The DYMO package provides tools for multi-feature time-series forecasting using a Dynamic Mode Decomposition (DMD) model combined with conformal predictive sampling for uncertainty quantification.

This package provides a grammar of data manipulation with data.table', providing a consistent a series of utility functions that help you solve the most common data manipulation challenges.

Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.

The Discrete Transmuted Generalized Inverse Weibull (DTGIW) distribution is a new distribution for count data analysis. The DTGIW is discrete distribution based on Atchanut and Sirinapa (2021). <DOI: 10.14456/sjst-psu.2021.149>.

Visualize one-factor data frame. Beads plot consists of diamonds of each factor of each data series. A diamond indicates average and range. Look over a data frame with many numeric columns and a factor column.

Tools, methods and processes for the management of analysis workflows. These lightweight solutions facilitate structuring R&D activities. These solutions were developed to comply with Good Documentation Practice (GDP), with ALCOA+ principles as proposed by the U.S. FDA, and with FAIR principles as discussed by Jacobsen et al. (2017) <doi:10.1162/dint_r_00024>.

This package provides R bindings to the dockview JavaScript library <https://dockview.dev/>. Create fully customizable grid layouts (docks) in seconds to include in interactive R reports with R Markdown or Quarto or in shiny apps <https://shiny.posit.co/>. In shiny mode, modify docks by dynamically adding, removing or moving panels or groups of panels from the server function. Choose among 8 stunning themes (dark and light), serialise the state of a dock to restore it later.

Function to test spatial segregation and association based in contingency table analysis of nearest neighbour counts following Dixon (2002) <doi:10.1080/11956860.2002.11682700>. Some Fortran code has been included to the original dixon2002() function of the ecespa package to improve speed.

Query for metrics from Datadog (<https://www.datadoghq.com/>) via its API.

Fast fitting of generalised linear models on moderately large datasets, by taking an initial sample, fitting in memory, then evaluating the score function for the full data in the database. Thomas Lumley <doi:10.1080/10618600.2019.1610312>.

This package provides methods for evaluating the probability mass function, cumulative distribution function, and generating random samples from discrete tempered stable distributions. For more details see Grabchak (2021) <doi:10.1007/s11009-021-09904-3>.

This package contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1.

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 provides a collection of asymmetrical kernels belong to lifetime distributions for kernel density estimation is presented. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000a,b), Jin and Kawczak (2003) and Salha et al. (2014) <doi:10.12988/pms.2014.4616>.

Calculates expected values, variance, different moments (kth moment, truncated mean), stop-loss, mean excess loss, Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) as well as some density and cumulative (survival) functions of continuous, discrete and compound distributions. This package also includes a visual Shiny component to enable students to visualize distributions and understand the impact of their parameters. This package is intended to expand the stats package so as to enable students to develop an intuition for probability.

This package provides a deep neural network model with a monotonic increasing single index function tailored for periodontal disease studies. The residuals are assumed to follow a skewed T distribution, a skewed normal distribution, or a normal distribution. More details can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.

This package implements survival proximity score matching in multi-state survival models. Includes tools for simulating survival data and estimating transition-specific coxph models with frailty terms. The primary methodological work on multistate censored data modeling using propensity score matching has been published by Bhattacharjee et al.(2024) <doi:10.1038/s41598-024-54149-y>.

This package provides functions for direct surrogate variable analysis, which can identify hidden factors in high-dimensional biomedical data.

This package provides an interactive viewer for data.frame and tibble objects using shiny <https://shiny.posit.co/> and DT <https://rstudio.github.io/DT/>. It supports complex filtering, column selection, and automatic generation of reproducible dplyr <https://dplyr.tidyverse.org/> code for data manipulation. The package is designed for ease of use in data exploration and reporting workflows.

Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

This package provides functions to run the CRM and TITE-CRM in phase I trials and calibration tools for trial planning purposes.

Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic dynamic systems which can behave like an erratic and apparently random motion. A relevant field inside chaos theory and nonlinear time series analysis is the detection of a chaotic behaviour from empirical time series data. One of the main features of chaos is the well known initial value sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify the initial value sensitive property estimating the Lyapunov exponents. The DChaos package provides different useful tools and efficient algorithms which test robustly the hypothesis of chaos based on the Lyapunov exponent in order to know if the data generating process behind time series behave chaotically or not.

This package provides vectorised functions for computing p-values of various common discrete statistical tests, as described e.g. in Agresti (2002) <doi:10.1002/0471249688>, including their distributions. Exact and approximate computation methods are provided. For exact ones, several procedures of determining two-sided p-values are included, which are outlined in more detail in Hirji (2006) <doi:10.1201/9781420036190>.

Supports import/export for a number of datetime string standards and R datetime classes often including lossless re-export of any original reduced precision including ISO 8601 <https://en.wikipedia.org/wiki/ISO_8601> and pdfmark <https://opensource.adobe.com/dc-acrobat-sdk-docs/library/pdfmark/> datetime strings. Supports local/global datetimes with optional UTC offsets and/or (possibly heterogeneous) time zones with up to nanosecond precision.