This is a package for semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution.

This package provides an API for efficient .hic file data extraction with programmatic matrix access. It doesn't store the pointer data for all the matrices, only the one queried, and currently it only supports matrices.

Seamless extraction of river networks from digital elevation models data. The package allows analysis of digital elevation models that can be either externally provided or downloaded from open source repositories (thus interfacing with the elevatr package). Extraction is performed via the D8 flow direction algorithm of TauDEM (Terrain Analysis Using Digital Elevation Models), thus interfacing with the traudem package. Resulting river networks are compatible with functions from the OCNet package. See Carraro (2023) <doi:10.5194/hess-27-3733-2023> for a presentation of the package.

The open sourced data management software Integrated Rule-Oriented Data System ('iRODS') offers solutions for the whole data life cycle (<https://irods.org/>). The loosely constructed and highly configurable architecture of iRODS frees the user from strict formatting constraints and single-vendor solutions. This package provides an interface to the iRODS HTTP API, allowing you to manage your data and metadata in iRODS with R. Storage of annotated files and R objects in iRODS ensures findability, accessibility, interoperability, and reusability of data.

This package provides tools to read/write/publish metadata based on the Atom XML syndication format. This includes support of Dublin Core XML implementation, and a client to API(s) implementing the AtomPub - SWORD API specification.

Fit Bayesian Regression Additive Trees (BART) models to select true confounders from a large set of potential confounders and to estimate average treatment effect. For more information, see Kim et al. (2023) <doi:10.1111/biom.13833>.

This package provides a penalized/non-penalized implementation for dynamic regression in the presence of autocorrelated residuals (DREGAR) using iterative penalized/ordinary least squares. It applies Mallows CP, AIC, BIC and GCV to select the tuning parameters.

Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>. divent provides functions to estimate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.

Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>.

Leverages dplyr to process the calculations of a plot inside a database. This package provides helper functions that abstract the work at three levels: outputs a ggplot', outputs the calculations, outputs the formula needed to calculate bins.

This package provides a set of functions, which facilitates removing objects from an environment. It allows to delete objects specified with regular expression or with other conditions (e.g. if object is numeric), using one function call.

Detects sustained change in digital bio-marker data using simultaneous confidence bands. Accounts for noise using an auto-regressive model. Based on Buehlmann (1998) "Sieve bootstrap for smoothing in nonstationary time series" <doi:10.1214/aos/1030563978>.

An optimizer of Fused-Sparse Structural Equation Models, which is the state of the art jointly fused sparse maximum likelihood function for structural equation models proposed by Xin Zhou and Xiaodong Cai (2018 <doi:10.1101/466623>).

This package provides tools and features for "Exploratory Landscape Analysis (ELA)" of single-objective continuous optimization problems. Those features are able to quantify rather complex properties, such as the global structure, separability, etc., of the optimization problems.

New kernel-based test and fast tests for detecting change-points or changed-intervals where the distributions abruptly change. They work well particularly for high-dimensional data. Song, H. and Chen, H. (2022) <arXiv:2206.01853>.

Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). The book was published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler".

Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew power exponential and power exponential distributions, respectively. A generalized expectation-maximization framework is used for parameter estimation. See citation() for how to cite.

This package provides tools for handling NetCDF metadata in data frames. The metadata is provided as relations in tabular form, to avoid having to scan printed header output or to navigate nested lists of raw metadata.

Perform flexible simulation studies using one or multiple computer cores. The package is set up to be usable on high-performance clusters in addition to being run locally, see examples on <https://github.com/SachaEpskamp/parSim>.

Homogeneity tests of the coefficients in panel data. Currently, only the Hsiao test for determining coefficient homogeneity between the panel data individuals is implemented, as described in Hsiao (2022), "Analysis of Panel Data" (<doi:10.1017/9781009057745>).

This function aims to calculate risk of developing cardiovascular disease of individual patients in next 10 years. This unofficial package was based on published open-sourced free risk prediction algorithm QRISK3-2017 <https://qrisk.org/src.php>.

This package provides advanced functionality for performing configurational comparative research with Qualitative Comparative Analysis (QCA), including crisp-set, multi-value, and fuzzy-set QCA. It also offers advanced tools for sensitivity diagnostics and methodological evaluations of QCA.

This package implements the SVM-Maj algorithm to train data with support vector machine <doi:10.1007/s11634-008-0020-9>. This algorithm uses two efficient updates, one for linear kernel and one for the nonlinear kernel.

Sequential Kalman filter for scalable online changepoint detection by temporally correlated data. It enables fast single and multiple change points with missing values. See the reference: Hanmo Li, Yuedong Wang, Mengyang Gu (2023), <arXiv:2310.18611>.