Simulation of simple and complex survival data including recurrent and multiple events and competing risks. See Moriña D, Navarro A. (2014) <doi:10.18637/jss.v059.i02> and Moriña D, Navarro A. (2017) <doi:10.1080/03610918.2016.1175621>.

Sensitivity analysis for trials with irregular and informative assessment times, based on a new influence function-based, augmented inverse intensity-weighted estimator.

Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.

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.

Collection (syllogi in greek) of real and fictitious data sets for teaching purposes. The datasets were manually entered by the author from the respective references as listed in the individual dataset documentation. The fictions datasets are the creation of the author, that he has found useful for teaching statistics.

This package provides a complete suite of tools for interacting with the Survey Solutions GraphQL API <https://demo.mysurvey.solutions/graphql/>. This package encompasses all currently available queries and mutations, including the latest features for map uploads. It is built on the modern httr2 package, offering a streamlined and efficient interface without relying on external GraphQL client packages. In addition to core API functionalities, the package includes a range of helper functions designed to facilitate the use of available query filters.

This package provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.

Interval fusion and selection procedures for regression with functional inputs. Methods include a semiparametric approach based on Sliced Inverse Regression (SIR), as described in <doi:10.1007/s11222-018-9806-6> (standard ridge and sparse SIR are also included in the package) and a random forest based approach, as described in <doi:10.1002/sam.11705>.

We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620> and Lindmark (2022) <doi:10.1007/s10260-021-00611-4>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.

Implementation of the SIC epsilon-telescope method, either using single or distributional (multiparameter) regression. Includes classical regression with normally distributed errors and robust regression, where the errors are from the Laplace distribution. The "smooth generalized normal distribution" is used, where the estimation of an additional shape parameter allows the user to move smoothly between both types of regression. See O'Neill and Burke (2022) "Robust Distributional Regression with Automatic Variable Selection" for more details. <doi:10.48550/arXiv.2212.07317>. This package also contains the data analyses from O'Neill and Burke (2023). "Variable selection using a smooth information criterion for distributional regression models". <doi:10.1007/s11222-023-10204-8>.

This package provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).

Insert Glide JavaScript component into Shiny applications for carousel or assistant-like user interfaces.

Compute the frequency distribution of a search term in a series of texts. For example, Arthur Conan Doyle wrote a total of 60 Sherlock Holmes stories, comprised of 54 short stories and 4 longer novels. I wanted to test my own subjective impression that, in many of the stories, Sherlock Holmes popularity was used as bait to induce the reader to read a story that is essentially not primarily a Sherlock Holmes story. I used the term "Holmes" as a search pattern, since Watson would frequently address him by name, or use his name to describe something that he was doing. My hypothesis is that the frequency distribution of the search pattern "Holmes" is a good proxy for the degree to which a story is or is not truly a Sherlock Holmes story. The results are presented in a manuscript that is available as a vignette and online at <https://barryzee.github.io/Concordance/index.html>.

This package provides a robust solution employing the SRS (Simple Random Sampling), systematic and PPS (Probability Proportional to Size) sampling methods, ensuring a methodical and representative selection of data. Seamlessly allocate predetermined allocations to smaller levels.

Using the adjustment method from Benjamini & Hochberg (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x>, this package determines which variables are significant under repeated testing with a given dataframe of p values and an user defined "q" threshold. It then returns the original dataframe along with a significance column where an asterisk denotes a significant p value after FDR calculation, and NA denotes all other p values. This package uses the Benjamini & Hochberg method specifically as described in Lee, S., & Lee, D. K. (2018) <doi:10.4097/kja.d.18.00242>.

Create Upset plots using a combination of ggplot2 and patchwork'.

Add fancy CSS effects to your shinydashboards or shiny apps. 100% compatible with shinydashboardPlus and bs4Dash'.

Spatio-temporal change of support (STCOS) methods are designed for statistical inference on geographic and time domains which differ from those on which the data were observed. In particular, a parsimonious class of STCOS models supporting Gaussian outcomes was introduced by Bradley, Wikle, and Holan <doi:10.1002/sta4.94>. The stcos package contains tools which facilitate use of STCOS models.

This package provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <arXiv:1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package shapper is a port of the Python library shap'.

By binding R functions and the Highcharts <http://www.highcharts.com/> charting library, sankeywheel package provides a simple way to draw dependency wheels and sankey diagrams.

This package provides a sparklyr extension that enables reading and writing TensorFlow TFRecord files via Apache Spark'.

Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder TMB in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in TMB by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2024) <doi:10.48550/arXiv.2110.07051>.

Implementation of single-source capture-recapture methods for population size estimation using zero-truncated, zero-one truncated and zero-truncated one-inflated Poisson, Geometric and Negative Binomial regression as well as Zelterman's and Chao's regression. Package includes point and interval estimators for the population size with variances estimated using analytical or bootstrap method. Details can be found in: van der Heijden et all. (2003) <doi:10.1191/1471082X03st057oa>, Böhning and van der Heijden (2019) <doi:10.1214/18-AOAS1232>, Böhning et al. (2020) Capture-Recapture Methods for the Social and Medical Sciences or Böhning and Friedl (2021) <doi:10.1007/s10260-021-00556-8>.

Calculates (unconditional) post-selection confidence intervals and p-values for the coefficients of (generalized) linear models.