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

This package provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) <doi:10.1080/01621459.2016.1193022>. For details of TPR, see Zhang X, Li L (2017) <doi:10.1080/00401706.2016.1272495>. For details of 1D algorithm, see Cook RD, Zhang X (2016) <doi:10.1080/10618600.2015.1029577>. For details of ECD algorithm, see Cook RD, Zhang X (2018) <doi:10.5705/ss.202016.0037>. For more details of the package, see Zeng J, Wang W, Zhang X (2021) <doi:10.18637/jss.v099.i12>.

This package provides a kernel of functions for programming time series methods in a way that is relatively independently of the representation of time. Also provides plotting, time windowing, and some other utility functions which are specifically intended for time series. See the Guide distributed as a vignette, or ?tframe.Intro for more details. (User utilities are in package tfplot.).

This package provides a step-up test for genetic rare variants in a gene or in a pathway. The method determines an optimal grouping of rare variants analytically. The method has been described in Hoffmann TJ, Marini NJ, and Witte JS (2010) <doi:10.1371/journal.pone.0013584>.

This package provides a toolbox to assist with statistical analysis of animal trajectories. It provides simple access to algorithms for calculating and assessing a variety of characteristics such as speed and acceleration, as well as multiple measures of straightness or tortuosity. Some support is provided for 3-dimensional trajectories. McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.

Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R.

Doubly robust estimation for the mean of an arbitrarily transformed survival time under covariate-induced dependent left truncation and noninformative right censoring. The functions truncAIPW(), truncAIPW_cen1(), and truncAIPW_cen2() compute the doubly robust estimators under the scenario without censoring and the two censoring scenarios, respectively. The package also contains three simulated data sets simu', simu_c1', and simu_c2', which are used to illustrate the usage of the functions in this package. Reference: Wang, Y., Ying, A., Xu, R. (2022) "Doubly robust estimation under covariate-induced dependent left truncation" <arXiv:2208.06836>.

Performing the hypothesis tests for the two sample problem based on order statistics and power comparisons. Calculate the test statistic, density, distribution function, quantile function, random number generation and others.

This package provides a set of functions to implement Time Series Cointegrated System (TSCS) spatial interpolation and relevant data visualization.

Lightweight extension of the base R graphics system, with support for automatic legends, facets, themes, and various other enhancements.

This package implements target trial emulation methods to apply randomized clinical trial design and analysis in an observational setting. Using marginal structural models, it can estimate intention-to-treat and per-protocol effects in emulated trials using electronic health records. A description and application of the method can be found in Danaei et al (2013) <doi:10.1177/0962280211403603>.

An opinionated, tidyverse-native toolkit for intensive longitudinal data (ILD). Encodes time structure, enforces within-between decomposition, provides spacing-aware lags, and integrates diagnostics and visualization. Use ild_prepare(), ild_center(), ild_lag(), and related functions for a unified pipeline from raw EMA/diary data to interpretable models.

Differential analysis of tumor tissue immune cell type abundance based on RNA-seq gene-level expression from The Cancer Genome Atlas (TCGA; <https://pancanatlas.xenahubs.net>) database.

This package provides a lightweight, WebSocket'-enabled proxy server for hosting multiple shiny applications with automatic health monitoring, session management, and resource cleanup. Provides a simple entry point to run the server using a JSON configuration file.

Suite of tools to support the practice of tada science. It includes an engaging package roulette that is designed to facilitate learning about new packages.

This package provides a collection of functions to plot acid/base titration curves (pH vs. volume of titrant), complexation titration curves (pMetal vs. volume of EDTA), redox titration curves (potential vs.volume of titrant), and precipitation titration curves (either pAnalyte or pTitrant vs. volume of titrant). Options include the titration of mixtures, the ability to overlay two or more titration curves, and the ability to show equivalence points.

This package implements harmonic analysis of tidal and sea-level data. Over 400 harmonic tidal constituents can be estimated, all with daily nodal corrections. Time-varying mean sea-levels can also be used.

This package provides data frames for forest or tree data structures. You can create forest data structures from data frames and process them based on their hierarchies.

Accurately estimates phase shifts by accounting for period changes and for the point in the circadian cycle at which the stimulus occurs. See Tackenberg et al. (2018) <doi:10.1177/0748730418768116>.

Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.

This package provides functions for compounding and discounting calculations included here serve as a complete reference for various scenarios of time value of money. Raymond M. Brooks (â Financial Management,â 2018, ISBN: 9780134730417). Sheridan Titman, Arthur J. Keown, John D. Martin (â Financial Management: Principles and Applications,â 2017, ISBN: 9780134417219). Jonathan Berk, Peter DeMarzo, David Stangeland, Andras Marosi (â Fundamentals of Corporate Finance,â 2019, ISBN: 9780134735313). S. A. Hummelbrunner, Kelly Halliday, Ali R. Hassanlou (â Contemporary Business Mathematics with Canadian Applications,â 2020, ISBN: 9780135285015).

Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.

Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) <doi:10.1080/01621459.1993.10476408> to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) <doi:10.1111/1467-9892.00203>, <doi:10.1006/jmva.2000.1942> and Cotta (2017) <doi:10.13140/RG.2.2.14092.10883> are provided. The robust pseudo-periodogram of Molinares et. al. (2009) <doi:10.1016/j.jspi.2008.12.014> is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.

Several functions to allow comparisons of data across different geographies, in particular for Canadian census data from different censuses.