Sudoku designs (Bailey et al., 2008<doi:10.1080/00029890.2008.11920542>) can be used as experimental designs which tackle one extra source of variation than conventional Latin square designs. Although Sudoku designs are similar to Latin square designs, only addition is the region concept. Some very important functions related to row-column designs as well as block designs along with basic functions are included in this package.

Generally, soil functionality is characterized by its capability to sustain microbial activity, nutritional element supply, structural stability and aid for crop production. Since soil functions can be linked to 80% of ecosystem services, conservation of degraded land should strive to restore not only the capacity of soil to sustain flora but also ecosystem provisions. The primary ecosystem services of soil are carbon sequestration, food or biomass production, provision of microbial habitat, nutrient recycling. However, the actual magnitude of soil functions provided by agricultural land uses has never been quantified. Nutrient supply capacity (NSC) is a measure of nutrient dynamics in restored land uses. Carbon accumulation proficiency (CAP) is a measure of ecosystem carbon sequestration. Biological activity index (BAI) is the average of responses of all enzyme activities in treated land over control/reference land. The CAP parameter investigates how land uses may affect carbon flows, retention, and sequestration. The CAP provides a signal for C cycles, flows, and the systems relative operational supremacy.

Data sets utilized by the SGP package as exemplars for users to conduct their own student growth percentiles (SGP) analyses.

It offers functions for creating dashboard with Fomantic UI.

Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed. Dynamic cluster size is also available.

The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.

Implementation of SPECS, your favourite Single-Equation Penalized Error-Correction Selector developed in Smeekes and Wijler (2021) <doi:10.1016/j.jeconom.2020.07.021>. SPECS provides a fully automated estimation procedure for large and potentially (co)integrated datasets. The dataset in levels is converted to a conditional error-correction model, either by the user or by means of the functions included in this package, and various specialised forms of penalized regression can be applied to the model. Automated options for initializing and selecting a sequence of penalties, as well as the construction of penalty weights via an initial estimator, are available. Moreover, the user may choose from a number of pre-specified deterministic configurations to further simplify the model building process.

This software is useful for loading .fasta or .gbk files, and for retrieving sequences from GenBank dataset <https://www.ncbi.nlm.nih.gov/genbank/>. This package allows to detect differences or asymmetries based on nucleotide composition by using local linear kernel smoothers. Also, it is possible to draw inference about critical points (i. e. maximum or minimum points) related with the derivative curves. Additionally, bootstrap methods have been used for estimating confidence intervals and speed computational techniques (binning techniques) have been implemented in seq2R'.

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>.

Simulation tools for closed-loop simulation are provided for the MSEtool operating model to inform data-rich fisheries. SAMtool provides a conditioning model, assessment models of varying complexity with standardized reporting, model-based management procedures, and diagnostic tools for evaluating assessments inside closed-loop simulation.

Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.

This package provides an easy-to-use module for adding a chat to a Shiny app. Allows users to send messages and view messages from other users. Messages can be stored in a database or a .rds file.

This package provides a set of tools developed at Simularia for Simularia, to help preprocessing and post-processing of meteorological and air quality data.

Launch an application by a simple click without opening R or RStudio. The package has 3 functions of which only one is essential in its use, `shiny.exe()`. It generates a script in the open shiny project then create a shortcut in the same folder that allows you to launch the app by clicking.If you set `host = public'`, the application will be launched on the public server to which you are connected. Thus, all other devices connected to the same server will be able to access the application through the link of your `IPv4` extended by the port. You can stop the application by leaving the terminal opened by the shortcut.

This package provides functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) <doi:10.48550/arXiv.1712.03561>. The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit.

This package provides tools for smoothing and tidying spatial features (i.e. lines and polygons) to make them more aesthetically pleasing. Smooth curves, fill holes, and remove small fragments from lines and polygons.

This package provides option settings management that goes beyond R's default options function. With this package, users can define their own option settings manager holding option names, default values and (if so desired) ranges or sets of allowed option values that will be automatically checked. Settings can then be retrieved, altered and reset to defaults with ease. For R programmers and package developers it offers cloning and merging functionality which allows for conveniently defining global and local options, possibly in a multilevel options hierarchy. See the package vignette for some examples concerning functions, S4 classes, and reference classes. There are convenience functions to reset par() and options() to their factory defaults'.

This package implements the Simple Non-Iterative Clustering algorithm for superpixel segmentation of multi-band images, as introduced by Achanta and Susstrunk (2017) <doi:10.1109/CVPR.2017.520>. Supports both standard image arrays and geospatial raster objects, with a design that can be extended to other spatial data frameworks. The algorithm groups adjacent pixels into compact, coherent regions based on spectral similarity and spatial proximity. A high-performance implementation supports images with arbitrary spectral bands.

Calculate numerical agricultural soil management indicators from on a management timeline of an arable field. Currently, indicators for carbon (C) input into the soil system, soil tillage intensity rating (STIR), number of soil cover and living plant cover days, N fertilization and livestock intensity, and plant diversity are implemented. The functions can also be used independently of the management timeline to calculate some indicators. The package contains tables with reference information for the functions, as well as a *.xlsx template to collect the management data.

Easily analyze and visualize differences between samples (e.g., benchmark comparisons, nonresponse comparisons in surveys) on three levels. The comparisons can be univariate, bivariate or multivariate. On univariate level the variables of interest of a survey and a comparison survey (i.e. benchmark) are compared, by calculating one of several difference measures (e.g., relative difference in mean), and an average difference between the surveys. On bivariate level a function can calculate significant differences in correlations for the surveys. And on multivariate levels a function can calculate significant differences in model coefficients between the surveys of comparison. All of those differences can be easily plotted and outputted as a table. For more detailed information on the methods and example use see Rohr, B., Silber, H., & Felderer, B. (2024). Comparing the Accuracy of Univariate, Bivariate, and Multivariate Estimates across Probability and Nonprobability Surveys with Population Benchmarks. Sociological Methodology <doi:10.1177/00811750241280963>.

This package provides functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) \doi10.1002/cem.2691. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) \doi10.1093/biomet/asm038 and Lee and Yoo (2014) \doi10.1111/anzs.12057. Then, the canonical correlation is finalized with the initially-reduced two sets of variables.

This package performs analysis of split-split plot experiments in both completely randomized and randomized complete block designs. With the results, you can obtain ANOVA, mean tests, and regression analysis (Este pacote faz a analise de experimentos em parcela subsubdivididas no delineamento inteiramente casualizado e delineamento em blocos casualizados. Com resultados e possà vel obter a ANOVA, testes de medias e análise de regressao) <https://www.expstat.com/pacotes-do-r>.

This package implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides statistical methods based on the SWDFT, and graphical tools to display the outputs.

Generate common data forms for complex data suitable for conversions and transmission by decomposition as paths or primitives. Paths are sequentially-linked records, primitives are basic atomic elements and both can model many forms and be grouped into hierarchical structures. The universal models SC0 (structural) and SC (labelled, relational) are composed of edges and can represent any hierarchical form. Specialist models PATH', ARC and TRI provide the most common intermediate forms used for converting from one form to another. The methods are inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and provide intermediate forms that relate spatial data structures to this mathematical construct.