Launches a shiny application generating code to view tables in several ways, import/export tables, modify tables, make some basic graphics. IGoR is a graphic user interface designed to help beginners using simple functions around table management and exploration. Inspired by Rcmdr', IGoR is a code generator that, with simple inputs under a Shiny application, provides R code mainly built around the tidyverse or some packages in the direct line of the Mosaic project: the rio and ggformula packages. The generated code doesn't depend on IGoR and can be manually modified by the user or copied elsewhere.

This package provides functions of marginal mean and quantile regression models are used to analyze environmental exposure and biomonitoring data with repeated measurements and non-detects (i.e., values below the limit of detection (LOD)), as well as longitudinal exposure data that include non-detects and time-dependent covariates. For more details see Chen IC, Bertke SJ, Curwin BD (2021) <doi:10.1038/s41370-021-00345-1>, Chen IC, Bertke SJ, Estill CF (2024) <doi:10.1038/s41370-024-00640-7>, Chen IC, Bertke SJ, Dahm MM (2024) <doi:10.1093/annweh/wxae068>, and Chen IC (2025) <doi:10.1038/s41370-025-00752-8>.

Fits Bayesian regularized varying coefficient models with the Nonparametric Varying Coefficient Spike-and-Slab Lasso (NVC-SSL) introduced by Bai et al. (2023) <https://jmlr.org/papers/volume24/20-1437/20-1437.pdf>. Functions to fit frequentist penalized varying coefficients are also provided, with the option of employing the group lasso penalty of Yuan and Lin (2006) <doi:10.1111/j.1467-9868.2005.00532.x>, the group minimax concave penalty (MCP) of Breheny and Huang <doi:10.1007/s11222-013-9424-2>, or the group smoothly clipped absolute deviation (SCAD) penalty of Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>.

This package provides functions to construct confidence intervals for the Overlap Coefficient (OVL). OVL measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. Implements methods based on the work of Franco-Pereira, A.M., Nakas, C.T., Reiser, B., and Pardo, M.C. (2021) <doi:10.1177/09622802211046386>.

The nonparametric trend and its derivatives in equidistant time series (TS) with short-memory stationary errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel smoother is also built-in as a comparison. With version 1.1.0, a linearity test for the trend function, forecasting methods and backtesting approaches are implemented as well. The smoothing methods of the package are described in Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.

Fits finite Bayesian mixture models with a random number of components. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) <arXiv:1904.09733> and offers a more computationally efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, multivariate Gaussian, univariate Poisson, and multivariate Bernoulli (latent class analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices for the prior on the number of components: shifted Poisson, negative binomial, and point masses (i.e. mixtures with fixed number of components).

Tool to assessing whether the results of a study could be influenced by collinearity. Simulations under a given hypothesized truth regarding effects of an exposure on the outcome are used and the resulting curves of lagged effects are visualized. A user's manual is provided, which includes detailed examples (e.g. a cohort study looking for windows of vulnerability to air pollution, a time series study examining the linear association of air pollution with hospital admissions, and a time series study examining the non-linear association between temperature and mortality). The methods are described in Basagana and Barrera-Gomez (2021) <doi:10.1093/ije/dyab179>.

Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error. Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.

Serves as a platform for published fluorometric enzyme assay protocols. ezmmek calibrates, calculates, and plots enzyme activities as they relate to the transformation of synthetic substrates. At present, ezmmek implements two common protocols found in the literature, and is modular to accommodate additional protocols. Here, these protocols are referred to as the In-Sample Calibration (Hoppe, 1983; <doi:10.3354/meps011299>) and In-Buffer Calibration (German et al., 2011; <doi:10.1016/j.soilbio.2011.03.017>). protocols. By containing multiple protocols, ezmmek aims to stimulate discussion about how to best optimize fluorometric enzyme assays. A standardized approach would make studies more comparable and reproducible.

This package provides a novel statistical model to detect the joint genetic and dynamic gene-environment (GxE) interaction with continuous traits in genetic association studies. It uses varying-coefficient models to account for different GxE trajectories, regardless whether the relationship is linear or not. The package includes one function, GxEtest(), to test a single genetic variant (e.g., a single nucleotide polymorphism or SNP), and another function, GxEscreen(), to test for a set of genetic variants. The method involves a likelihood ratio test described in Crainiceanu, C. M., and Ruppert, D. (2004) <doi:10.1111/j.1467-9868.2004.00438.x>.

This package provides a method for identifying responses to experimental stimulation in mass or flow cytometry that uses high dimensional analysis of measured parameters and can be performed with an end-to-end unsupervised approach. In the context of in vitro stimulation assays where high-parameter cytometry was used to monitor intracellular response markers, using cell populations annotated either through automated clustering or manual gating for a combined set of stimulated and unstimulated samples, HDStIM labels cells as responding or non-responding. The package also provides auxiliary functions to rank intracellular markers based on their contribution to identifying responses and generating diagnostic plots.

Data sets on various litter types like beach litter, riverain litter, floating litter, and seafloor litter are rapidly growing. This package offers a simple user interface to analyse these litter data in a consistent and reproducible way. It also provides functions to facilitate several kinds of litter analysis, e.g., trend analysis, power analysis, and baseline analysis. Under the hood, these functions are also used by the user interface. See Schulz et al. (2019) <doi:10.1016/j.envpol.2019.02.030> for details. MS-Windows users are advised to run litteR in RStudio'. See our vignette: Installation manual for RStudio and litteR'.

This package contains functions for mapping odds ratios, hazard ratios, or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) or Cox models for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting linearly for confounding variables, using methods described by Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (2020). Includes convenient functions for mapping point estimates and confidence intervals, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).

Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean survival times, relative/net survival (in particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and standardized incidence and mortality ratios, all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of Lexis objects (from package Epi') and other computations achieved using data.table'.

The Model Disability Survey (MDS) <https://www.who.int/activities/collection-of-data-on-disability> is a World Health Organization (WHO) general population survey instrument to assess the distribution of disability within a country or region, grounded in the International Classification of Functioning, Disability and Health <https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health>. This package provides fit-for-purpose functions for calculating and presenting the results from this survey, as used by the WHO. The package primarily provides functions for implementing Rasch Analysis (see Andrich (2011) <doi:10.1586/erp.11.59>) to calculate a metric scale for disability.

An implementation of z-curves - a method for estimating expected discovery and replicability rates on the bases of test-statistics of published studies. The package provides functions for fitting the density, EM, and censored EM version (Bartoš & Schimmack, 2022, <doi:10.15626/MP.2021.2720>; Schimmack & Bartoš, 2023, <doi: 10.1371/journal.pone.0290084>), as well as the original density z-curve (Brunner & Schimmack, 2020, <doi:10.15626/MP.2018.874>). Furthermore, the package provides summarizing and plotting functions for the fitted z-curve objects. See the aforementioned articles for more information about the z-curves, expected discovery and replicability rates, validation studies, and limitations.

AUCell identifies cells with active gene sets (e.g. signatures, gene modules, etc) in single-cell RNA-seq data. AUCell uses the Area Under the Curve (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed.

This package provides a programmatic interface to the Finnish Biodiversity Information Facility ('FinBIF') API (<https://api.laji.fi>). FinBIF aggregates Finnish biodiversity data from multiple sources in a single open access portal for researchers, citizen scientists, industry and government. FinBIF allows users of biodiversity information to find, access, combine and visualise data on Finnish plants, animals and microorganisms. The finbif package makes the publicly available data in FinBIF easily accessible to programmers. Biodiversity information is available on taxonomy and taxon occurrence. Occurrence data can be filtered by taxon, time, location and other variables. The data accessed are conveniently preformatted for subsequent analyses.

Estimation of generalized linear models with correlated/clustered observations by use of generalized estimating equations (GEE). See e.g. Halekoh and Højsgaard, (2005, <doi:10.18637/jss.v015.i02>), for details. Several types of clustering are supported, including exchangeable variance structures, AR1 structures, M-dependent, user-specified variance structures and more. The model fitting computations are performed using modified code from the geeM package, while the interface and output objects have been written to resemble the geepack package. The package also contains additional tools for working with and inspecting results from the geepack package, e.g. a confint method for geeglm objects from geepack'.

This package provides a suite of tools for estimating, assessing model fit, simulating from, and visualizing location dependent marked point processes characterized by regularity in the pattern. You provide a reference marked point process, a set of raster images containing location specific covariates, and select the estimation algorithm and type of mark model. ldmppr estimates the process and mark models and allows you to check the appropriateness of the model using a variety of diagnostic tools. Once a satisfactory model fit is obtained, you can simulate from the model and visualize the results. Documentation for the package ldmppr is available in the form of a vignette.

The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. memapp is a web application created in the Shiny framework for the mem R package.

This package provides a toolbox for working with public opinion data from Argentina. It facilitates access to microdata and the calculation of indicators of the Trust in Government Index (ICG), prepared by the Torcuato Di Tella University. Although we will try to document everything possible in English, by its very nature Spanish will be the main language. El paquete fue pensado como una caja de herramientas para el trabajo con datos de opinión pública de Argentina. El mismo facilita el acceso a los microdatos y el cálculos de indicadores del à ndice de Confianza en el Gobierno (ICG), elaborado por la Universidad Torcuato Di Tella.

The main attribute of PopVar is the prediction of genetic variance in bi-parental populations, from which the package derives its name. PopVar contains a set of functions that use phenotypic and genotypic data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015, <doi:10.2135/cropsci2015.01.0030>). A dataset think_barley.rda is included for reference and examples.

This package implements `import()` and `export()` standard generics for importing and exporting biological data formats. `import()` supports whole-file as well as chunk-wise iterative import. The `import()` interface optionally provides a standard mechanism for 'lazy' access via `filter()` (on row or element-like components of the file resource), `select()` (on column-like components of the file resource) and `collect()`. The `import()` interface optionally provides transparent access to remote (e.g. via https) as well as local access. Developers can register a file extension, e.g., `.loom` for dispatch from character-based URIs to specific `import()` / `export()` methods based on classes representing file types, e.g., `LoomFile()`.