Get the most appropriate autoregressive integrated moving average, generalized auto-regressive conditional heteroscedasticity and Markov switching GARCH model. For method details see Haas M, Mittnik S, Paolella MS (2004). <doi:10.1093/jjfinec/nbh020>, Bollerslev T (1986). <doi:10.1016/0304-4076(86)90063-1>.

This package provides function shinyShortcut() that, when given the base directory of a shiny application, will produce an executable file that runs the shiny app directly in the user's default browser. Tested on both windows and unix machines. Inspired by and borrowing from <http://www.mango-solutions.com/wp/2017/03/shiny-based-tablet-or-desktop-app/>.

This package provides several datasets useful for processing and analysis of text in Turkish from an online shopping platform.

Automated unit testing of Shiny applications through a headless Chromium browser.

Detection of outliers and influential errors using a latent variable model.

Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster Analysis (SCA) for multivariate data analysis. The package provides comprehensive tools for feature selection, model training, prediction, and evaluation in hydrological and environmental modeling applications. Key functionalities include recursive feature elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag (OOB) validation, and ensemble prediction capabilities. The package supports both single and multivariate response variables, making it suitable for complex environmental modeling scenarios. For more details see Li et al. (2021) <doi:10.5194/hess-25-4947-2021>.

This package provides functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>. Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2.

Extended Susceptible-Exposed-Infected-Recovery Model for handling high false negative rate and symptom based administration of diagnostic tests. <doi:10.1101/2020.09.24.20200238>.

This package provides convenience functions to replace hyphen-minuses (ASCII 45) with proper minus signs (Unicode character 2212). The true minus matches the plus symbol in width, line thickness, and height above the baseline. It was designed for mathematics, looks better in presentation, and is understood properly by screen readers.

Validate data.frames against schemas to ensure that data matches expectations. Define schemas using tidyselect and predicate functions for type consistency, nullability, and more. Schema failure messages can be tailored for non-technical users and are ideal for user-facing applications such as in shiny or plumber'.

Fitting a smooth path to a given set of noisy spherical data observed at known time points. It implements a piecewise geodesic curve fitting method on the unit sphere based on a velocity-based penalization scheme. The proposed approach is implemented using the Riemannian block coordinate descent algorithm. To understand the method and algorithm, one can refer to Bak, K. Y., Shin, J. K., & Koo, J. Y. (2023) <doi:10.1080/02664763.2022.2054962> for the case of order 1. Additionally, this package includes various functions necessary for handling spherical data.

This package provides a set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. spsurv includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) <arXiv:2003.10548>.

Main properties and regression procedures using a generalization of the Dirichlet distribution called Simplicial Generalized Beta distribution. It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package.

Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) <doi:10.1002/sim.7622>, the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) <doi:10.1002/sim.8315>, the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) <doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) <doi:10.1002/sim.9150> that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2023) <doi:10.1080/00224065.2022.2081104>, and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) <doi:10.1080/24725854.2019.1696496>.

Ratings, votes, swear words and sentiments are analysed for the show SouthPark through a Shiny application after web scraping from IMDB and the website <https://southpark.fandom.com/wiki/South_Park_Archives>.

This package performs parametric synthesis of sounds with harmonic and noise components such as animal vocalizations or human voice. Also offers tools for audio manipulation and acoustic analysis, including pitch tracking, spectral analysis, audio segmentation, pitch and formant shifting, etc. Includes four interactive web apps for synthesizing and annotating audio, manually correcting pitch contours, and measuring formant frequencies. Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.

Smoothing signals and computing their derivatives is a common requirement in signal processing workflows. Savitzky-Golay filters are a established method able to do both (Savitzky and Golay, 1964 <doi:10.1021/ac60214a047>). This package implements one dimensional Savitzky-Golay filters that can be applied to vectors and matrices (either row-wise or column-wise). Vectorization and memory allocations have been profiled to reduce computational fingerprint. Short filter lengths are implemented in the direct space, while longer filters are implemented in frequency space, using a Fast Fourier Transform (FFT).

Seamless and standardized interaction with data exported from the clinical data management system (CDMS) secuTrial'<https://www.secutrial.com>. The primary data export the package works with is a standard non-rectangular export.

Miscellaneous functions for working with stars objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.

Interactive shiny application for working with Structural Equation Modelling technique. Runtime examples are provided in the package function as well as at <https://kartikeyab.shinyapps.io/semwebappk/> .

Create short sprint acceleration-velocity (AVP) and force-velocity (FVP) profiles and predict kinematic and kinetic variables using the timing-gate split times, laser or radar gun data, tether devices data, as well as the data provided by the GPS and LPS monitoring systems. The modeling method utilized in this package is based on the works of Furusawa K, Hill AV, Parkinson JL (1927) <doi: 10.1098/rspb.1927.0035>, Greene PR. (1986) <doi: 10.1016/0025-5564(86)90063-5>, Chelly SM, Denis C. (2001) <doi: 10.1097/00005768-200102000-00024>, Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) <doi: 10.1519/JSC.0000000000002081>, Samozino P. (2018) <doi: 10.1007/978-3-319-05633-3_11>, Samozino P. and Peyrot N., et al (2022) <doi: 10.1111/sms.14097>, Clavel, P., et al (2023) <doi: 10.1016/j.jbiomech.2023.111602>, Jovanovic M. (2023) <doi: 10.1080/10255842.2023.2170713>, and Jovanovic M., et al (2024) <doi: 10.3390/s24092894>.

Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. <arXiv:1709.10074> for methodological details.

This package provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.

Shiny module for easily sharing files between users. Admin can add, remove, edit and download file. User can only download file. It's also possible to manage files using R functions directly.