Saturation of ionic substances in urine is calculated based on sodium, potassium, calcium, magnesium, ammonia, chloride, phosphate, sulfate, oxalate, citrate, ph, and urate. This program is intended for research use, only. The code within is translated from EQUIL2 Visual Basic code based on Werness, et al (1985) "EQUIL2: a BASIC computer program for the calculation of urinary saturation" <doi:10.1016/s0022-5347(17)47703-2> to R. The Visual Basic code was kindly provided by Dr. John Lieske of the Mayo Clinic.

Includes R functions for the estimation of tumor clones percentages for both snp data and (whole) genome sequencing data. See Cheng, Y., Dai, J. Y., Paulson, T. G., Wang, X., Li, X., Reid, B. J., & Kooperberg, C. (2017). Quantification of multiple tumor clones using gene array and sequencing data. The Annals of Applied Statistics, 11(2), 967-991, <doi:10.1214/17-AOAS1026> for more details.

Chat with large language models from a range of providers including Claude <https://claude.ai>, OpenAI <https://chatgpt.com>, and more. Supports streaming, asynchronous calls, tool calling, and structured data extraction.

This is a utility for transforming Ecological Metadata Language ('EML') files into JSON-LD and back into EML. Doing so creates a list-based representation of EML in R, so that EML data can easily be manipulated using standard R tools. This makes this package an effective backend for other R'-based tools working with EML. By abstracting away the complexity of XML Schema, developers can build around native R list objects and not have to worry about satisfying many of the additional constraints of set by the schema (such as element ordering, which is handled automatically). Additionally, the JSON-LD representation enables the use of developer-friendly JSON parsing and serialization that may facilitate the use of EML in contexts outside of R, as well as the informatics-friendly serializations such as RDF and SPARQL queries.

This package provides measures to characterize the complexity of classification and regression problems based on aspects that quantify the linearity of the data, the presence of informative feature, the sparsity and dimensionality of the datasets. This package provides bug fixes, generalizations and implementations of many state of the art measures. The measures are described in the papers: Lorena et al. (2019) <doi:10.1145/3347711> and Lorena et al. (2018) <doi:10.1007/s10994-017-5681-1>.

Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.

Produce maximum likelihood estimates of common accuracy statistics for multiple measurement methods when a gold standard is not available. An R implementation of the expectation maximization algorithms described in Zhou et al. (2011) <doi:10.1002/9780470906514> with additional functions for creating simulated data and visualizing results. Supports binary, ordinal, and continuous measurement methods.

Set of functions to keep track and find objects in user-defined environments by identifying environments by name --which cannot be retrieved with the built-in function environmentName(). The package also provides functionality to obtain simplified information about function calling chains and to get an object's memory address.

This package provides simple functions to create constraints for small test assembly problems (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, GLPK', lpSolve', Symphony', and Gurobi are supported as solvers. The gurobi package is not available from any mainstream repository; see <https://www.gurobi.com/downloads/>.

Evaluates the performance of binary classifiers. Computes confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR, accuracy, F1, DOR, ..), and area under the curve. Outputs are well suited for nested dataframes.

This package provides a collection of curated educational datasets for teaching ecology and agriculture concepts. Includes data on wildlife monitoring, plant treatments, and ecological observations with documentation and examples for educational use. All datasets are derived from published scientific studies and are available under CC0 or compatible licenses.

This package provides unsupervised selection and clustering of microarray data using mixture models. Following the methods described in McLachlan, Bean and Peel (2002) <doi:10.1093/bioinformatics/18.3.413> a subset of genes are selected based one the likelihood ratio statistic for the test of one versus two components when fitting mixtures of t-distributions to the expression data for each gene. The dimensionality of this gene subset is further reduced through the use of mixtures of factor analyzers, allowing the tissue samples to be clustered by fitting mixtures of normal distributions.

Collection of functions related to benchmark with prediction models for data analysis and editing of clinical and epidemiological data.

This package provides a small group of functions to read in a data dictionary and the corresponding data table from Excel and to automate the cleaning, re-coding and creation of simple calculated variables. This package was designed to be a companion to the macro-enabled Excel template available on the GitHub site, but works with any similarly-formatted Excel data.

We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points. For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula. The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration. All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data <arXiv:2009.06699>.

Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls. For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.

This cointegration based Time Delay Neural Network Model hybrid model allows the researcher to make use of the information extracted by the cointegrating vector as an input in the neural network model.

Application of Ensemble Empirical Mode Decomposition and its variant based Support Vector regression model for univariate time series forecasting. For method details see Das (2020).<http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

This package provides functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) <doi:10.1016/j.anbehav.2011.07.016> for an introduction.

This package provides a flexible tool for enrichment analysis based on user-defined sets. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. EnrichIntersect also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model (Zhao et al., 2022 <doi:10.1016/j.isci.2022.104767>) or similar methods.

An interface for performing climate matching using the Euclidean "Climatch" algorithm. Functions provide a vector of climatch scores (0-10) for each location (i.e., grid cell) within the recipient region, the percent of climatch scores >= a threshold value, and mean climatch score. Tools for parallelization and visualizations are also provided. Note that the floor function that rounds the climatch score down to the nearest integer has been removed in this implementation and the â Climatchâ algorithm, also referred to as the â Climateâ algorithm, is described in: Crombie, J., Brown, L., Lizzio, J., & Hood, G. (2008). â Climatch user manualâ . The method for the percent score is described in: Howeth, J.G., Gantz, C.A., Angermeier, P.L., Frimpong, E.A., Hoff, M.H., Keller, R.P., Mandrak, N.E., Marchetti, M.P., Olden, J.D., Romagosa, C.M., and Lodge, D.M. (2016). <doi:10.1111/ddi.12391>.

Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis. Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book. Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included. Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard. Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc.

An implementation for using efficient initials to compute the maximal eigenpair in R. It provides three algorithms to find the efficient initials under two cases: the tridiagonal matrix case and the general matrix case. Besides, it also provides two algorithms for the next to the maximal eigenpair under these two cases.

Exploitation, processing and 2D-3D visualization of DICOM-RT files (structures, dosimetry, imagery) for medical physics and clinical research, in a patient-oriented perspective.