Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the networkDynamic format. This work was supported by grant R01HD68395 from the National Institute of Health.

This package provides a diverse collection of time series datasets spanning various fields such as economics, finance, energy, healthcare, and more. Designed to support time series analysis in R by offering datasets from multiple disciplines, making it a valuable resource for researchers and analysts.

Schedule R scripts/processes with the Windows task scheduler. This allows R users to automate R processes on specific time points from R itself.

Return the first four moments of the SMN distributions (Normal, Student-t, Pearson VII, Slash or Contaminated Normal).

We present a range of simulations to aid researchers in determining appropriate sample sizes when performing critical thermal limits studies (e.g. CTmin/CTmin experiments). A number of wrapper functions are provided for plotting and summarising outputs from these simulations. This package is presented in van Steenderen, C.J.M., Sutton, G.F., Owen, C.A., Martin, G.D., and Coetzee, J.A. Sample size assessments for thermal physiology studies: An R package and R Shiny application. 2023. Physiological Entomology. <doi:10.1111/phen.12416>. The GUI version of this package is available on the R Shiny online server at: <https://clarkevansteenderen.shinyapps.io/ThermalSampleR_Shiny/> , or it is accessible via GitHub at <https://github.com/clarkevansteenderen/ThermalSampleR_Shiny/>. We would like to thank Grant Duffy (University of Otago, Dundedin, New Zealand) for granting us permission to use the source code for the Test of Total Equivalency function.

Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the mgcv package to specify splines via the formula interface. See Thorson et al. (2025) <doi:10.1111/geb.70035> for more details.

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>.

This package provides the "r, q, p, and d" distribution functions for the triangle distribution. Also includes maximum likelihood estimation of parameters.

Univariate time series operations that follow an opinionated design. The main principle of transx is to keep the number of observations the same. Operations that reduce this number have to fill the observations gap.

Swift and seamless Single Sign-On (SSO) integration. Designed for effortless compatibility with popular Single Sign-On providers like Google and Microsoft, it streamlines authentication, enhancing both user experience and application security. Elevate your shiny applications for a simplified, unified, and secure authentication process.

Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics. Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions. For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.

Estimation of group-based trajectory models, including finite mixture models for longitudinal data, supporting censored normal, zero-inflated Poisson, logit, and beta distributions, using expectation-maximization and quasi-Newton methods, with tools for model selection, diagnostics, and visualization of latent trajectory groups, <doi:10.4159/9780674041318>, Nagin, D. (2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press. and Noel (2022), <https://orbilu.uni.lu/>, thesis.

Perform and Runtime statistical comparisons between models. This package aims at choosing the best model for a particular dataset, regarding its discriminant power and runtime.

Plot official statistics time series conveniently: automatic legends, highlight windows, stacked bar chars with positive and negative contributions, sum-as-line option, two y-axes with automatic horizontal grids that fit both axes and other popular chart types. tstools comes with a plethora of defaults to let you plot without setting an abundance of parameters first, but gives you the flexibility to tweak the defaults. In addition to charts, tstools provides a super fast, data.table backed time series I/O that allows the user to export / import long format, wide format and transposed wide format data to various file types.

Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).

This package provides functionality of a statistical testing implementation whether a dataset comes from a symmetric distribution when the center of symmetry is unknown, including Wilcoxon test and sign test procedure. In addition, sample size determination for both tests is provided. The Wilcoxon test procedure is described in Vexler et al. (2023) <https://www.sciencedirect.com/science/article/abs/pii/S0167947323000579>, and the sign test is outlined in Gastwirth (1971) <https://www.jstor.org/stable/2284233>.

This package provides a new measure of similarity between a pair of mass spectrometry (MS) experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. Truncated rank correlation as a robust measure of test-retest reliability in mass spectrometry data. For more details see Lim et al. (2019) <doi:10.1515/sagmb-2018-0056>.

This package provides functions for statistical analysis, modeling and simulation of time series with state space model, based on the methodology in Kitagawa (2020, ISBN: 978-0-367-18733-0).

Visualisation, analysis and quality control of conversational data. Rapid and visual insights into the nature, timing and quality of time-aligned annotations in conversational corpora. For more details, see Dingemanse et al., (2022) <doi:10.18653/v1/2022.acl-long.385>.

This package provides methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.

Loads the 5 packages in the Tidy Consultant Universe. This collection of packages is useful for anyone doing data science, data analysis, or quantitative consulting. The functions in these packages range from data cleaning, data validation, data binning, statistical modeling, and file exporting.

This package provides a set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. TensorPreAve provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. See Chen and Lam (2023) <arXiv:2208.04012> for more details.

This package contains functions for applying the T^2-test for equivalence. The T^2-test for equivalence is a multivariate two-sample equivalence test. Distance measure of the test is the Mahalanobis distance. For multivariate normally distributed data the T^2-test for equivalence is exact and UMPI. The function T2EQ() implements the T^2-test for equivalence according to Wellek (2010) <DOI:10.1201/ebk1439808184>. The function T2EQ.dissolution.profiles.hoffelder() implements a variant of the T^2-test for equivalence according to Hoffelder (2016) <http://www.ecv.de/suse_item.php?suseId=Z|pi|8430> for the equivalence comparison of highly variable dissolution profiles.

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