This package provides R routine for the so called two-sample Cramer-Test. This nonparametric two-sample-test on equality of the underlying distributions can be applied to multivariate data as well as univariate data. It offers two possibilities to approximate the critical value both of which are included in this package.

Automatically builds 12 classification models from data. The package also returns 25 plots, 5 tables and a summary report.

Connect to the California Data Exchange Center (CDEC) Web Service <http://cdec.water.ca.gov/>. CDEC provides a centralized database to store, process, and exchange real-time hydrologic information gathered by various cooperators throughout California. The CDEC Web Service <http://cdec.water.ca.gov/dynamicapp/wsSensorData> provides a data download service for accessing historical records.

It aims to find significant pathways through network topology information. It has several advantages compared with current pathway enrichment tools. First, pathway node instead of single gene is taken as the basic unit when analysing networks to meet the fact that genes must be constructed into complexes to hold normal functions. Second, multiple network centrality measures are applied simultaneously to measure importance of nodes from different aspects to make a full view on the biological system. CePa extends standard pathway enrichment methods, which include both over-representation analysis procedure and gene-set analysis procedure. <doi:10.1093/bioinformatics/btt008>.

Account for uncertainty when working with ranks. Estimate standard errors consistently in linear regression with ranked variables. Construct confidence sets of various kinds for positions of populations in a ranking based on values of a certain feature and their estimation errors. Theory based on Mogstad, Romano, Shaikh, and Wilhelm (2023)<doi:10.1093/restud/rdad006> and Chetverikov and Wilhelm (2023) <doi:10.48550/arXiv.2310.15512>.

This package provides an interactive shiny web application for constructing, analyzing, and visualizing composite indices from multidimensional datasets. Users can upload or select indicator data, group variables into logical categories, apply normalization and weighting methods (such as equal or custom schemes), and compute aggregate composite indices. The shiny interface includes tools for exploring results through tables, plots, and data exports, making it useful for researchers, policymakers, and analysts interested in index-based evaluations.

Simulate one or many frequentist confidence clinical trials based on a specified set of parameters. From a two-arm, single-stage trial to a perpetually run Adaptive Platform Trial, this package offers vast flexibility to customize your trial and observe operational characterisitics over thousands of instances.

Geometric circle fitting with Levenberg-Marquardt (a, b, R), Levenberg-Marquardt reduced (a, b), Landau, Spath and Chernov-Lesort. Algebraic circle fitting with Taubin, Kasa, Pratt and Fitzgibbon-Pilu-Fisher. Geometric ellipse fitting with ellipse LMG (geometric parameters) and conic LMA (algebraic parameters). Algebraic ellipse fitting with Fitzgibbon-Pilu-Fisher and Taubin.

Under natural conditions, nest temperatures fluctuate daily around a mean value, whereas in captivity they are often held constant. The Constant Temperature Equivalent is designed to bridge the gap between the two by calculating a single temperature value for wild nests that corresponds with the amount of development that would occur in an incubator set to the same temperature. The theory and formulas behind this method were developed by Professor Author Georges and are implemented here as a single function.

This package provides a systematic biology tool was developed to identify cell infiltration via Individualized Cell-Cell interaction network. CITMIC first constructed a weighted cell interaction network through integrating Cell-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific sample, and then, it used a network propagation algorithm on the network to identify cell infiltration for the sample. Ultimately, cell infiltration in the patient dataset was obtained by normalizing the centrality scores of the cells.

This package provides a wrapper for the Clockify API <https://docs.clockify.me/>, making it possible to query, insert and update time keeping data.

Climate crop zoning based in minimum and maximum air temperature. The data used in the package are from TerraClimate dataset (<https://www.climatologylab.org/terraclimate.html>), but, it have been calibrated with automatic weather stations of National Meteorological Institute of Brazil. The climate crop zoning of this package can be run for all the Brazilian territory.

This package provides a cascade select widget for usage in Shiny applications. This is useful for selection of hierarchical choices (e.g. continent, country, city). It is taken from the JavaScript library PrimeReact'.

This package provides a simple way to write ".Rprofile" code in an R Markdown file and have it knit to the correct location for your operating system.

Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).

Read Condensed Cornell Ecology Program ('CEP') and legacy CANOCO files into R data frames.

Create simplex plots to visualize the similarity between single-cells and selected clusters in a 1-/2-/3-simplex space. Velocity information can be added as an additional layer. See Liu J, Wang Y et al (2023) <doi:10.1093/bioinformatics/btaf119> for more details.

This package implements the Cross-contribution Compensating Multiple standard Normalization (CCMN) method described in Redestig et al. (2009) Analytical Chemistry <doi:10.1021/ac901143w> and other normalization algorithms.

The compound growth rate indicates the percentage change of a specific variable over a defined period. It is calculated using non-linear models, particularly the exponential model. To estimate the compound growth rates, the growth model is first converted to semilog form and then analyzed using Ordinary Least Squares (OLS) regression. This package has been developed using concept of Shankar et al. (2022)<doi:10.3389/fsufs.2023.1208898>.

This package provides a lightweight data validation and testing toolkit for R. Its guiding philosophy is that adding code-based data checks to users existing workflow should be both quick and intuitive. The suite of functions included therefore mirror the common data checks many users already perform by hand or by eye. Additionally, the checkthat package is optimized to work within tidyverse data manipulation pipelines.

This package provides a dashboard supports the usage of cromwell'. Cromwell is a scientific workflow engine for command line users. This package utilizes cromwell REST APIs and provides these convenient functions: timing diagrams for running workflows, cromwell engine status, a tabular workflow list. For more information about cromwell', visit <http://cromwell.readthedocs.io>.

Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with MatchIt', WeightIt', MatchThem', twang', Matching', optmatch', CBPS', ebal', cem', sbw', and designmatch for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.

Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) <doi:10.32614/RJ-2011-015> (Song & Zhong 2020) <doi:10.1093/bioinformatics/btaa613>, k-median, k-segments, and multi-channel weighted k-means. Dynamic programming is used to minimize the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced when there are many clusters. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility, useful for peak calling on temporal, spatial, and spectral data.

This package provides a simple, fast algorithm to find the neighbors and similarities of users in user-based filtering systems, to break free from the complex computation of existing similarity formulas and the ability to solve big data.