Calculates the desparsified lasso as originally introduced in van de Geer et al. (2014) <doi:10.1214/14-AOS1221>, and provides inference suitable for high-dimensional time series, based on the long run covariance estimator in Adamek et al. (2020) <doi:10.48550/arXiv.2007.10952>. Also estimates high-dimensional local projections by the desparsified lasso, as described in Adamek et al. (2022) <doi:10.48550/arXiv.2209.03218>.

This package provides a set of algorithms based on Quinn et al. (1991) <doi:10.1002/hyp.3360050106> for processing river network and digital elevation data to build implementations of Dynamic TOPMODEL, a semi-distributed hydrological model proposed in Beven and Freer (2001) <doi:10.1002/hyp.252>. The dynatop package implements simulation code for Dynamic TOPMODEL based on the output of dynatopGIS'.

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).

Bindings for additional classification models for use with the parsnip package. Models include flavors of discriminant analysis, such as linear (Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>), regularized (Friedman (1989) <doi:10.1080/01621459.1989.10478752>), and flexible (Hastie, Tibshirani, and Buja (1994) <doi:10.1080/01621459.1994.10476866>), as well as naive Bayes classifiers (Hand and Yu (2007) <doi:10.1111/j.1751-5823.2001.tb00465.x>).

Various utilities for the Davies distribution.

This package provides a flexible container to manage and annotate Differential Gene Expression (DGE) analysis results (Smythe et. al (2015) <doi:10.1093/nar/gkv007>). The DGEobj has data slots for row (gene), col (samples), assays (matrix n-rows by m-samples dimensions) and metadata (not keyed to row, col, or assays). A set of accessory functions to deposit, query and retrieve subsets of a data workflow has been provided. Attributes are used to capture metadata such as species and gene model, including reproducibility information such that a 3rd party can access a DGEobj history to see how each data object was created or modified. Since the DGEobj is customizable and extensible it is not limited to RNA-seq analysis types of workflows -- it can accommodate nearly any data analysis workflow that starts from a matrix of assays (rows) by samples (columns).

The Demographic Table in R combines contingency table for categorical variables, mean and standard deviation for continuous variables. t-test, chi-square test and Fisher's exact test calculated the p-value of two groups. The standardized mean difference were performed with 95 % confident interval, and writing table into document file.

Offers statistical methods to compare diagnostic performance between two binary diagnostic tests on the same subject in clinical studies. Includes functions for generating formatted tables to display diagnostic outcomes, facilitating a clear and comprehensive comparison directly through the R console. Inspired by and extending the functionalities of the DTComPair', tableone', and gtsummary packages.

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.

It provides the subset operator for dist objects and a function to compute medoid(s) that are fully parallelized leveraging the RcppParallel package. It also provides functions for package developers to easily implement their own parallelized dist() function using a custom C++'-based distance function.

Differential partial correlation identification with the ridge and the fusion penalties.

This package provides a simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) <doi:10.18637/jss.v089.i01> for more information on methods and <https://distancesampling.org/resources/vignettes.html> for example analyses.

This package implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.

Decomposing value added growth into explanatory factors. A cost constrained value added function is defined to specify the production frontier. Industry estimates can also be aggregated using a weighted average approach. Details about the methodology and data can be found in Diewert and Fox (2018) <doi:10.1093/oxfordhb/9780190226718.013.19> and Zeng, Parsons, Diewert and Fox (2018) <https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/Documents/emg2018-6_SZeng_EMG-Slides.pdf>.

Access diverse ggplot2'-compatible color palettes for simplified data visualization.

This package contains one main function deduped() which speeds up slow, vectorized functions by only performing computations on the unique values of the input and expanding the results at the end.

Deconvolving cell types from high-throughput gene profiling data. For more information on dtangle see Hunt et al. (2019) <doi:10.1093/bioinformatics/bty926>.

Ingredient specific diagnostics for drug exposure records in the Observational Medical Outcomes Partnership (OMOP) common data model.

An implementation by Chen, Li, and Zhang (2022) <doi: 10.1093/bioadv/vbac041> of the Depth Importance in Precision Medicine (DIPM) method in Chen and Zhang (2022) <doi:10.1093/biostatistics/kxaa021> and Chen and Zhang (2020) <doi:10.1007/978-3-030-46161-4_16>. The DIPM method is a classification tree that searches for subgroups with especially poor or strong performance in a given treatment group.

This package provides a big-data-friendly and memory-efficient difference-in-differences estimator for staggered (and non-staggered) treatment contexts.

This package contains functions for the DivE estimator <doi:10.1371/journal.pcbi.1003646>. The DivE estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.

This package provides a comprehensive approach for identifying and estimating change points in multivariate time series through various statistical methods. Implements the multiple change point detection methodology from Ryan & Killick (2023) <doi:10.1080/00401706.2023.2183261> and a novel estimation methodology from Fotopoulos et al. (2023) <doi:10.1007/s00362-023-01495-0> generalized to fit the detection methodologies. Performs both detection and estimation of change points, providing visualization and summary information of the estimation process for each detected change point.

Formatting of population and case data, calculation of Standardized Incidence Ratios, and fitting the BYM model using INLA'. For details see Brown (2015) <doi:10.18637/jss.v063.i12>.

This package provides a function for plotting maps of agricultural field experiments that are laid out in grids. See Ryder (1981) <doi:10.1017/S0014479700011601>.