Offers a comprehensive solution for managing empty states in Shiny applications. It provides tools to create both default and customizable components for scenarios where data is absent or doesn't match user-defined filters. The package prioritizes user experience, ensuring clarity and consistency even when data is not available to display.

Monitoring reporting rates of subject-level clinical events (e.g. adverse events, protocol deviations) reported by clinical trial sites is an important aspect of risk-based quality monitoring strategy. Sites that are under-reporting or over-reporting events can be detected using bootstrap simulations during which patients are redistributed between sites. Site-specific distributions of event reporting rates are generated that are used to assign probabilities to the observed reporting rates. (Koneswarakantha 2024 <doi:10.1007/s43441-024-00631-8>).

Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. In addition to out-performing traditional, lexicon-based sentiment analysis (see <https://benwiseman.github.io/sentiment.ai/#Benchmarks>), it also allows the user to create embedding vectors for text which can be used in other analyses. GPU acceleration is supported on Windows and Linux.

Miscellaneous functions for analysing species association and niche overlap.

This package provides access to packages developed for downloading, reading and analyzing microdata from household surveys in Integrated System of Household Surveys - SIPD conducted by Brazilian Institute of Geography and Statistics - IBGE. More information can be obtained from the official website <https://www.ibge.gov.br/>.

Testing the mediation effect of multiple SNPs on an outcome through a mediator.

This package implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of fitted probabilities and percentiles. For multiple experts, a weighted linear pool can be calculated. Also includes functions for eliciting beliefs about population distributions; eliciting multivariate distributions using a Gaussian copula; eliciting a Dirichlet distribution; eliciting distributions for variance parameters in a random effects meta-analysis model; survival extrapolation. R Shiny apps for most of the methods are included.

Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>) for the semiparametric setting introduced in Drost et al. (2009) <doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR data (Jentsch, C. and Weià , C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible simulation of INAR data.

Reports markers list, differentially expressed genes, associated pathways, cell-type annotations, does batch correction and other related single cell analyses all wrapped within Seurat'.

The sufficient forecasting (SF) method is implemented by this package for a single time series forecasting using many predictors and a possibly nonlinear forecasting function. Assuming that the predictors are driven by some latent factors, the SF first conducts factor analysis and then performs sufficient dimension reduction on the estimated factors to derive predictive indices for forecasting. The package implements several dimension reduction approaches, including principal components (PC), sliced inverse regression (SIR), and directional regression (DR). Methods for dimension reduction are as described in: Fan, J., Xue, L. and Yao, J. (2017) <doi:10.1016/j.jeconom.2017.08.009>, Luo, W., Xue, L., Yao, J. and Yu, X. (2022) <doi:10.1093/biomet/asab037> and Yu, X., Yao, J. and Xue, L. (2022) <doi:10.1080/07350015.2020.1813589>.

There is variation across AgNPs due to differences in characterization techniques and testing metrics employed in studies. To address this problem, we have developed a systematic evaluation framework called sysAgNPs'. Within this framework, Distribution Entropy (DE) is utilized to measure the uncertainty of feature categories of AgNPs, Proclivity Entropy (PE) assesses the preference of these categories, and Combination Entropy (CE) quantifies the uncertainty of feature combinations of AgNPs. Additionally, a Markov chain model is employed to examine the relationships among the sub-features of AgNPs and to determine a Transition Score (TS) scoring standard that is based on steady-state probabilities. The sysAgNPs framework provides metrics for evaluating AgNPs, which helps to unravel their complexity and facilitates effective comparisons among different AgNPs, thereby advancing the scientific research and application of these AgNPs.

This package provides a collection of classes and methods for working with indexed rectangular data. The index values can be calendar (timeSeries class) or numeric (signalSeries class). Methods are included for aggregation, alignment, merging, and summaries. The code was originally available in S-PLUS'.

This package provides an XY pad input for the Shiny framework. An XY pad is like a bivariate slider. It allows to pick up a pair of numbers.

This package provides utilities to create or suppress start-up messages.

This package provides systematic geometry-adaptive parameter optimization with statistical validation for experimental biological data. Combines ANOVA-based validation with systematic constraint configuration testing (log-scale, positive domain, Euclidean) through T,P,E testing. Only proceeds with parameter optimization when statistically significant biological effects are detected, preventing over-fitting to noise. Uses GALAHAD trust region methods with constraint projection from Conn et al. (2000) <doi:10.1137/S1052623497325107>, ANOVA-based validation following Fisher (1925) <doi:10.1007/978-1-4612-4380-9_6>, and effect size calculations per Cohen (1988, ISBN:0805802835). Designed for structured experimental data including kinetic curves, dose-response studies, and treatment comparisons where appropriate parameter constraints and statistical justification are important for meaningful biological interpretation. Developed at the Minnesota Center for Prion Research and Outreach at the University of Minnesota.

Calculate superior identification index and its extensions. Measure the performance of journals based on how well they could identify the top papers by any index (e.g. citation indices) according to Huang & Yang. (2022) <doi:10.1007/s11192-022-04372-z>. These methods could be extended to evaluate other entities such as institutes, countries, etc.

This package provides a spatial population can be generated based on spatially varying regression model under the assumption that observations are collected from a uniform two-dimensional grid consist of (m * m) lattice points with unit distance between any two neighbouring points. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<DOI:10.1080/10485252.2018.1499907>. This spatially generated data can be used to test different issues related to the statistical analysis of spatial data. This generated spatial data can be utilized in geographically weighted regression analysis for studying the spatially varying relationships among the variables.

This package implements the Smoothness-Penalized Deconvolution method for estimating a probability density under measurement error of Kent and Ruppert (2023) <doi:10.1080/01621459.2023.2259028>. The estimator is formed by computing a histogram of the error-contaminated data, and then finding an estimate that minimizes a reconstruction error plus a smoothness-inducing penalty term. The primary function, sped(), takes the data and error distribution, and returns the estimator as a function.

This package provides a set of basic functions for creating Moodle XML output files suited for importing questions in Moodle (a learning management system, see <https://moodle.org/> for more information).

Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating the squared deviations from sample variance. Besides indicating the contribution of squared deviations for the sample variability, these plots are capable of detecting characteristics of the distribution such as symmetry, skewness and outliers. A remarkable graphical method based on Sv-plot2 can determine the decision on testing hypotheses over one or two population means. In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.

This package provides a tool for bootstrapping new packages with useful defaults, including a test suite outline that passes checks and helpers for running tests, checking test coverage, building vignettes, and more. Package skeletons it creates are set up for pushing your package to GitHub and using other hosted services for building and test automation.

This package provides a suite of statistical methods for analysis of single-cell omics data including linear model-based methods for differential abundance analysis for individual level single-cell RNA-seq data. For more details see Zhang, et al. (Submitted to Bioinformatics)<https://github.com/Lujun995/DiSC_Replication_Code>.

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/>.

Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies, following a general simulation workflow that closely aligns with the approach described by Morris, White, and Crowther (2019) <DOI:10.1002/sim.8086>. Also includes functions for calculating bootstrap confidence intervals (including normal, basic, studentized, percentile, bias-corrected, and bias-corrected-and-accelerated) with tidy output, as well as for extrapolating confidence interval coverage rates and hypothesis test rejection rates following techniques suggested by Boos and Zhang (2000) <DOI:10.1080/01621459.2000.10474226>.