Life Table Response Experiments (LTREs) are a method of comparative demographic analysis. The purpose is to quantify how the difference or variance in vital rates (stage-specific survival, growth, and fertility) among populations contributes to difference or variance in the population growth rate, "lambda." We provide functions for one-way fixed design and random design LTRE, using either the classical methods that have been in use for several decades, or an fANOVA-based exact method that directly calculates the impact on lambda of changes in matrix elements, for matrix elements and their interactions. The equations and descriptions for the classical methods of LTRE analysis can be found in Caswell (2001, ISBN: 0878930965), and the fANOVA-based exact methods are described in Hernandez et al. (2023) <doi:10.1111/2041-210X.14065>. We also provide some demographic functions, including generation time from Bienvenu and Legendre (2015) <doi:10.1086/681104>. For implementation of exactLTRE where all possible interactions are calculated, we use an operator matrix presented in Poelwijk, Krishna, and Ranganathan (2016) <doi:10.1371/journal.pcbi.1004771>.

This package provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). Our JSS article, Willwerscheid, Carbonetto, and Stephens (2025) <doi:10.18637/jss.v114.i03>, provides a detailed introduction to the package.

This package provides a series of R functions that come in handy while working with metabarcoding data. The reasoning of doing this is to have the same functions we use all the time stored in a curated, reproducible way. In a way it is all about putting together the grammar of the tidyverse from Wickham et al.(2019) <doi:10.21105/joss.01686> with the functions we have used in community ecology compiled in packages like vegan from Dixon (2003) <doi:10.1111/j.1654-1103.2003.tb02228.x> and phyloseq McMurdie & Holmes (2013) <doi:10.1371/journal.pone.0061217>. The package includes functions to read sequences from FAST(A/Q) into a tibble ('fasta_reader and fastq_reader'), to process cutadapt Martin (2011) <doi:10.14806/ej.17.1.200> info-file output. When it comes to sequence counts across samples, the package works with the long format in mind (a three column tibble with Sample, Sequence and counts ), with functions to move from there to the wider format.

Computes exact p-values for multinomial goodness-of-fit tests based on multiple test statistics, namely, Pearson's chi-square, the log-likelihood ratio and the probability mass statistic. Implements the algorithm detailed in Resin (2023) <doi:10.1080/10618600.2022.2102026>. Estimates based on the classical asymptotic chi-square approximation or Monte-Carlo simulation can also be computed.

Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD) - for qualitative treatments; residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.

Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages BDgraph', bgms and BGGM', as well as network comparison fitted using the bgms and BBGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for BDgraph and bgms it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.

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.

Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2018) <doi:10.1111/2041-210X.12918>. The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation.

Misc functions programmed by Eduard Szöcs. Provides read_regnie() to read gridded precipitation data from German Weather Service (DWD, see <http://www.dwd.de/> for more information).

Gas/Liquid Chromatography-Mass Spectrometer(GC/LC-MS) Data Analysis for Environmental Science. This package covered topics such molecular isotope ratio, matrix effects and Short-Chain Chlorinated Paraffins analysis etc. in environmental analysis.

This package provides a SQLite database is designed to store all information of experiment-based data including metadata, experiment design, managements, phenotypic values and climate records. The dataset can be imported from an Excel file.

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

This package provides functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.

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>.

Fast and very memory-efficient calculation of isotope patterns, subsequent convolution to theoretical envelopes (profiles) plus valley detection and centroidization or intensoid calculation. Batch processing, resolution interpolation, wrapper, adduct calculations and molecular formula parsing. Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) <doi:10.1021/acs.analchem.5b00941>.

Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in esmprep are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data.

This package provides a collection of fast and flexible functions for analyzing omics data in observational studies. Multiple different approaches for integrating multiple environmental/genetic factors, omics data, and/or phenotype data are implemented. This includes functions for performing omics wide association studies with one or more variables of interest as the exposure or outcome; a function for performing a meet in the middle analysis for linking exposures, omics, and outcomes (as described by Chadeau-Hyam et al., (2010) <doi:10.3109/1354750X.2010.533285>); and a function for performing a mixtures analysis across all omics features using quantile-based g-Computation (as described by Keil et al., (2019) <doi:10.1289/EHP5838>).

This package provides tools to download data from the Eurostat database <https://ec.europa.eu/eurostat> together with search and manipulation utilities.

To run data analysis for enzyme-link immunosorbent assays (ELISAs). Either the five- or four-parameter logistic model will be fitted for data of single ELISA. Moreover, the batch effect correction/normalization will be carried out, when there are more than one batches of ELISAs. Feng (2018) <doi:10.1101/483800>.

This package provides statistical tests and graphics for assessing tests of equivalence. Such tests have similarity as the alternative hypothesis instead of the null. Sample data sets are included.

This package provides a shiny-based front end (the ExPanD app) and a set of functions for exploratory data analysis. Run as a web-based app, ExPanD enables users to assess the robustness of empirical evidence without providing them access to the underlying data. You can export a notebook containing the analysis of ExPanD and/or use the functions of the package to support your exploratory data analysis workflow. Refer to the vignettes of the package for more information on how to use ExPanD and/or the functions of this package.

Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). <doi:10.1093/jrsssb/qkae108>.

This package provides methods to simulate and analyse the size and length of branching processes with an arbitrary offspring distribution. These can be used, for example, to analyse the distribution of chain sizes or length of infectious disease outbreaks, as discussed in Farrington et al. (2003) <doi:10.1093/biostatistics/4.2.279>.

Calculates 15 different goodness of fit criteria. These are; standard deviation ratio (SDR), coefficient of variation (CV), relative root mean square error (RRMSE), Pearson's correlation coefficients (PC), root mean square error (RMSE), performance index (PI), mean error (ME), global relative approximation error (RAE), mean relative approximation error (MRAE), mean absolute percentage error (MAPE), mean absolute deviation (MAD), coefficient of determination (R-squared), adjusted coefficient of determination (adjusted R-squared), Akaike's information criterion (AIC), corrected Akaike's information criterion (CAIC), Mean Square Error (MSE), Bayesian Information Criterion (BIC) and Normalized Mean Square Error (NMSE).