As a chronomètre is a stopwatch', this package offers a simple stopwatch, and in particular one that can be shared with Python (using the corresponding package of the same name available via PyPi') such that both interpreters operate on the same object instance and shown in the demo file, as well as in the unit tests.

Thematic quality indices are provided to facilitate the evaluation and quality control of geospatial data products (e.g. thematic maps, remote sensing classifications, etc.). The indices offered are based on the so-called confusion matrix. This matrix is constructed by comparing the assigned classes or attributes of a set of pairs of positions or objects in the product and the ground truth. In this package it is considered that the classes of the ground truth correspond to the columns and that the classes of the product to be valued correspond to the rows. The package offers two object classes with their methods: ConfMatrix (Confusion matrix) and QCCS (Quality Control Columns Set). The ConfMatrix class of objects offers more than 20 methods based on the confusion matrix. The QCCS class of objects offers a different perspective in which the ground truth is considered to allow the values of the column marginals to be fixed, see Ariza López et al. (2019) <doi:10.3390/app9204240> and Canran Liu et al. (2007) <doi:10.1016/j.rse.2006.10.010> for more details. The package was created with R6'.

This package provides a utility to quickly obtain clean and tidy college football data. Serves as a wrapper around the <https://collegefootballdata.com/> API and provides functions to access live play by play and box score data from ESPN <https://www.espn.com> when available. It provides users the capability to access a plethora of endpoints, and supplement that data with additional information (Expected Points Added/Win Probability added).

Gene Symbols or Ensembl Gene IDs are converted using the Bimap interface in AnnotationDbi in convertId2() but that function is only provided as fallback mechanism for the most common use cases in data analysis. The main function in the package is convert.bm() which queries BioMart using the full capacity of the API provided through the biomaRt package. Presets and defaults are provided for convenience but all "marts", "filters" and "attributes" can be set by the user. Function convert.alias() converts Gene Symbols to Aliases and vice versa and function likely_symbol() attempts to determine the most likely current Gene Symbol.

Analyzes data from a Conconi et al. (1996) <doi:10.1055/s-2007-972887> treadmill fitness test where speed is augmented by a constant amount every set number of seconds to estimate the anaerobic (lactate) threshold speed and heart rate. It reads a TCX file, allows optional removal observations from before and after the actual test, fits a change-point linear model where the change-point is the estimate of the lactate threshold, and plots the data points and fit model. Details of administering the fitness test are provided in the package vignette. Functions work by default for Garmin Connect TCX exports but may require additional data preparation for heart rate, time, and speed data from other sources.

Estimation of crop water demand can be processed via this package. As example, the data from TerraClimate dataset (<https://www.climatologylab.org/terraclimate.html>) calibrated with automatic weather stations of National Meteorological Institute of Brazil is available in a coarse spatial resolution to do the crop water demand. However, the user have also the option to download the variables directly from TerraClimate repository with the download.terraclimate function and access the original TerraClimate products. If the user believes that is necessary calibrate the variables, there is another function to do it. Lastly, the estimation of the crop water demand present in this package can be run for all the Brazilian territory with TerraClimate dataset.

Extend cxxfunction by saving the dynamic shared objects for reusing across R sessions.

Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods; see "Contrast trees and distribution boosting", Jerome H. Friedman (2020) <doi:10.1073/pnas.1921562117>. In situations where inaccuracies are detected, boosted contrast trees can often improve performance. Functions are provided to to build such trees in addition to a special case, distribution boosting, an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.

This package implements lasso and ridge regression for dichotomised outcomes (<doi:10.1080/02664763.2023.2233057>), i.e., numerical outcomes that were transformed to binary outcomes. Such artificial binary outcomes indicate whether an underlying measurement is greater than a threshold.

Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models.

This package provides tools for interacting with the Circle CI API (<https://circleci.com/docs/api/v2/>). Besides executing common tasks such as querying build logs and restarting builds, this package also helps setting up permissions to deploy from builds.

This package provides a shiny app to discover cocktails. The app allows one to search for cocktails by ingredient, filter on rating, and number of ingredients. The package also contains data with the ingredients of nearly 26 thousand cocktails scraped from the web.

This package implements Dirichlet multinomial modeling of relative abundance data using functionality provided by the Stan software. The purpose of this package is to provide a user friendly way to interface with Stan that is suitable for those new to modeling. For more regarding the modeling mathematics and computational techniques we use see our publication in Molecular Ecology Resources titled Dirichlet multinomial modeling outperforms alternatives for analysis of ecological count data (Harrison et al. 2020 <doi:10.1111/1755-0998.13128>).

This package implements a wide range of dose escalation designs. The focus is on model-based designs, ranging from classical and modern continual reassessment methods (CRMs) based on dose-limiting toxicity endpoints to dual-endpoint designs taking into account a biomarker/efficacy outcome. Bayesian inference is performed via MCMC sampling in JAGS, and it is easy to setup a new design with custom JAGS code. However, it is also possible to implement 3+3 designs for comparison or models with non-Bayesian estimation. The whole package is written in a modular form in the S4 class system, making it very flexible for adaptation to new models, escalation or stopping rules. Further details are presented in Sabanés Bové et al. (2019) <doi:10.18637/jss.v089.i10>.

Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins statistic is implemented. See Hopkins and Skellam (1954) <doi:10.1093/oxfordjournals.aob.a083391>.

Cochran-Mantel-Haenszel methods (Cochran (1954) <doi:10.2307/3001616>; Mantel and Haenszel (1959) <doi:10.1093/jnci/22.4.719>; Landis et al. (1978) <doi:10.2307/1402373>) are a suite of tests applicable to categorical data. A competitor to those tests is the procedure of Nonparametric ANOVA which was initially introduced in Rayner and Best (2013) <doi:10.1111/anzs.12041>. The methodology was then extended in Rayner et al. (2015) <doi:10.1111/anzs.12113>. This package employs functions related to both methodologies and serves as an accompaniment to the book: An Introduction to Cochranâ Mantelâ Haenszel and Non-Parametric ANOVA. The package also contains the data sets used in that text.

Computing comorbidity indices and scores such as the weighted Charlson score (Charlson, 1987 <doi:10.1016/0021-9681(87)90171-8>) and the Elixhauser comorbidity score (Elixhauser, 1998 <doi:10.1097/00005650-199801000-00004>) using ICD-9-CM or ICD-10 codes (Quan, 2005 <doi:10.1097/01.mlr.0000182534.19832.83>). Australian and Swedish modifications of the Charlson Comorbidity Index are available as well (Sundararajan, 2004 <doi:10.1016/j.jclinepi.2004.03.012> and Ludvigsson, 2021 <doi:10.2147/CLEP.S282475>), together with different weighting algorithms for both the Charlson and Elixhauser comorbidity scores.

After using this, a publication-ready correlation table with p-values indicated will be created. The input can be a full data frame; any string and Boolean terms will be dropped as part of functionality. Correlations and p-values are calculated using the Hmisc framework. Output of the correlation_matrix() function is a table of strings; this gets saved out to a .csv2 with the save_correlation_matrix() function for easy insertion into a paper. For more details about the process, consult <https://paulvanderlaken.com/2020/07/28/publication-ready-correlation-matrix-significance-r/>.

Analyze data from a crossover design using generalized estimation equations (GEE), including carryover effects and various correlation structures based on the Kronecker product. It contains functions for semiparametric estimates of carry-over effects in repeated measures and allows estimation of complex carry-over effects. Related work includes: a) Cruz N.A., Melo O.O., Martinez C.A. (2023). "CrossCarry: An R package for the analysis of data from a crossover design with GEE". <doi:10.48550/arXiv.2304.02440>. b) Cruz N.A., Melo O.O., Martinez C.A. (2023). "A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures". <doi:10.1007/s00362-022-01391-z> and c) Cruz N.A., Melo O.O., Martinez C.A. (2023). "Semiparametric generalized estimating equations for repeated measurements in cross-over designs". <doi:10.1177/09622802231158736>.

This package provides a helpful R6 class and methods for interacting with the Posit Connect Server API along with some meaningful utility functions for regular tasks. API documentation varies by Posit Connect installation and version, but the latest documentation is also hosted publicly at <https://docs.posit.co/connect/api/>.

The concaveman function ports the concaveman (<https://github.com/mapbox/concaveman>) library from mapbox'. It computes the concave polygon(s) for one or several set of points.

Develop Nonlinear Mixed Effects (NLME) models for pharmacometrics using a shiny interface. The Pharmacometric Modeling Language (PML) code updates in real time given changes to user inputs. Models can be executed using the Certara.RsNLME package. Additional support to generate the underlying Certara.RsNLME code to recreate the corresponding model in R is provided in the user interface.

This package provides functions to generate ensembles of generalized linear models using competing proximal gradients. The optimal sparsity and diversity tuning parameters are selected via an alternating grid search.

Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision).