This package provides functions for eleven procedures for determining the number of factors, including functions for parallel analysis and the minimum average partial test. There are also functions for conducting principal components analysis, principal axis factor analysis, maximum likelihood factor analysis, image factor analysis, and extension factor analysis, all of which can take raw data or correlation matrices as input and with options for conducting the analyses using Pearson correlations, Kendall correlations, Spearman correlations, gamma correlations, or polychoric correlations. Varimax rotation, promax rotation, and Procrustes rotations can be performed. Additional functions focus on the factorability of a correlation matrix, the congruences between factors from different datasets, the assessment of local independence, the assessment of factor solution complexity, internal consistency, and for correcting Pearson correlation coefficients for attenuation due to unreliability. Auerswald & Moshagen (2019, ISSN:1939-1463); Field, Miles, & Field (2012, ISBN:978-1-4462-0045-2); Mulaik (2010, ISBN:978-1-4200-9981-2); O'Connor (2000, <doi:10.3758/bf03200807>); O'Connor (2001, ISSN:0146-6216).

Small toolbox for data analyses in environmental chemistry and ecotoxicology. Provides, for example, calibration() to calculate calibration curves and corresponding limits of detection (LODs) and limits of quantification (LOQs) according to German DIN 32645 (2008). texture() makes it easy to estimate soil particle size distributions from hydrometer measurements (ASTM D422-63, 2007).

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

Allows to calculate the probabilities of occurrences of an event in a great number of repetitions of Bernoulli experiment, through the application of the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. Gives the possibility to show the results graphically and analytically, and to compare the results obtained by the application of the above theorems with those calculated by the direct application of the Binomial formula. Is basically useful for educational purposes.

This is the course package for the exercise portion of the "Ecological Data Collection and Processing" course.

This package provides a system to facilitate designing comparative (and non-comparative) experiments using the grammar of experimental designs <https://emitanaka.org/edibble-book/>. An experimental design is treated as an intermediate, mutable object that is built progressively by fundamental experimental components like units, treatments, and their relation. The system aids in experimental planning, management and workflow.

This package provides a lightweight implementation of functions and methods for fast and fully automatic time series modeling and forecasting using Echo State Networks (ESNs).

An RStudio addin for editing a data.frame or a tibble'. You can delete, add or update a data.frame without coding. You can get resultant data as a data.frame'. In the package, modularized shiny app codes are provided. These modules are intended for reuse across applications.

Runs the eDITH (environmental DNA Integrating Transport and Hydrology) model, which implements a mass balance of environmental DNA (eDNA) transport at a river network scale coupled with a species distribution model to obtain maps of species distribution. eDITH can work with both eDNA concentration (e.g., obtained via quantitative polymerase chain reaction) or metabarcoding (read count) data. Parameter estimation can be performed via Bayesian techniques (via the BayesianTools package) or optimization algorithms. An interface to the DHARMa package for posterior predictive checks is provided. See Carraro and Altermatt (2024) <doi:10.1111/2041-210X.14317> for a package introduction; Carraro et al. (2018) <doi:10.1073/pnas.1813843115> and Carraro et al. (2020) <doi:10.1038/s41467-020-17337-8> for methodological details.

Reproducibility assessment is essential in extracting reliable scientific insights from high-throughput experiments. While the Irreproducibility Discovery Rate (IDR) method has been instrumental in assessing reproducibility, its standard implementation is constrained to handling only two replicates. Package eCV introduces an enhanced Coefficient of Variation (eCV) metric to assess the likelihood of omic features being reproducible. Additionally, it offers alternatives to the Irreproducible Discovery Rate (IDR) calculations for multi-replicate experiments. These tools are valuable for analyzing high-throughput data in genomics and other omics fields. The methods implemented in eCV are described in Gonzalez-Reymundez et al., (2023) <doi:10.1101/2023.12.18.572208>.

Implementation of Energy Trees, a statistical model to perform classification and regression with structured and mixed-type data. The model has a similar structure to Conditional Trees, but brings in Energy Statistics to test independence between variables that are possibly structured and of different nature. Currently, the package covers functions and graphs as structured covariates. It builds upon partykit to provide functionalities for fitting, printing, plotting, and predicting with Energy Trees. Energy Trees are described in Giubilei et al. (2022) <arXiv:2207.04430>.

Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial error (SEM), spatial Durbin (SDM), spatial error Durbin (SDEM) and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.

Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.

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>.

Estimation of production functions by the Olley-Pakes, Levinsohn-Petrin and Wooldridge methodologies. The package aims to reproduce the results obtained with the Stata's user written opreg <http://www.stata-journal.com/article.html?article=st0145> and levpet <http://www.stata-journal.com/article.html?article=st0060> commands. The first was originally proposed by Olley, G.S. and Pakes, A. (1996) <doi:10.2307/2171831>. The second by Levinsohn, J. and Petrin, A. (2003) <doi:10.1111/1467-937X.00246>. And the third by Wooldridge (2009) <doi:10.1016/j.econlet.2009.04.026>.

This package provides a novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data from different perspectives. Janitza, S., Majumder, M., Mendolia, F., Jeske, S., & Kulmann, H. (2021) <doi:10.1007/s43441-021-00318-4>.

Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.

This package provides a data package containing a database of epidemiological parameters. It stores the data for the epiparameter R package. Epidemiological parameter estimates are extracted from the literature.

Tests the equality of two covariance matrices, used in paper "Two sample tests for high dimensional covariance matrices." Li and Chen (2012) <arXiv:1206.0917>.

Take the examples written in your documentation of functions and use them to create shells (skeletons which must be manually completed by the user) of test files to be tested with the testthat package. Sort of like python doctests for R.

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.

Enables simulation of water piping networks using EPANET'. The package provides functions from the EPANET programmer's toolkit as R functions so that basic or customized simulations can be carried out from R. The package uses EPANET version 2.2 from Open Water Analytics <https://github.com/OpenWaterAnalytics/EPANET/releases/tag/v2.2>.

This package implements the Polynomial Maximization Method ('PMM') for parameter estimation in linear and time series models when error distributions deviate from normality. The PMM2 variant achieves lower variance parameter estimates compared to ordinary least squares ('OLS') when errors exhibit significant skewness. Includes methods for linear regression, AR'/'MA'/'ARMA'/'ARIMA models, and bootstrap inference. Methodology described in Zabolotnii, Warsza, and Tkachenko (2018) <doi:10.1007/978-3-319-77179-3_75>, Zabolotnii, Tkachenko, and Warsza (2022) <doi:10.1007/978-3-031-03502-9_37>, and Zabolotnii, Tkachenko, and Warsza (2023) <doi:10.1007/978-3-031-25844-2_21>.

Extends the ergm.multi packages from the Statnet suite to fit (temporal) exponential-family random graph models for signed networks. The framework models positive and negative ties as interdependent, which allows estimation and testing of structural balance theory. The package also includes options for descriptive summaries, visualization, and simulation of signed networks. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2025) <doi:10.1017/pan.2024.21>.