This package provides methods for estimating multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring. Cho, H., Holloway, S. T., and Kosorok, M. R. (2022) <doi:10.1093/biomet/asac047>.

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

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.

This package implements the distribution-free goodness-of-fit regression test for the mean structure of parametric models introduced in Khmaladze (2021) <doi:10.1007/s10463-021-00786-3>. The test is implemented for general functions with minimal distributional assumptions as well as common models (e.g., lm, glm) with the usual assumptions.

Solving large scale distance weighted discrimination. The main algorithm is a symmetric Gauss-Seidel based alternating direction method of multipliers (ADMM) method. See Lam, X.Y., Marron, J.S., Sun, D.F., and Toh, K.C. (2018) <doi:10.48550/arXiv.1604.05473> for more details.

Dynamic stochastic block model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time, developed in Matias and Miele (2016) <doi:10.1111/rssb.12200>.

This package provides methods for reading, displaying, processing and writing files originally arranged for the DSSAT-CSM fixed width format. The DSSAT-CSM cropping system model is described at J.W. Jones, G. Hoogenboomb, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, J.T. Ritchie (2003) <doi:10.1016/S1161-0301(02)00107-7>.

Compares two dataframes with a common key and returns the delta records. The package will return three dataframes that contain the added, changed, and deleted records.

This package provides a general framework using mixture Weibull distributions to accurately predict biomarker-guided trial duration accounting for heterogeneous population. Extensive simulations are performed to evaluate the impact of heterogeneous population and the dynamics of biomarker characteristics and disease on the study duration. Several influential parameters including median survival time, enrollment rate, biomarker prevalence and effect size are identified. Efficiency gains of biomarker-guided trials can be quantitatively compared to the traditional all-comers design. For reference, see Zhang et al. (2024) <arXiv:2401.00540>.

Includes functions that researchers or practitioners may use to clean raw data, transferring html, xlsx, txt data file into other formats. And it also can be used to manipulate text variables, extract numeric variables from text variables and other variable cleaning processes. It is originated from a author's project which focuses on creative performance in online education environment. The resulting paper of that study will be published soon.

Compares the fit of alternative models of continuous trait differentiation between sister species and other paired lineages. Differences in trait means between two lineages arise as they diverge from a common ancestor, and alternative processes of evolutionary divergence are expected to leave unique signatures in the distribution of trait differentiation in datasets comprised of many lineage pairs. Models include approximations of divergent selection, drift, and stabilizing selection. A variety of model extensions facilitate the testing of process-to-pattern hypotheses. Users supply trait data and divergence times for each lineage pair. The fit of alternative models is compared in a likelihood framework.

Various methods for the identification of trend and seasonal components in time series (TS) are provided. Among them is a data-driven locally weighted regression approach with automatically selected bandwidth for equidistant short-memory time series. The approach is a combination / extension of the algorithms by Feng (2013) <doi:10.1080/02664763.2012.740626> and Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598> and a brief description of this new method is provided in the package documentation. Furthermore, the package allows its users to apply the base model of the Berlin procedure, version 4.1, as described in Speth (2004) <https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>. Permission to include this procedure was kindly provided by the Federal Statistical Office of Germany.

An easy-to-use yet powerful system for plotting grouped data effect sizes. Various types of effect size can be estimated, then plotted together with a representation of the original data. Select from many possible data representations (box plots, violin plots, raw data points etc.), and combine as desired. Durga plots are implemented in base R, so are compatible with base R methods for combining plots, such as layout()'. See Khan & McLean (2023) <doi:10.1101/2023.02.06.526960>.

The disparity filter algorithm is a network reduction technique to identify the backbone structure of a weighted network without destroying its multi-scale nature. The algorithm is documented in M. Angeles Serrano, Marian Boguna and Alessandro Vespignani in "Extracting the multiscale backbone of complex weighted networks", Proceedings of the National Academy of Sciences 106 (16), 2009. This implementation of the algorithm supports both directed and undirected networks.

Download and import time series from <http://www.dataseries.org>, a comprehensive and up-to-date collection of open data from Switzerland.

This package implements two out-of box classifiers presented in <doi:10.1002/env.2848> for distinguishing forest and non-forest terrain images. Under these algorithms, there are frequentist approaches: one parametric, using stable distributions, and another one- non-parametric, using the squared Mahalanobis distance. The package also contains functions for data handling and building of new classifiers as well as some test data set.

This package provides a set of user-friendly wrapper functions for creating consistent graphics and diagrams with lines, common shapes, text, and page settings. Compatible with and based on the R grid package.

Manage your source code dependencies by decorating your existing R code with special, roxygen'-style comments.

Offers robust tools to identify and manage incomplete responses in survey datasets, thereby enhancing the quality and reliability of research findings.

The function takes a DNA sequence, a start point, an end point in the sequence, dot size and dot color and draws a fractal image of the sequence. The fractal starts in the center of the canvas. The image is drawn by moving base by base along the sequence and dropping a midpoint between the actual point and the corner designated by the actual base. For more details see Jeffrey (1990) <doi:10.1093/nar/18.8.2163>, Hill, Schisler, and Singh (1992) <doi:10.1007/BF00178602>, and Löchel and Heider (2021) <doi:10.1016/j.csbj.2021.11.008>.

Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package dynr (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state-space or difference equation model. The continuous-time models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easy-to-learn model specification functions in R. Model fitting can be performed using single-subject time series data or multiple-subject longitudinal data. Ou, Hunter, & Chow (2019) <doi:10.32614%2FRJ-2019-012> provided a detailed introduction to the interface and more information on the algorithms.

Phase I/II adaptive dose-finding design for single-agent Molecularly Targeted Agent (MTA), according to the paper "Phase I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau Determination using Adaptive Randomization", Riviere Marie-Karelle et al. (2016) <doi:10.1177/0962280216631763>.

Model fitting and evaluation tools for double generalized linear models (DGLMs). This class of models uses one generalized linear model (GLM) to fit the specified response and a second GLM to fit the deviance of the first model.

Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.