Interactive laboratory of Time Series based in Box-Jenkins methodology.

This package provides functions to construct two-phase design layouts, compute treatment- and block-incidence matrices, derive C-matrices for residual, direct, and interaction effects, and calculate the efficiency factor for two-phase experimental designs with factorial treatment structure.

This package implements the TWO-Component Single Cell Model-Based Association Method (TWO-SIGMA) for gene-level differential expression (DE) analysis and DE-based gene set testing of single-cell RNA-sequencing datasets. See Van Buren et al. (2020) <doi:10.1002/gepi.22361> and Van Buren et al. (2021) <doi:10.1101/2021.01.24.427979>.

Analyse time to event data with two time scales by estimating a smooth hazard that varies over two time scales. If covariates are available, estimate a proportional hazards model with such a two-dimensional baseline hazard. Functions are provided to prepare the raw data for estimation, to fit the model and to plot the two-dimensional smooth hazard. Extension to a competing risks model is implemented. For details about the method please refer to Carollo et al. (2025) <doi:10.1002/sim.10297>.

This package provides a system for personalized exercise plan recommendations for T2D (Type 2 Diabetes) patients based on the primary outcome of HbA1c (Glycated Hemoglobin). You provide the individual's information, and T2DFitTailor details the exercise plan and predicts the intervention's effectiveness.

Instead of nesting function calls, annotate and transform functions using "#." comments.

This package provides a tidy workflow for generating, estimating, reporting, and plotting structural equation models using lavaan', OpenMx', or Mplus'. Throughout this workflow, elements of syntax, results, and graphs are represented as tidy data, making them easy to customize. Includes functionality to estimate latent class analyses, and to plot dagitty and igraph objects.

This package provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the DoseFinding package, which is used for the fitting of the models. See <doi:10.1080/01621459.2017.1281813> for details.

Feature selection algorithm that extracts features in highly correlated spaces. The extracted features are meant to be fed into simple explainable models such as linear or logistic regressions. The package is useful in the field of explainable modelling as a way to understand variable behavior.

The function TailClassifier() suggests one of the following types of tail for your discrete data: 1) Power decaying tail; 2) Sub-exponential decaying tail; and 3) Near-exponential decaying tail. The function also provides an estimate of the parameter for the classified-distribution as a reference.

Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates.

This package provides a plug-in for the text mining framework tm to support text mining in a distributed way. The package provides a convenient interface for handling distributed corpus objects based on distributed list objects.

Generating Tag and Word Clouds.

Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.

This package provides a collection of functions used to format taxonomic names in Markdown documents. Those functions work with data structured according to Alvarez and Luebert (2018) <doi:10.3897/bdj.6.e23635>.

Facilities to work with vector and raster data in efficient repeatable and systematic work flow. Missing functionality in existing packages is included here to allow extraction from raster data with simple features and Spatial types and to make extraction consistent and straightforward. Extract cell numbers from raster data and return the cells as a data frame rather than as lists of matrices or vectors. The functions here allow spatial data to be used without special handling for the format currently in use.

General framework to organize data, methods, and results used in reproducible scientific analyses. A TAF analysis consists of four scripts (data.R, model.R, output.R, report.R) that are run sequentially. Each script starts by reading files from a previous step and ends with writing out files for the next step. Convenience functions are provided to version control the required data and software, run analyses, clean residues from previous runs, manage files, manipulate tables, and produce figures. With a focus on stability and reproducible analyses, the TAF package comes with no dependencies. TAF forms a base layer for the icesTAF package and other scientific applications.

Multiple ways to bin numeric columns with a tidy output. Wraps a variety of existing binning methods into one function, and includes a new method for binning by equal value, which is useful for sales data. Provides a function to automatically summarize the properties of the binned columns.

Calculates the community four moments (mean, variance, skewness, and kurtosis) of a given trait based on the moments described in Wieczynski et al. (2019) <doi:10.1073/pnas.1813723116>. These functional metrics are extremely useful in characterizing the distribution of traits in a plant community. It also provides tidyverse-friendly wrappers to seamlessly calculate advanced functional diversity indices (e.g., FDis, Rao's Q) using fundiversity (Grenie et al. 2023 <doi:10.1111/ecog.06585>) and functional rarity indices using funrar (Grenie et al. 2017 <doi:10.1111/ddi.12629>). Evaluating these community-weighted moments and diversity metrics allows researchers to evaluate shifts in optimal phenotypes and understand ecological filtering with exactness.

This package implements simulated tests for the hypothesis that terminal digits are uniformly distributed (chi-squared goodness-of-fit) and the hypothesis that terminal digits are independent from preceding digits (several tests of independence for r x c contingency tables). Also, for a number of distributions, implements Monte Carlo simulations for type I errors and power for the test of independence.

This package provides a pure interface for the Telegram Bot API <http://core.telegram.org/bots/api>. In addition to the pure API implementation, it features a number of tools to make the development of Telegram bots with R easy and straightforward, providing an easy-to-use interface that takes some work off the programmer.

Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time. These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) <doi:10.1037/a0025814>. We thank Kaylee Litson, Patricia Berglund, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

This package provides a framework for the creation and use of Neural ordinary differential equations with the tensorflow and keras packages. The idea of Neural ordinary differential equations comes from Chen et al. (2018) <doi:10.48550/arXiv.1806.07366>, and presents a novel way of learning and solving differential systems.

Develop, evaluate, and score multiple choice examinations, psychological scales, questionnaires, and similar types of data involving sequences of choices among one or more sets of answers. This version of the package should be considered as brand new. Almost all of the functions have been changed, including their argument list. See the file NEWS.Rd in the Inst folder for more information. Using the package does not require any formal statistical knowledge beyond what would be provided by a first course in statistics in a social science department. There the user would encounter the concept of probability and how it is used to model data and make decisions, and would become familiar with basic mathematical and statistical notation. Most of the output is in graphical form.