It provides easy internationalization of Shiny applications. It can be used as standalone translation package to translate reports, interactive visualizations or graphical elements as well.

SKIFTI files contain brain imaging data in coordinates across Tract Based Spatial Statistics (TBSS) skeleton, which represent the brain white matter intensity values. skiftiTools provides a unified environment for reading, writing, visualizing and manipulating SKIFTI-format data. It supports the "subsetting", "concatenating", and using data as data.frame for R statistical functions. The SKIFTI data is structured for convenient access to the data and metadata, and includes support for visualizations. For more information see Merisaari et al. (2024) <doi:10.57736/87d2-0608>.

This package provides methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation. Jerald E. Pinto (â Equity Asset Valuation (4th Edition)â , 2020, ISBN: 9781119628194). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614).

Download data (tables and datasets) from the Swiss National Bank (SNB; <https://www.snb.ch/en>), the Swiss central bank. The package is lightweight and comes with few dependencies; suggested packages are used only if data is to be transformed into particular data structures, for instance into zoo objects. Downloaded data can optionally be cached, to avoid repeated downloads of the same files.

This package implements a method to combine multiple levels of multiple sequence alignment to uncover the structure of complex DNA rearrangements.

This package provides functions for self-determination motivation theory (SDT) to compute measures of motivation internalization, motivation simplex structure, and of the original and adjusted self-determination or relative autonomy index. SDT was introduced by Deci and Ryan (1985) <doi:10.1007/978-1-4899-2271-7>. See package?SDT for an overview.

Providing convenience functions to connect R with the Spotify application programming interface ('API'). At first it aims to help setting up the OAuth2.0 Authentication flow. The default output of the get_*() functions is tidy, but optionally the functions could return the raw response from the API as well. The search_*() and get_*() functions can be combined. See the vignette for more information and examples and the official Spotify for Developers website <https://developer.spotify.com/documentation/web-api/> for information about the Web API'.

This package provides tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap. See Ekstrøm CT, Gerds TA, Jensen, AK (2018). "Sequential rank agreement methods for comparison of ranked lists." _Biostatistics_, *20*(4), 582-598 <doi:10.1093/biostatistics/kxy017> for more information.

This package provides tools for the stochastic simulation of effectiveness scores to mitigate data-related limitations of Information Retrieval evaluation research, as described in Urbano and Nagler (2018) <doi:10.1145/3209978.3210043>. These tools include: fitting, selection and plotting distributions to model system effectiveness, transformation towards a prespecified expected value, proxy to fitting of copula models based on these distributions, and simulation of new evaluation data from these distributions and copula models.

Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing with data from complex sample surveys are available too.

This package provides a Shiny app including the Monaco editor. The Monaco editor is the code editor which powers VS Code'. It is particularly well developed for JavaScript'. In addition to the Monaco editor features, the app provides prettifiers and minifiers for multiple languages, SCSS and TypeScript compilers, code checking for C and C++ (requires cppcheck').

Encrypt text using a simple shifting substitution cipher with setcode(), providing two numeric keys used to define the encryption algorithm. The resulting text can be decoded using decode() function and the two numeric keys specified during encryption.

This package provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.

Slack <https://slack.com/> provides a service for teams to collaborate by sharing messages, images, links, files and more. Functions are provided that make it possible to interact with the Slack platform API'. When you need to share information or data from R, rather than resort to copy/ paste in e-mails or other services like Skype <https://www.skype.com/en/>, you can use this package to send well-formatted output from multiple R objects and expressions to all teammates at the same time with little effort. You can also send images from the current graphics device, R objects, and upload files.

Take screenshots from R command and locate an image position.

Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).

Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.

This package provides a simple, one-command package which runs an interactive dashboard capable of common visualizations for single cell RNA-seq. SeuratExplorer requires a processed Seurat object, which is saved as rds or qs2 file.

Fits a semiparametric spatiotemporal model for data with mixed frequencies, specifically where the response variable is observed at a lower frequency than some covariates. The estimation uses an iterative backfitting algorithm that combines a non-parametric smoothing spline for high-frequency data, parametric estimation for low-frequency and spatial neighborhood effects, and an autoregressive error structure. Methodology based on Malabanan, Lansangan, and Barrios (2022) <https://scienggj.org/2022/SciEnggJ%202022-vol15-no02-p90-107-Malabanan%20et%20al.pdf>.

This package provides tools for retrieving, organizing, and analyzing environmental data from the System Wide Monitoring Program of the National Estuarine Research Reserve System <https://cdmo.baruch.sc.edu/>. These tools address common challenges associated with continuous time series data for environmental decision making.

This package provides methods for the analysis of signed networks. This includes several measures for structural balance as introduced by Cartwright and Harary (1956) <doi:10.1037/h0046049>, blockmodeling algorithms from Doreian (2008) <doi:10.1016/j.socnet.2008.03.005>, various centrality indices, and projections of signed two-mode networks introduced by Schoch (2020) <doi:10.1080/0022250X.2019.1711376>.

Create mocked bindings to Shiny update functions within test function calls to automatically update input values. The mocked bindings simulate the communication between the server and UI components of a Shiny module in testServer().

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 a system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data in a â publication readyâ format. This is, the goal is to automatically generate plots with the highest quality possible, that can be used right away or with minimal modifications for a research article.