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 provides a complete data set of historic GB trig points in British National Grid (OSGB36) coordinate reference system. Trig points (aka triangulation stations) are fixed survey points used to improve the accuracy of map making in Great Britain during the 20th Century. Trig points are typically located on hilltops so still serve as a useful navigational aid for walkers and hikers today.

This package implements inverse and augmented inverse probability weighted estimators for common treatment effect parameters at an interim analysis with time-lagged outcome that may not be available for all enrolled subjects. Produces estimators, standard errors, and information that can be used to compute stopping boundaries using software that assumes that the estimators/test statistics have independent increments. Tsiatis, A. A. and Davidian, M., (2022) <doi:10.1002/sim.9580> .

This is a small package to provide consistent tick marks for plotting ggplot2 figures. It provides breaks and labels for ggplot2 without requiring ggplot2 to be installed.

This package provides a traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.

This package provides a simple wrapper around the Telegram Bot API (<https://core.telegram.org/bots/api>) to access Telegram's messaging facilities with ease (e.g. you send messages, images, files from R to your smartphone).

Automates translating the instructions of iatgen generated qsf (Qualtrics survey files) to other languages using either officially supported or user-supplied translations (for tutorial see Santos et al., 2023 <doi:10.17504/protocols.io.kxygx34jdg8j/v1>).

This package performs the detection of linear trend changes for univariate time series by implementing the bottom-up unbalanced wavelet transformation proposed by H. Maeng and P. Fryzlewicz (2023). The estimated number and locations of the change-points are returned with the piecewise-linear estimator for signal.

An interface to twitter-text', a JavaScript library which is responsible for determining the length/validity of a tweet and identifying/linking any URLs or special tags (e.g. mentions or hashtags) which may be present.

Defines a graphics device and functions for graphical output in terminal emulators that support graphical output. Currently terminals that support the Terminal Graphics Protocol (<https://sw.kovidgoyal.net/kitty/graphics-protocol/>) and terminal supporting Sixel (<https://en.wikipedia.org/wiki/Sixel>) are supported.

Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.

Datasets from Yotov, et al. (2016, ISBN:978-92-870-4367-2) "An Advanced Guide to Trade Policy Analysis" and functions to report regression summaries with clustered robust standard errors.

First - Generates (potentially high-dimensional) high-frequency and low-frequency series for simulation studies in temporal disaggregation; Second - a toolkit utilizing temporal disaggregation and benchmarking techniques with a low-dimensional matrix of indicator series previously proposed in Dagum and Cholette (2006, ISBN:978-0-387-35439-2) ; and Third - novel techniques proposed by Mosley, Gibberd and Eckley (2021) <arXiv:2108.05783> for disaggregating low-frequency series in the presence of high-dimensional indicator matrices.

Tidy tools for NetCDF data sources. Explore the contents of a NetCDF source (file or URL) presented as variables organized by grid with a database-like interface. The hyper_filter() interactive function translates the filter value or index expressions to array-slicing form. No data is read until explicitly requested, as a data frame or list of arrays via hyper_tibble() or hyper_array().

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including dplyr', stats', xts', forecast', slider', padr', recipes', and rsample'.

This package provides functions are provided for prior specification in divergence time estimation using fossils as well as other kinds of data. It provides tools for interacting with the input and output of Bayesian platforms in evolutionary biology such as BEAST2', MrBayes', RevBayes', or MCMCTree'. It Implements a simple measure similarity between probability density functions for comparing prior and posterior Bayesian densities, as well as code for calculating the combination of distributions using conflation of Hill (2008). Functions for estimating the origination time in collections of distributions using the x-intercept (e.g., Draper and Smith, 1998) and stratigraphic intervals (Marshall 2010) are also available. Hill, T. 2008. "Conflations of probability distributions". Transactions of the American Mathematical Society, 363:3351-3372. <doi:10.48550/arXiv.0808.1808>, Draper, N. R. and Smith, H. 1998. "Applied Regression Analysis". 1--706. Wiley Interscience, New York. <DOI:10.1002/9781118625590>, Marshall, C. R. 2010. "Using confidence intervals to quantify the uncertainty in the end-points of stratigraphic ranges". Quantitative Methods in Paleobiology, 291--316. <DOI:10.1017/S1089332600001911>.

The data that is generated from independent and consecutive GillespieSSA runs for a generic biochemical network is formatted as rows and constitutes an observation. The first column of each row is the computed timestep for each run. Subsequent columns are used for the number of molecules of each participating molecular species or "metabolite" of a generic biochemical network. In this way TemporalGSSA', is a wrapper for the R-package GillespieSSA'. The number of observations must be at least 30. This will generate data that is statistically significant. TemporalGSSA', transforms this raw data into a simulation time-dependent and metabolite-specific trial. Each such trial is defined as a set of linear models (n >= 30) between a timestep and number of molecules for a metabolite. Each linear model is characterized by coefficients such as the slope, arbitrary constant, etc. The user must enter an integer from 1-4. These specify the statistical modality utilized to compute a representative timestep (mean, median, random, all). These arguments are mandatory and will be checked. Whilst, the numeric indicator "0" indicates suitability, "1" prompts the user to revise and re-enter their data. An optional logical argument controls the output to the console with the default being "TRUE" (curtailed) whilst "FALSE" (verbose). The coefficients of each linear model are averaged (mean slope, mean constant) and are incorporated into a metabolite-specific linear regression model as the dependent variable. The independent variable is the representative timestep chosen previously. The generated data is the imputed molecule number for an in silico experiment with (n >=30) observations. These steps can be replicated with multiple set of observations. The generated "technical replicates" can be statistically evaluated (mean, standard deviation) and will constitute simulation time-dependent molecules for each metabolite. For SSA-generated datasets with varying simulation times TemporalGSSA will generate a simulation time-dependent trajectory for each metabolite of the biochemical network under study. The relevant publication with the mathematical derivation of the algorithm is (2022, Journal of Bioinformatics and Computational Biology) <doi:10.1142/S0219720022500184>. The algorithm has been deployed in the following publications (2021, Heliyon) <doi:10.1016/j.heliyon.2021.e07466> and (2016, Journal of Theoretical Biology) <doi:10.1016/j.jtbi.2016.07.002>.

This package implements the Temporal Forest algorithm for feature selection in high-dimensional longitudinal data. The method combines time-aware network construction via weighted gene co-expression network analysis (WGCNA), module-based feature screening, and stability selection using tree-based models. This package provides tools for reproducible longitudinal analysis, closely following the methodology described in Shao, Moore, and Ramirez (2025) <https://github.com/SisiShao/TemporalForest>.

Simple tabulation should be dead simple. This package is an opinionated approach to easy tabulations while also providing exact numbers and allowing for re-usability. This is achieved by providing tabulations as data.frames with columns for values, optional variable names, frequency counts including and excluding NAs and percentages for counts including and excluding NAs. Also values are automatically sorted by in decreasing order of frequency counts to allow for fast skimming of the most important information.

This contains functions that can be used to estimate the time-dependent precision-recall curve (PRC) and the corresponding area under the PRC for right-censored survival data. It also compute time-dependent ROC curve and its corresponding area under the ROC curve (AUC). See Beyene, Chen and Kifle (2024) <doi:10.1002/bimj.202300135>.

This package implements combined p-value functions for two trials along with compatible combined point and interval estimates as described in Pawel, Roos, and Held (2025) <doi:10.48550/arXiv.2503.10246>.

Framework to run Monte Carlo simulations over a parameter grid. Allows to parallelize the simulations. Generates plots and LaTeX tables summarizing the results from the simulation.

Fit a threshold regression model for Interval Censored Data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.

This package provides a system for personalized exercise plan recommendations for T2D (Type 2 Diabetes) patients based on the primary outcome of HbA1c (Glycated Hemoglobin). You provide the individual's information, and T2DFitTailor details the exercise plan and predicts the intervention's effectiveness.