Statistical tests for the comparison between two correlations based on either independent or dependent groups. Dependent correlations can either be overlapping or nonoverlapping. A web interface is available on the website <http://comparingcorrelations.org>. A plugin for the R GUI and IDE RKWard is included. Please install RKWard from <https://rkward.kde.org> to use this feature. The respective R package rkward cannot be installed directly from a repository, as it is a part of RKWard.

Decomposing value added growth into explanatory factors. A cost constrained value added function is defined to specify the production frontier. Industry estimates can also be aggregated using a weighted average approach. Details about the methodology and data can be found in Diewert and Fox (2018) <doi:10.1093/oxfordhb/9780190226718.013.19> and Zeng, Parsons, Diewert and Fox (2018) <https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/Documents/emg2018-6_SZeng_EMG-Slides.pdf>.

An implementation of sequential testing that uses evidence ratios computed from the weights of a set of models. These weights correspond either to Akaike weights computed from the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) and following Burnham & Anderson (2004, <doi:10.1177/0049124104268644>) recommendations, or to pseudo-BMA weights computed from the WAIC or the LOO-IC of models fitted with brms and following Yao et al. (2017, <arXiv:1704.02030v3>).

This package contains data on Post-Secondary Institution Statistics in 2020 <https://nces.ed.gov/ipeds/use-the-data>. The package allows easy access to a wide variety of information regarding Post-secondary Institutions, its students, faculty, and their demographics, financial aid, educational and recreational offerings, and completions. This package can be used by students, college counselors, or involved parents interested in pursuing higher education, considering their options, and securing admission into their school of choice.

The implemented methods reach out to scientists that seek to estimate multiplicity of infection (MOI) and lineage (allele) frequencies and prevalences at molecular markers using the maximum-likelihood method described in Schneider (2018) <doi:10.1371/journal.pone.0194148>, and Schneider and Escalante (2014) <doi:10.1371/journal.pone.0097899>. Users can import data from Excel files in various formats, and perform maximum-likelihood estimation on the imported data by the package's moimle() function.

Surface topography calculations of Dirichlet's normal energy, relief index, surface slope, and orientation patch count for teeth using scans of enamel caps. Importantly, for the relief index and orientation patch count calculations to work, the scanned tooth files must be oriented with the occlusal plane parallel to the x and y axes, and perpendicular to the z axis. The files should also be simplified, and smoothed in some other software prior to uploading into R.

Optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) <doi:10.1016/S0169-2607(03)00073-7>.

Check available classification and regression data sets from the PMLB repository and download them. The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms. These data sets cover a range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features. There are currently over 150 datasets included in the PMLB repository.

This package provides several functions for area level of small area estimation using hierarchical Bayesian (HB) methods with several univariate distributions for variables of interest. The dataset that is used in every function is generated accordingly in the Example. The rjags package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

This package provides functions to estimate, predict and interpolate areal data. For estimation and prediction we assume areal data is an average of an underlying continuous spatial process as in Moraga et al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020) <doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020) <doi:10.1093/biostatistics/kxy041>. The interpolation methodology is (mostly) based on Goodchild and Lam (1980, ISSN:01652273).

Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes.

TRIP is a software framework that provides analytics services on antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor, TR) gene sequence data. It is a web application written in R Shiny. It takes as input the output files of the IMGT/HighV-Quest tool. Users can select to analyze the data from each of the input samples separately, or the combined data files from all samples and visualize the results accordingly.

The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching.

This package extracts tandem mass spectrometry (MS/MS) ID data from mzIdentML (leveraging the mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. It also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc.

Pdist computes the euclidean distance between rows of a matrix X and rows of another matrix Y. Previously, this could be done by binding the two matrices together and calling dist, but this creates unnecessary computation by computing the distances between a row of X and another row of X, and likewise for Y. Pdist strictly computes distances across the two matrices, not within the same matrix, making computations significantly faster for certain use cases.

This package contains functions for the MCMC simulation of dyadic network models j2 (Zijlstra, 2017, <doi:10.1080/0022250X.2017.1387858>) and p2 (Van Duijn, Snijders & Zijlstra, 2004, <doi: 10.1046/j.0039-0402.2003.00258.x>), the multilevel p2 model (Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>), and the bidirectional (multilevel) counterpart of the the multilevel p2 model as described in Zijlstra, Van Duijn & Snijders (2009) <doi: 10.1348/000711007X255336>, the (multilevel) b2 model.

Joint dimension reduction and spatial clustering is conducted for Single-cell RNA sequencing and spatial transcriptomics data, and more details can be referred to Wei Liu, Xu Liao, Yi Yang, Huazhen Lin, Joe Yeong, Xiang Zhou, Xingjie Shi and Jin Liu. (2022) <doi:10.1093/nar/gkac219>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.

Efficient procedures for fitting the DD-PCA (Ke et al., 2019, <arXiv:1906.00051>) by decomposing a large covariance matrix into a low-rank matrix plus a diagonally dominant matrix. The implementation of DD-PCA includes the convex approach using the Alternating Direction Method of Multipliers (ADMM) and the non-convex approach using the iterative projection algorithm. Applications of DD-PCA to large covariance matrix estimation and global multiple testing are also included in this package.

Simulates cyclic voltammetry, linear-sweep voltammetry (both with and without stirring of the solution), and single-pulse and double-pulse chronoamperometry and chronocoulometry experiments using the implicit finite difference method outlined in Gosser (1993, ISBN: 9781560810261) and in Brown (2015) <doi:10.1021/acs.jchemed.5b00225>. Additional functions provide ways to display and to examine the results of these simulations. The primary purpose of this package is to provide tools for use in courses in analytical chemistry.

This package provides a very flexible framework for building server side logic in R. The framework is unopinionated when it comes to how HTTP requests and WebSocket messages are handled and supports all levels of app complexity; from serving static content to full-blown dynamic web-apps. Fiery does not hold your hand as much as e.g. the shiny package does, but instead sets you free to create your web app the way you want.

This package provides a novel forward stepwise discriminant analysis framework that integrates Pillai's trace with Uncorrelated Linear Discriminant Analysis (ULDA), providing an improvement over traditional stepwise LDA methods that rely on Wilks Lambda. A stand-alone ULDA implementation is also provided, offering a more general solution than the one available in the MASS package. It automatically handles missing values and provides visualization tools. For more details, see Wang (2024) <doi:10.48550/arXiv.2409.03136>.

Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm. A paper describing all these methods will be submitted soon. The reference will be added to this file as soon as available.

This package provides implementation of various correlation coefficients of common use in Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008) <doi:10.1145/1390334.1390435> tauAP correlation coefficient, and versions tauAP_a and tauAP_b developed by Urbano and Marrero (2017) <doi:10.1145/3121050.3121106> to cope with ties.

These functions take a gene expression value matrix, a primary covariate vector, an additional known covariates matrix. A two stage analysis is applied to counter the effects of latent variables on the rankings of hypotheses. The estimation and adjustment of latent effects are proposed by Sun, Zhang and Owen (2011). "leapp" is developed in the context of microarray experiments, but may be used as a general tool for high throughput data sets where dependence may be involved.