Modeling the zero coupon yield curve using the dynamic De Rezende and Ferreira (2011) <doi:10.1002/for.1256> five factor model with variable or fixed decaying parameters. For explanatory purposes, the package also includes various short datasets of interest rates for the BRICS countries.

Constructs dynamic optimal shrinkage estimators for the weights of the global minimum variance portfolio which are reconstructed at given reallocation points as derived in Bodnar, Parolya, and Thorsén (2021) (<arXiv:2106.02131>). Two dynamic shrinkage estimators are available in this package. One using overlapping samples while the other use nonoverlapping samples.

Allows to perform the dynamic mixture estimation with state-space components and normal regression components, and clustering with normal mixture. Quasi-Bayesian estimation, as well as, that based on the Kerridge inaccuracy approximation are implemented. Main references: Nagy and Suzdaleva (2013) <doi:10.1016/j.apm.2013.05.038>; Nagy et al. (2011) <doi:10.1002/acs.1239>.

Simultaneously detect the number and locations of change points in piecewise linear models under stationary Gaussian noise allowing autocorrelated random noise. The core idea is to transform the problem of detecting change points into the detection of local extrema (local maxima and local minima)through kernel smoothing and differentiation of the data sequence, see Cheng et al. (2020) <doi:10.1214/20-EJS1751>. A low-computational and fast algorithm call dSTEM is introduced to detect change points based on the STEM algorithm in D. Cheng and A. Schwartzman (2017) <doi:10.1214/16-AOS1458>.

Statistical methods for DNA mixture analysis. This package is a lite-version of the DNAmixtures package to allow users without a HUGIN software license to experiment with the statistical methodology. While the lite-version aims to provide the full functionality it is noticeably less efficient than the original DNAmixtures package. For details on implementation and methodology see <https://dnamixtures.r-forge.r-project.org/>.

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly,our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Dampened weighted least squares ('DWLS') is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred. This method corrects common biases towards cell types that are characterized by highly expressed genes and/or are highly prevalent, to provide accurate detection across diverse cell types. See: <https://www.nature.com/articles/s41467-019-10802-z.pdf> for more information about the development of DWLS and the methods behind our functions.

Analysis of historical non-decimal currencies and value systems that use tripartite or tetrapartite systems such as pounds, shillings, and pence. It introduces new vector classes to represent non-decimal currencies, making them compatible with numeric classes, and provides functions to work with these classes in data frames in the context of double-entry bookkeeping.

Fits dose-response models utilizing a Bayesian model averaging approach as outlined in Gould (2019) <doi:10.1002/bimj.201700211> for both continuous and binary responses. Longitudinal dose-response modeling is also supported in a Bayesian model averaging framework as outlined in Payne, Ray, and Thomann (2024) <doi:10.1080/10543406.2023.2292214>. Functions for plotting and calculating various posterior quantities (e.g. posterior mean, quantiles, probability of minimum efficacious dose, etc.) are also implemented. Copyright Eli Lilly and Company (2019).

This package provides extra functions to manipulate dendrograms that build on the base functions provided by the stats package. The main functionality it is designed to add is the ability to colour all the edges in an object of class dendrogram according to cluster membership i.e. each subtree is coloured, not just the terminal leaves. In addition it provides some utility functions to cut dendrogram and hclust objects and to set/get labels.

Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event).

It is sometimes necessary to create documentation for all files in a directory. Doing so by hand can be very tedious. This task is made fast and reproducible using the functionality of documenter'. It aggregates all text files in a directory and its subdirectories into a single word document in a semi-automated fashion.

This package provides methods for analyzing the dispersion of tabular datasets with batched and ordered samples. Based on convex hull or integrated covariance Mahalanobis, several indicators are implemented for inter and intra batch dispersion analysis. It is designed to facilitate robust statistical assessment of data variability, supporting applications in exploratory data analysis and quality control, for such datasets as the one found in metabololomics studies. For more details see Salanon (2024) <doi:10.1016/j.chemolab.2024.105148> and Salanon (2025) <doi:10.1101/2025.08.01.668073>.

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.

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

Evaluation (S4-)classes based on package distr for evaluating procedures (estimators/tests) at data/simulation in a unified way.

Easy access to species distribution data for 6 regions in the world, for a total of 226 anonymised species. These data are described and made available by Elith et al (2020) <doi:10.17161/bi.v15i2.13384> to compare species distribution modelling methods.

Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-Seq expression profiles. Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.

This package provides an interface to D4Science StorageHub API (<https://dev.d4science.org/>). Allows to get user profile, and perform actions over the StorageHub (workspace) including creation of folders, files management (upload/update/deletion/sharing), and listing of stored resources.

This package implements a flexible, versatile, and computationally tractable model for density regression based on a single-weights dependent Dirichlet process mixture of normal distributions model for univariate continuous responses. The model assumes an additive structure for the mean of each mixture component and the effects of continuous covariates are captured through smooth nonlinear functions. The key components of our modelling approach are penalised B-splines and their bivariate tensor product extension. The proposed method can also easily deal with parametric effects of categorical covariates, linear effects of continuous covariates, interactions between categorical and/or continuous covariates, varying coefficient terms, and random effects. Please see Rodriguez-Alvarez, Inacio et al. (2025) for more details.

This package provides a distance density clustering (DDC) algorithm in R. DDC uses dynamic time warping (DTW) to compute a similarity matrix, based on which cluster centers and cluster assignments are found. DDC inherits dynamic time warping (DTW) arguments and constraints. The cluster centers are centroid points that are calculated using the DTW Barycenter Averaging (DBA) algorithm. The clustering process is divisive. At each iteration, cluster centers are updated and data is reassigned to cluster centers. Early stopping is possible. The output includes cluster centers and clustering assignment, as described in the paper (Ma et al (2017) <doi:10.1109/ICDMW.2017.11>).

Load configuration from a .env file, that is in the current working directory, into environment variables.

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 a system for extracting news from Chilean media, specifically through Web Scapping from Chilean media. The package allows for news searches using search phrases and date filters, and returns the results in a structured format, ready for analysis. Additionally, it includes functions to clean the extracted data, visualize it, and store it in databases. All of this can be done automatically, facilitating the collection and analysis of relevant information from Chilean media.

This package provides functions for interacting with all sections of the official Danish Address Web API (also known as DAWA') <https://api.dataforsyningen.dk>. The development of this package is completely independent from the government agency, Klimadatastyrelsen, who maintains the API.