generates RFM scores on a 1-5 point scale for customer transaction data. The function consumes a data frame with Transaction Number, Customer ID, Date of Purchase (in date format) and Amount of Purchase as the attributes. The function returns a data frame with RFM data for the sales information.

This package contains a robust set of tools designed for constructing deep neural networks, which are highly adaptable with user-defined loss function and probability models. It includes several practical applications, such as the (deepAFT) model, which utilizes a deep neural network approach to enhance the accelerated failure time (AFT) model for survival data. Another example is the (deepGLM) model that applies deep neural network to the generalized linear model (glm), accommodating data types with continuous, categorical and Poisson distributions.

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

This package provides a datetime range picker widget for usage in Shiny'. It creates a calendar allowing to select a start date and an end date as well as two fields allowing to select a start time and an end time.

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>).

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

This package provides statistical tests and support functions for detecting irregular digit patterns in numerical data. The package includes tools for extracting digits at various locations in a number, tests for repeated values, and (Bayesian) tests of digit distributions.

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>.

Piecewise linear segmentation of ordered data by a dynamic programming algorithm. The algorithm was developed for time series data, e.g. growth curves, and for genome-wide read-count data from next generation sequencing, but is broadly applicable. Generic implementations of dynamic programming routines allow to scan for optimal segmentation parameters and test custom segmentation criteria ("scoring functions").

Finds regular and chaotic intervals in the data using the 0-1 test for chaos proposed by Gottwald and Melbourne (2004) <DOI:10.1137/080718851>.

Facilitates the import and analysis of SNP (single nucleotide polymorphism') and silicodart (presence/absence) data. The main focus is on data generated by DarT (Diversity Arrays Technology), however, data from other sequencing platforms can be used once SNP or related fragment presence/absence data from any source is imported. Genetic datasets are stored in a derived genlight format (package adegenet'), that allows for a very compact storage of data and metadata. Functions are available for importing and exporting of SNP and silicodart data, for reporting on and filtering on various criteria (e.g. callrate', heterozygosity', reproducibility', maximum allele frequency). Additional functions are available for visualization (e.g. Principle Coordinate Analysis) and creating a spatial representation using maps. dartR.base is the base package of the dartRverse suits of packages. To install the other packages, we recommend to install the dartRverse package, that supports the installation of all packages in the dartRverse'. If you want to cite dartR', you find the information by typing citation('dartR.base') in the console.

In practice, we will encounter problems where the longitudinal performance of processes needs to be monitored over time. Dynamic screening systems (DySS) are methods that aim to identify and give signals to processes with poor performance as early as possible. This package is designed to implement dynamic screening systems and the related methods. References: Qiu, P. and Xiang, D. (2014) <doi:10.1080/00401706.2013.822423>; Qiu, P. and Xiang, D. (2015) <doi:10.1002/sim.6477>; Li, J. and Qiu, P. (2016) <doi:10.1080/0740817X.2016.1146423>; Li, J. and Qiu, P. (2017) <doi:10.1002/qre.2160>; You, L. and Qiu, P. (2019) <doi:10.1080/00949655.2018.1552273>; Qiu, P., Xia, Z., and You, L. (2020) <doi:10.1080/00401706.2019.1604434>; You, L., Qiu, A., Huang, B., and Qiu, P. (2020) <doi:10.1002/bimj.201900127>; You, L. and Qiu, P. (2021) <doi:10.1080/00224065.2020.1767006>.

The automated clustering and quantification of the digital PCR data is based on the combination of DBSCAN (Hahsler et al. (2019) <doi:10.18637/jss.v091.i01>) and c-means (Bezdek et al. (1981) <doi:10.1007/978-1-4757-0450-1>) algorithms. The analysis is independent of multiplexing geometry, dPCR system, and input amount. The details about input data and parameters are available in the vignette.

Efficient Global Optimization (EGO) algorithm as described in "Roustant et al. (2012)" <doi:10.18637/jss.v051.i01> and adaptations for problems with noise ("Picheny and Ginsbourger, 2012") <doi:10.1016/j.csda.2013.03.018>, parallel infill, and problems with constraints.

Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms.

Nonparametric kernel density estimation, bandwidth selection, and other utilities for analyzing directional data. Implements the estimator in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987) <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in Garcà a-Portugués (2013) <doi:10.1214/13-ejs821>.