Add functionality to create drag and drop div elements in shiny.

Compute Time series Resistant Smooth 4253H, twice smoothing method.

Offers markdown output formats designed with various styles, allowing users to generate HTML reports tailored for scientific or machine learning showcase. The output has a contemporary appearance with vibrant visuals, providing numerous styles for effective highlighting. Created using the tufte <https://rstudio.github.io/tufte/> package code as a starting point.

This package provides a collection of data processing, visualization, and export functions to support soil survey operations. Many of the functions build on the `SoilProfileCollection` S4 class provided by the aqp package, extending baseline visualization to more elaborate depictions in the context of spatial and taxonomic data. While this package is primarily developed by and for the USDA-NRCS, in support of the National Cooperative Soil Survey, the authors strive for generalization sufficient to support any soil survey operation. Many of the included functions are used by the SoilWeb suite of websites and movile applications. These functions are provided here, with additional documentation, to enable others to replicate high quality versions of these figures for their own purposes.

In various domains, many datasets exhibit both high variable dependency and group structures, which necessitates their simultaneous estimation. This package provides functions for two subgroup identification methods based on penalized functions, both of which utilize factor model structures to adapt to data with cross-sectional dependency. The first method is the Subgroup Identification with Latent Factor Structure Method (SILFSM) we proposed. By employing Center-Augmented Regularization and factor structures, the SILFSM effectively eliminates data dependencies while identifying subgroups within datasets. For this model, we offer optimization functions based on two different methods: Coordinate Descent and our newly developed Difference of Convex-Alternating Direction Method of Multipliers (DC-ADMM) algorithms; the latter can be applied to cases where the distance function in Center-Augmented Regularization takes L1 and L2 forms. The other method is the Factor-Adjusted Pairwise Fusion Penalty (FA-PFP) model, which incorporates factor augmentation into the Pairwise Fusion Penalty (PFP) developed by Ma, S. and Huang, J. (2017) <doi:10.1080/01621459.2016.1148039>. Additionally, we provide a function for the Standard CAR (S-CAR) method, which does not consider the dependency and is for comparative analysis with other approaches. Furthermore, functions based on the Bayesian Information Criterion (BIC) of the SILFSM and the FA-PFP method are also included in SILFS for selecting tuning parameters. For more details of Subgroup Identification with Latent Factor Structure Method, please refer to He et al. (2024) <doi:10.48550/arXiv.2407.00882>.

Create sets of variables based on a mutual information approach. In this context, a set is a collection of distinct elements (e.g., variables) that can also be treated as a single entity. Mutual information, a concept from probability theory, quantifies the dependence between two variables by expressing how much information about one variable can be gained from observing the other. Furthermore, you can analyze, and visualize these sets in order to better understand the relationships among variables.

Perform joint segmentation on two signal dimensions derived from total read depth (intensity) and allele specific read depth (intensity) for whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data.

Data Sets for Kumar and Petersen (2012). Statistical Methods in Customer Relationship Management, Wiley: New York.

Assigns a score projection from 0 to 1 between a given in vivo stage and each single cluster from an in vitro dataset. The score is assigned based on the the fraction of specific markers of the in vivo stage that are conserved in the in vitro clusters <https://github.com/ScialdoneLab>.

This package provides tools for making, retrieving, displaying and solving sudoku games. This package is an alternative to the earlier sudoku-solver package, sudoku'. The present package uses a slightly different algorithm, has a simpler coding and presents a few more sugar tools, such as plot and print methods. Solved sudoku games are of some interest in Experimental Design as examples of Latin Square designs with additional balance constraints.

Extension to the spatstat package, containing interactive graphics capabilities.

This package provides a collection of classes and methods for working with times and dates. The code was originally available in S-PLUS'.

Identifies single nucleotide variants in next-generation sequencing data by estimating their local false discovery rates. For more details, see Karimnezhad, A. and Perkins, T. J. (2024) <doi:10.1038/s41598-024-51958-z>.

This package provides a system that computes metrics to assess the segmentation accuracy of geospatial data. These metrics calculate the discrepancy between segmented and reference objects, and indicate the segmentation accuracy. For more details on choosing evaluation metrics, we suggest seeing Costa et al. (2018) <doi:10.1016/j.rse.2017.11.024> and Jozdani et al. (2020) <doi:10.1016/j.isprsjprs.2020.01.002>.

It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.

This package provides a novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. seer package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.

This package provides a set of methods to implement Generalized Method of Moments and Maximal Likelihood methods for Random Utility Models. These methods are meant to provide inference on rank comparison data. These methods accept full, partial, and pairwise rankings, and provides methods to break down full or partial rankings into their pairwise components. Please see Generalized Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of some of our methods.

Input widget that allows to construct complex filtering queries in Shiny'. It's a wrapper for JavaScript library jQuery-QueryBuilder', check <https://querybuilder.js.org/>.

This package implements L0-constrained Multi-Task Learning and domain generalization algorithms. The algorithms are coded in Julia allowing for fast implementations of the coordinate descent and local combinatorial search algorithms. For more details, see a preprint of the paper: Loewinger et al., (2022) <arXiv:2212.08697>.

Convert text (and text in R objects) to Mocking SpongeBob case <https://knowyourmeme.com/memes/mocking-spongebob> and show them off in fun ways. CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem OFf IN Fun WayS.

Enables the ability to change or flash the title of the browser window during a shiny session.

Implementation of the boosting procedure with the simulation and extrapolation approach to address variable selection and estimation for high-dimensional data subject to measurement error in predictors. It can be used to address generalized linear models (GLM) in Chen (2023) <doi: 10.1007/s11222-023-10209-3> and the accelerated failure time (AFT) model in Chen and Qiu (2023) <doi: 10.1111/biom.13898>. Some relevant references include Chen and Yi (2021) <doi:10.1111/biom.13331> and Hastie, Tibshirani, and Friedman (2008, ISBN:978-0387848570).

Sejong(http://www.sejong.or.kr/) corpus and Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum) dictionaries for KoNLP.

Metapackage for implementing a variety of event-based models, with a focus on spatially explicit models. These include raster-based, event-based, and agent-based models. The core simulation components (provided by SpaDES.core') are built upon a discrete event simulation (DES; see Matloff (2011) ch 7.8.3 <https://nostarch.com/artofr.htm>) framework that facilitates modularity, and easily enables the user to include additional functionality by running user-built simulation modules (see also SpaDES.tools'). Included are numerous tools to visualize rasters and other maps (via quickPlot'), and caching methods for reproducible simulations (via reproducible'). Tools for running simulation experiments are provided by SpaDES.experiment'. Additional functionality is provided by the SpaDES.addins and SpaDES.shiny packages.