This package provides functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) <doi:10.5194/gmd-5-1045-2012> and Sierra and others (2014) <doi:10.5194/gmd-7-1919-2014>.

This package creates images that are the proper size for social media. Beautiful plots, charts and graphs wither and die if they are not shared. Social media is perfect for this but every platform has its own image dimensions. With smpic you can easily save your plots with the exact dimensions needed for the different platforms.

The spork syntax describes label formatting concisely, supporting mixed nesting of subscripts and superscripts to arbitrary depth. It intends to be easy to read and write in plain text, and easy to convert to equivalent presentations in plotmath', latex', and html'. Greek symbols and a multiplication symbol are explicitly supported. See ?as_spork and ?as_previews.

This package creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.

GNU Rot[t]log is a program for managing log files. It is used to automatically rotate out log files when they have reached a given size or according to a given schedule. It can also be used to automatically compress and archive such logs. Rot[t]log will mail reports of its activity to the system administrator.

This package provides functions to perform reproducible parallel foreach loops, using independent random streams as generated by L'Ecuyer's combined multiple-recursive generator. It enables to easily convert standard %dopar% loops into fully reproducible loops, independently of the number of workers, the task scheduling strategy, or the chosen parallel environment and associated foreach backend.

Machine Learning models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance, but such black-box models usually lack interpretability. The DALEX package contains various explainers that help to understand the link between input variables and model output.

This package provides tools for making the descriptive "Table 1" used in medical articles, a transition plot for showing changes between categories (also known as a Sankey diagram), flow charts by extending the grid package, a method for variable selection based on the SVD, Bezier lines with arrows complementing the ones in the grid package, and more.

An interface to the ArcGIS arcpy and arcgis python API <https://pro.arcgis.com/en/pro-app/latest/arcpy/get-started/arcgis-api-for-python.htm>. Provides various tools for installing and configuring a Conda environment for accessing ArcGIS geoprocessing functions. Helper functions for manipulating and converting ArcGIS objects from R are also provided.

This package provides functions to perform statistical inference in the balanced one-way ANOVA model with a random factor: confidence intervals, prediction interval, and Weerahandi generalized pivotal quantities. References: Burdick & Graybill (1992, ISBN-13: 978-0824786441); Weerahandi (1995) <doi:10.1007/978-1-4612-0825-9>; Lin & Liao (2008) <doi:10.1016/j.jspi.2008.01.001>.

Simultaneously clusters the Periodontal diseases (PD) patients and their tooth sites after taking the patient- and site-level covariates into consideration. BAREB uses the determinantal point process (DPP) prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Essentially, BAREB is a cluster-wise linear model based on Yuliang (2020) <doi:10.1002/sim.8536>.

Use frequentist and Bayesian methods to estimate parameters from a binary outcome misclassification model. These methods correct for the problem of "label switching" by assuming that the sum of outcome sensitivity and specificity is at least 1. A description of the analysis methods is available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>.

Encryption wrappers, using low-level support from sodium and openssl'. cyphr tries to smooth over some pain points when using encryption within applications and data analysis by wrapping around differences in function names and arguments in different encryption providing packages. It also provides high-level wrappers for input/output functions for seamlessly adding encryption to existing analyses.

Collection of indices and tools relating to cardiovascular, nephrology, and hepatic research that aid epidemiological chort or retrospective chart review with big data. All indices and tools take commonly used lab values and patient demographics and measurements to compute various risk and predictive values for survival. References to original literature and validation contained in each function documentation.

Use numerical optimization to fit ordinary differential equations (ODEs) to time series data to examine the dynamic relationships between variables or the characteristics of a dynamical system. It can now be used to estimate the parameters of ODEs up to second order, and can also apply to multilevel systems. See <https://github.com/yueqinhu/defit> for details.

Provee una versión traducida de los siguientes conjuntos de datos: airlines', airports', AwardsManagers', babynames', Batting', credit_data', diamonds', faithful', fueleconomy', Fielding', flights', gapminder', gss_cat', iris', Managers', mpg', mtcars', atmos', palmerpenguins', People, Pitching', planes', presidential', table1', table2', table3', table4a', table4b', table5', vehicles', weather', who'. English: It provides a Spanish translated version of the datasets listed above.

This package provides several validator functions for checking if arguments passed by users have valid types, lengths, etc. and for generating informative and well-formatted error messages in a consistent style. Also provides tools for users to create their own validator functions. The error message style used is adopted from <https://style.tidyverse.org/error-messages.html>.

Weighted-L2 FPOP Maidstone et al. (2017) <doi:10.1007/s11222-016-9636-3> and pDPA/FPSN Rigaill (2010) <arXiv:1004.0887> algorithm for detecting multiple changepoints in the mean of a vector. Also includes a few model selection functions using Lebarbier (2005) <doi:10.1016/j.sigpro.2004.11.012> and the capsushe package.

Use Rmarkdown First method to build your package. Start your package with documentation, functions, examples and tests in the same unique file. Everything can be set from the Rmarkdown template file provided in your project, then inflated as a package. Inflating the template copies the relevant chunks and sections in the appropriate files required for package development.

This package provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the fable framework provided by the fabletools package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.

Implementations of the algorithms present article Generalized Spatial-Time Sequence Miner, original title (Castro, Antonio; Borges, Heraldo ; Pacitti, Esther ; Porto, Fabio ; Coutinho, Rafaelli ; Ogasawara, Eduardo . Generalização de Mineração de Sequências Restritas no Espaço e no Tempo. In: XXXVI SBBD - Simpósio Brasileiro de Banco de Dados, 2021 <doi:10.5753/sbbd.2021.17891>).

Functions, data sets, analyses and examples from the book A Handbook of Statistical Analyses Using R (Brian S. Everitt and Torsten Hothorn, Chapman & Hall/CRC, 2006). The first chapter of the book, which is entitled An Introduction to R'', is completely included in this package, for all other chapters, a vignette containing all data analyses is available.

This package implements an S4 distribution system and estimation methods for parameters of common distribution families. The common d, p, q, r function family for each distribution is enriched with the ll, e, and v counterparts, computing the log-likelihood, performing estimation, and calculating the asymptotic variance - covariance matrix, respectively. Parameter estimation is performed analytically whenever possible.

Auxiliary package for better/faster analytics, visualization, data mining, and machine learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling, Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust results, without the need of repetitive coding or advanced R programming skills.