Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.

Translate R control flow expressions into Tensorflow graphs.

This package provides a universal non-uniform random number generator for quite arbitrary distributions with piecewise twice differentiable densities.

This package provides users a quick exploratory dive into common visualizations without writing a single line of code given the users data follows the Analysis Data Model (ADaM) standards put forth by the Clinical Data Interchange Standards Consortium (CDISC) <https://www.cdisc.org>. Prominent modules/ features of the application are the Table Generator, Population Explorer, and the Individual Explorer. The Table Generator allows users to drag and drop variables and desired statistics (frequencies, means, ANOVA, t-test, and other summary statistics) into bins that automagically create stunning tables with validated information. The Population Explorer offers various plots to visualize general trends in the population from various vantage points. Plot modules currently include scatter plot, spaghetti plot, box plot, histogram, means plot, and bar plot. Each plot type allows the user to plot uploaded variables against one another, and dissect the population by filtering out certain subjects. Last, the Individual Explorer establishes a cohesive patient narrative, allowing the user to interact with patient metrics (params) by visit or plotting important patient events on a timeline. All modules allow for concise filtering & downloading bulk outputs into html or pdf formats to save for later.

Easily carry out latent profile analysis ("LPA"), determine the correct number of classes based on best practices, and tabulate and plot the results. Provides functionality to estimate commonly-specified models with free means, variances, and covariances for each profile. Follows a tidy approach, in that output is in the form of a data frame that can subsequently be computed on. Models can be estimated using the free open source R packages Mclust and OpenMx', or using the commercial program MPlus', via the MplusAutomation package.

This package provides functions for the computationally efficient simulation of dynamic networks estimated with the statistical framework of temporal exponential random graph models, implemented in the tergm package.

Allows the user to draw probabilistic samples and make inferences from a finite population based on several sampling designs.

This package provides functions for density, cumulative density, quantile and simulation of Tukey g-and-h (1977) distributions. The quantile-based transformation (Hoaglin 1985 <doi:10.1002/9781118150702.ch11>) and its reverse transformation, as well as the letter-value based estimates (Hoaglin 1985), are also provided.

Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) <doi:10.2202/1557-4679.1008>), estimators for risk differences and relative risks (Richardson et al. (2017) <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

For high-dimensional data whose main feature is a large number, p, of variables but a small sample size, the null hypothesis that the marginal distributions of p variables are the same for two groups is tested. We propose a test statistic motivated by the simple idea of comparing, for each of the p variables, the empirical characteristic functions computed from the two samples. If one rejects this global null hypothesis of no differences in distributions between the two groups, a set of permutation p-values is reported to identify which variables are not equally distributed in both groups.

The function TailClassifier() suggests one of the following types of tail for your discrete data: 1) Power decaying tail; 2) Sub-exponential decaying tail; and 3) Near-exponential decaying tail. The function also provides an estimate of the parameter for the classified-distribution as a reference.

This package provides utilities to create and use lenses to simplify data manipulation. Lenses are composable getter/setter pairs that provide a functional approach to manipulating deeply nested data structures, e.g., elements within list columns in data frames. The implementation is based on the earlier lenses R package <https://github.com/cfhammill/lenses>, which was inspired by the Haskell lens package by Kmett (2012) <https://github.com/ekmett/lens>, one of the most widely referenced implementations of lenses. For additional background and history on the theory of lenses, see the lens package wiki: <https://github.com/ekmett/lens/wiki/History-of-Lenses>.

Tidying functions built on data.table to provide quick and efficient data manipulation with minimal overhead.

Leveraging (large) language models for automatic topic labeling. The main function converts a list of top terms into a label for each topic. Hence, it is complementary to any topic modeling package that produces a list of top terms for each topic. While human judgement is indispensable for topic validation (i.e., inspecting top terms and most representative documents), automatic topic labeling can be a valuable tool for researchers in various scenarios.

Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) <https://tel.archives-ouvertes.fr/tel-01971574v1>) or the False Discovery Rate (Cai & Liu (2016) <doi:10.1080/01621459.2014.999157>). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures.

Calculate optimal Zhong's two-/three-stage Phase II designs (see Zhong (2012) <doi:10.1016/j.cct.2012.07.006>). Generate Target Toxicity decision table for Phase I dose-finding (two-/three-stage). This package also allows users to run dose-finding simulations based on customized decision table.

Additive hazards models with two stage residual inclusion method are fitted under either survival data or competing risks data. The estimator incorporates an instrumental variable and therefore can recover causal estimand in the presence of unmeasured confounding under some assumptions. A.Ying, R. Xu and J. Murphy. (2019) <doi:10.1002/sim.8071>.

Forecasting competitions are of increasing importance as a mean to learn best practices and gain knowledge. Data leakage is one of the most common issues that can often be found in competitions. Data leaks can happen when the training data contains information about the test data. For example: randomly chosen blocks of time series are concatenated to form a new time series, scale-shifts, repeating patterns in time series, white noise is added in the original time series to form a new time series, etc. tsdataleaks package can be used to detect data leakages in a collection of time series.

This package provides a tool to obtain tumor growth rates from clinical trial patient data. Output includes individual and summary data for tumor growth rate estimates as well as optional plots of the observed and predicted tumor quantity over time.

This package provides a clinically meaningful measures of treatment effects for right-censored data are provided, based on the concept of Kendall's tau, along with the corresponding inference procedures. Two plots of tau processes, with the option to account for the cure fraction or not, are available. The plots of tau processes serve as useful graphical tools for monitoring the relative performances over time.

For writing tables with custom formats in a Excel file ready to be distributed.

Helps the R users to get data from Tushare Pro'<https://tushare.pro>. Tushare Pro is a platform as well as a community with a lot of staffs working in financial area. We support financial data such as stock price, financial report statements and digital coins data.

This package implements a decomposition of the two-way fixed effects instrumental variable estimator into all possible Wald difference-in-differences estimators. Provides functions to summarize the contribution of different cohort comparisons to the overall two-way fixed effects instrumental variable estimate, with or without controls. The method is described in Miyaji (2024) <doi:10.48550/arXiv.2405.16467>.

Record all tree-ring Shapefile of tree disk with GIS soft Qgis and interpolating model from high resolution tree disk image.