This package provides various tools for preprocessing Emission-Excitation-Matrix (EEM) for Parallel Factor Analysis (PARAFAC). Different methods are also provided to calculate common metrics such as humification index and fluorescence index.

Builds contingency tables that cross-tabulate multiple categorical variables and also calculates various summary measures. Export to a variety of formats is supported, including: HTML', LaTeX', and Excel'.

Ensemble correlation-based low-rank matrix completion method (ECLRMC) is an extension to the LRMC based methods. Traditionally, the LRMC based methods give identical importance to the whole data which results in emphasizing on the commonality of the data and overlooking the subtle but crucial differences. This method aims to overcome the equality assumption problem that exists in the current LRMS based methods. Ensemble correlation-based low-rank matrix completion (ECLRMC) takes consideration of the specific characteristic of each sample and performs LRMC on the set of samples with a strong correlation. It uses an ensemble learning method to improve the imputation performance. Since each sample is analyzed independently this method can be parallelized by distributing imputation across many computation units or GPU platforms. This package provides three different methods (LRMC, CLRMC and ECLRMC) for data imputation. There is also an NRMS function for evaluating the result. Chen, Xiaobo, et al (2017) <doi:10.1016/j.knosys.2017.06.010>.

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.

Models integrate environmental DNA (eDNA) detection data and traditional survey data to jointly estimate species catch rate (see package vignette: <https://ednajoint.netlify.app/>). Models can be used with count data via traditional survey methods (i.e., trapping, electrofishing, visual) and replicated eDNA detection/nondetection data via polymerase chain reaction (i.e., PCR or qPCR) from multiple survey locations. Estimated parameters include probability of a false positive eDNA detection, a site-level covariates that scale the sensitivity of eDNA surveys relative to traditional surveys, and gear scaling coefficients for traditional gear types. Models are implemented with a Bayesian framework (Markov chain Monte Carlo) using the Stan probabilistic programming language.

Equating of multiple forms using Item Response Theory (IRT) methods (Battauz M. (2017) <doi:10.1007/s11336-016-9517-x>, Battauz and Leoncio (2023) <doi:10.1177/01466216231151702>, Haberman S. J. (2009) <doi:10.1002/j.2333-8504.2009.tb02197.x>).

Evaluate a function over a data frame of expressions.

Calculates the (approximate) effective number of clusters for a regression model, as described in Carter, Schnepel, and Steigerwald (2017) <doi:10.1162/REST_a_00639>. The effective number of clusters is a statistic to assess the reliability of asymptotic inference when sampling or treatment assignment is clustered. Methods are implemented for stats::lm(), plm::plm(), and fixest::feols(). There is also a formula method.

This package provides tools for modelling electric vehicle charging sessions into generic groups with similar connection patterns called "user profiles", using Gaussian Mixture Models clustering. The clustering and profiling methodology is described in Cañigueral and Meléndez (2021, ISBN:0142-0615) <doi:10.1016/j.ijepes.2021.107195>.

Access data related to the European union from GISCO <https://ec.europa.eu/eurostat/web/gisco>, the Geographic Information System of the European Commission, via its rest API at <https://gisco-services.ec.europa.eu>. This package tries to make it easier to get these data into R.

Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.

Computes and plots a transformed empirical CDF (ecdf) as a diagnostic for heavy tailed data, specifically data with power law decay on the tails. Routines for annotating the plot, comparing data to a model, fitting a nonparametric model, and some multivariate extensions are given.

This package creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.

Infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.

It allows structuring electoral data of different size and structure to calculate various indicators frequently used in the studies of electoral systems and party systems. Indicators of electoral volatility, electoral disproportionality, party nationalization and the effective number of parties are included.

Using variational techniques we address some epidemiological problems as the incidence curve decomposition by inverting the renewal equation as described in Alvarez et al. (2021) <doi:10.1073/pnas.2105112118> and Alvarez et al. (2022) <doi:10.3390/biology11040540> or the estimation of the functional relationship between epidemiological indicators. We also propose a learning method for the short time forecast of the trend incidence curve as described in Morel et al. (2022) <doi:10.1101/2022.11.05.22281904>.

This package provides functions for analysis of rate changes in sequential events.

There is no ophthalmic researcher who has not had headaches from the handling of visual acuity entries. Different notations, untidy entries. This shall now be a matter of the past. Eye makes it as easy as pie to work with VA data - easy cleaning, easy conversion between Snellen, logMAR, ETDRS letters, and qualitative visual acuity shall never pester you again. The eye package automates the pesky task to count number of patients and eyes, and can help to clean data with easy re-coding for right and left eyes. It also contains functions to help reshaping eye side specific variables between wide and long format. Visual acuity conversion is based on Schulze-Bonsel et al. (2006) <doi:10.1167/iovs.05-0981>, Gregori et al. (2010) <doi:10.1097/iae.0b013e3181d87e04>, Beck et al. (2003) <doi:10.1016/s0002-9394(02)01825-1> and Bach (2007) <https://michaelbach.de/sci/acuity.html>.

Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.

This package provides a tool to operate a batch of univariate or multivariate Cox models and return tidy result.

Bayesian Model Averaging to create probabilistic forecasts from ensemble forecasts and weather observations <https://stat.uw.edu/sites/default/files/files/reports/2007/tr516.pdf>.

This package implements Excel functions in R for your calculation simplicity.You can use most of the aggregate functions, addressing functions,logical functions and text functions. Helps you a ton in learning how R works as some Excel users might be struggling with the program.

This package provides data sets and R Codes for E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.

Integrates methods for epidemiological analysis, modeling, and visualization, including functions for summary statistics, SIR (Susceptible-Infectious-Recovered) modeling, DALY (Disability-Adjusted Life Years) estimation, age standardization, diagnostic test evaluation, NLP (Natural Language Processing) keyword extraction, clinical trial power analysis, survival analysis, SNP (Single Nucleotide Polymorphism) association, and machine learning methods such as logistic regression, k-means clustering, Random Forest, and Support Vector Machine (SVM). Includes datasets for prevalence estimation, SIR modeling, genomic analysis, clinical trials, DALY, diagnostic tests, and survival analysis. Methods are based on Gelman et al. (2013) <doi:10.1201/b16018> and Wickham et al. (2019, ISBN:9781492052040>.