Compute inbreeding coefficients using the method of Meuwissen and Luo (1992) <doi:10.1186/1297-9686-24-4-305>, and numerator relationship coefficients between individuals using the method of Van Vleck (2007) <https://pubmed.ncbi.nlm.nih.gov/18050089/>.

Run three dimensional functional principal component analysis and return the three dimensional functional principal component scores. The details of the method are explained in Lin et al.(2015) <doi:10.1371/journal.pone.0132945>.

Recent years have seen significant interest in neighborhood-based structural parameters that effectively represent the spatial characteristics of tree populations and forest communities, and possess strong applicability for guiding forestry practices. This package provides valuable information that enhances our understanding and analysis of the fine-scale spatial structure of tree populations and forest stands. Reference: Yan L, Tan W, Chai Z, et al (2019) <doi:10.13323/j.cnki.j.fafu(nat.sci.).2019.03.007>.

Transformations that allow obtaining a flat table from reports in text or Excel format that contain data in the form of pivot tables. They can be defined for a single report and applied to a set of reports.

Translates several CSV files with ontological terms and corresponding data into RDF triples. These RDF triples are stored in OWL and JSON-LD files, facilitating data accessibility, interoperability, and knowledge unification. The triples are also visualized in a graph saved as an SVG. The input CSVs must be formatted with a template from a public Google Sheet; see README or vignette for more information. This is a tool is used by the SDLE Research Center at Case Western Reserve University to create and visualize material science ontologies, and it includes example ontologies to demonstrate its capabilities. This work was supported by the U.S. Department of Energyâ s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Numbers E-EE0009353 and DE-EE0009347, Department of Energy (National Nuclear Security Administration) under Award Number DE-NA0004104 and Contract number B647887, and U.S. National Science Foundation Award under Award Number 2133576.

Create, visualize, and test fast-and-frugal decision trees (FFTs) using the algorithms and methods described by Phillips, Neth, Woike & Gaissmaier (2017), <doi:10.1017/S1930297500006239>. FFTs are simple and transparent decision trees for solving binary classification problems. FFTs can be preferable to more complex algorithms because they require very little information, are easy to understand and communicate, and are robust against overfitting.

This package provides a collection of tools to create, use and maintain modularized model code written in the modeling language GAMS (<https://www.gams.com/>). Out-of-the-box GAMS does not come with support for modularized model code. This package provides the tools necessary to convert a standard GAMS model to a modularized one by introducing a modularized code structure together with a naming convention which emulates local environments. In addition, this package provides tools to monitor the compliance of the model code with modular coding guidelines.

Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.

Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. For a single-cell dataset, the user can calculate the GTE value for individual features (such as genes), and then identify the highly batch-sensitive features. Removing these highly batch-sensitive features results in datasets with low batch effects.

Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.

This package provides a simple API for downloading and reading xml data directly from Lattes <http://lattes.cnpq.br/>.

This package provides a lightweight fork of gMCP with functions for graphical described multiple test procedures introduced in Bretz et al. (2009) <doi:10.1002/sim.3495> and Bretz et al. (2011) <doi:10.1002/bimj.201000239>. Implements a flexible function using ggplot2 to create multiplicity graph visualizations. Contains instructions of multiplicity graph and graphical testing for group sequential design, described in Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>, with necessary unit testing using testthat'.

Toolset to create perpendicular profile graphs and swath profiles. Method are based on coordinate rotation algorithm by Schaeben et al. (2024) <doi:10.1002/mma.9823>.

Método simples e eficiente de geolocalizar dados no Brasil. O pacote é baseado em conjuntos de dados espaciais abertos de endereços brasileiros, utilizando como fonte principal o Cadastro Nacional de Endereços para Fins Estatà sticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro de Geografia e Estatà stica (IBGE), órgão oficial de estatà sticas e geografia do Brasil. (A simple and efficient method for geolocating data in Brazil. The package is based on open spatial datasets of Brazilian addresses, primarily using the Cadastro Nacional de Endereços para Fins Estatà sticos (CNEFE), published by the Instituto Brasileiro de Geografia e Estatà stica (IBGE), Brazil's official statistics and geography agency.).

An intuitive interface to simulate superimposed (marked) point patterns with vectorized parameterization of random point pattern and distribution of marks. The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications.

This package provides a new take on the bar chart. Similar to a waffle style chart but instead of squares the layout resembles a brick wall.

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations errors association.

Given a landscape resistance surface, creates minimum planar graph (Fall et al. (2007) <doi:10.1007/s10021-007-9038-7>) and grains of connectivity (Galpern et al. (2012) <doi:10.1111/j.1365-294X.2012.05677.x>) models that can be used to calculate effective distances for landscape connectivity at multiple scales. Documentation is provided by several vignettes, and a paper (Chubaty, Galpern & Doctolero (2020) <doi:10.1111/2041-210X.13350>).

Stores small spatial datasets used to teach basic spatial analysis concepts. Datasets are based off of the GeoDa software workbook and data site <https://geodacenter.github.io/data-and-lab/> developed by Luc Anselin and team at the University of Chicago. Datasets are stored as sf objects.

Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters.

Access Google Cloud machine learning APIs for text and speech tasks. Use the Cloud Translation API for text detection and translation, the Natural Language API to analyze sentiment, entities, and syntax, the Cloud Speech API to transcribe audio to text, and the Cloud Text-to-Speech API to synthesize text into audio files.

Extensions to Freund and Schapire's AdaBoost algorithm, Y. Freund and R. Schapire (1997) <doi:10.1006/jcss.1997.1504> and Friedman's gradient boosting machine, J.H. Friedman (2001) <doi:10.1214/aos/1013203451>. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMART).

This package provides functions to calculate the best linear unbiased prediction of genotype-by-environment metrics: ecovalence, environmental variance, Finlay and Wilkinson regression and Lin and Binns superiority measure, based on a multi-environment genomic prediction model.

For plant physiologists, converts conductance (e.g. stomatal conductance) to different units: m/s, mol/m^2/s, and umol/m^2/s/Pa.