This package provides functions that offer seamless D3Plus integration. The examples provided here are taken from the official D3Plus website <http://d3plus.org>.

An implementation of the decimated two-dimensional complex dual-tree wavelet transform as described in Kingsbury (1999) <doi:10.1098/rsta.1999.0447> and Selesnick et al. (2005) <doi:10.1109/MSP.2005.1550194>. Also includes the undecimated version and spectral bias correction described in Nelson et al. (2018) <doi:10.1007/s11222-017-9784-0>. The code is partly based on the dtcwt Python library.

Get Drug information from given differential expression profile. The package search for the bioactive compounds from reference databases such as LINCS containing the genome-wide gene expression signature (GES) from tens of thousands of drug and genetic perturbations (Subramanian et al. (2017) <DOI:10.1016/j.cell.2017.10.049>).

Fast & memory-efficient functions to analyze and manipulate large data sets. It leverages the fast analytical capabilities of DuckDB and its spatial extension (see <https://duckdb.org/community_extensions/extensions/h3>) while maintaining compatibility with Râ s spatial data ecosystem to work with spatial vector data.

Distributed Online Mean Tests is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform mean tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, Domean ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for mean testing. The philosophy of Domean is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.

Implementation of the Dual Feature Reduction (DFR) approach for the Sparse Group Lasso (SGL) and the Adaptive Sparse Group Lasso (aSGL) (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.17094>). The DFR approach is a feature reduction approach that applies strong screening to reduce the feature space before optimisation, leading to speed-up improvements for fitting SGL (Simon et al. (2013) <doi:10.1080/10618600.2012.681250>) and aSGL (Mendez-Civieta et al. (2020) <doi:10.1007/s11634-020-00413-8> and Poignard (2020) <doi:10.1007/s10463-018-0692-7>) models. DFR is implemented using the Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) algorithm, with linear and logistic SGL models supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported.

Calculate multiple or pairwise dissimilarity for orders q = 0-N (CqN; Chao et al. 2008 <doi:10/fcvn63>) for a set of species assemblages or interaction networks.

Implementation of Das Gupta's standardisation and decomposition of population rates, as set out "Standardization and decomposition of rates: A userâ s manual", Das Gupta (1993) <https://www2.census.gov/library/publications/1993/demographics/p23-186.pdf>. The goal of these methods is to calculate adjusted rates based on compositional factors and quantify the contribution of each factor to the difference in crude rates between populations. The package offers functionality to handle various scenarios for any number of factors and populations, where said factors can be comprised of vectors across sub-populations (including cross-classified population breakdowns), and with the option to specify user-defined rate functions.

Geologic pattern data from <https://ngmdb.usgs.gov/fgdc_gds/geolsymstd.php>. Access functions are provided in the accompanying package deeptime'.

We have the code for disaggregation as found in Wei and Stram (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series" in Statistical Science I, Nova Publishing). The disaggregation models have different orders of the moving average component. These are based on ARIMA models rather than differencing or using similar time series.

This package provides a parallel backend for the %dopar% function using the Rmpi package.

Identification of hub genes in a gene co-expression network from gene expression data. The differential network analysis for two contrasting conditions leads to the identification of various types of hubs like Housekeeping, Unique to stress (Disease) and Unique to control (Normal) hub genes.

This package provides statistical tools for analyzing net and relative survival, with a key feature of relaxing the assumption of independent censoring and incorporating the effect of dependent competing risks. It employs a copula-based methodology, specifically the Archimedean copula, to simulate data, conduct survival analysis, and offer comparisons with other methods. This approach is detailed in the work of Adatorwovor et al. (2022) <doi:10.1515/ijb-2021-0016>.

Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios. The purpose of the package is to help individuals learn about infectious disease epidemiology (ecology/evolution) from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models.

Given the non-negative data and its distribution, the package estimates the rank parameter for Non-negative Matrix Factorization. The method is based on hypothesis testing, using a deconvolved bootstrap distribution to assess the significance level accurately despite the large amount of optimization error. The distribution of the non-negative data can be either Normal distributed or Poisson distributed.

Data whitening is a widely used preprocessing step to remove correlation structure since statistical models often assume independence. Here we use a probabilistic model of the observed data to apply a whitening transformation. This Gaussian Inverse Wishart Empirical Bayes model substantially reduces computational complexity, and regularizes the eigen-values of the sample covariance matrix to improve out-of-sample performance.

An implementation of common higher order functions with syntactic sugar for anonymous function. Provides also a link to dplyr and data.table for common transformations on data frames to work around non standard evaluation by default.

Estimates probabilistic phylogenetic Principal Component Analysis (PCA) and non-phylogenetic probabilistic PCA. Provides methods to implement alternative models of trait evolution including Brownian motion (BM), Ornstein-Uhlenbeck (OU), Early Burst (EB), and Pagel's lambda. Also provides flexible biplot functions.

Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on.

Uses species occupancy at coarse grain sizes to predict species occupancy at fine grain sizes. Ten models are provided to fit and extrapolate the occupancy-area relationship, as well as methods for preparing atlas data for modelling. See Marsh et. al. (2018) <doi:10.18637/jss.v086.c03>.

This package provides a general-purpose computational engine for data analysis, drake rebuilds intermediate data objects when their dependencies change, and it skips work when the results are already up to date. Not every execution starts from scratch, there is native support for parallel and distributed computing, and completed projects have tangible evidence that they are reproducible. Extensive documentation, from beginner-friendly tutorials to practical examples and more, is available at the reference website <https://docs.ropensci.org/drake/> and the online manual <https://books.ropensci.org/drake/>.

Developed to Solve the Multi-Criteria Decision Making Problems with Decision Making Trial and Evaluation Laboratory Technique in R.

This package provides functions for analyzing dichotomous choice contingent valuation (CV) data. It provides functions for estimating parametric and nonparametric models for single-, one-and-one-half-, and double-bounded CV data. For details, see Aizaki et al. (2022) <doi:10.1007/s42081-022-00171-1>.

This package provides functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well as other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) <doi:10.1101/423780>.