High Dynamic Range (HDR) images support a large range in luminosity between the lightest and darkest regions of an image. To capture this range, data in HDR images is often stored as floating point numbers and in formats that capture more data and channels than standard image types. This package supports reading and writing two types of HDR images; PFM (Portable Float Map) and OpenEXR images. HDR images can be converted to lower dynamic ranges (for viewing) using tone-mapping. A number of tone-mapping algorithms are included which are based on Reinhard (2002) "Photographic tone reproduction for digital images" <doi:10.1145/566654.566575>.

Generalized Additive Mixed Modeling (GAMM; Lin & Zhang, 1999) as implemented in the R package mgcv is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).

This package implements Collective And Point Anomaly (CAPA) Fisch, Eckley, and Fearnhead (2022) <doi:10.1002/sam.11586>, Multi-Variate Collective And Point Anomaly (MVCAPA) Fisch, Eckley, and Fearnhead (2021) <doi:10.1080/10618600.2021.1987257>, Proportion Adaptive Segment Selection (PASS) Jeng, Cai, and Li (2012) <doi:10.1093/biomet/ass059>, and Bayesian Abnormal Region Detector (BARD) Bardwell and Fearnhead (2015) <doi:10.1214/16-BA998>. These methods are for the detection of anomalies in time series data. Further information regarding the use of this package along with detailed examples can be found in Fisch, Grose, Eckley, Fearnhead, and Bardwell (2024) <doi:10.18637/jss.v110.i01>.

Several implementations of non-parametric stable bootstrap-based techniques to determine the numbers of components for Partial Least Squares linear or generalized linear regression models as well as and sparse Partial Least Squares linear or generalized linear regression models. The package collects techniques that were published in a book chapter (Magnanensi et al. 2016, The Multiple Facets of Partial Least Squares and Related Methods', <doi:10.1007/978-3-319-40643-5_18>) and two articles (Magnanensi et al. 2017, Statistics and Computing', <doi:10.1007/s11222-016-9651-4>) and (Magnanensi et al. 2021, Frontiers in Applied Mathematics and Statistics', <doi:10.3389/fams.2021.693126>).

This package provides tools to calibrate, validate, and make predictions with the General Unified Threshold model of Survival adapted for Bee species. The model is presented in the publication from Baas, J., Goussen, B., Miles, M., Preuss, T.G., Roessing, I. (2022) <doi:10.1002/etc.5423> and Baas, J., Goussen, B., Taenzler, V., Roeben, V., Miles, M., Preuss, T.G., van den Berg, S., Roessink, I. (2024) <doi:10.1002/etc.5871>, and is based on the GUTS framework Jager, T., Albert, C., Preuss, T.G. and Ashauer, R. (2011) <doi:10.1021/es103092a>. The authors are grateful to Bayer A.G. for its financial support.

This package implements fast, exact bootstrap Principal Component Analysis and Singular Value Decompositions for high dimensional data, as described in <doi:10.1080/01621459.2015.1062383> (see also <doi:10.48550/arXiv.1405.0922>). For data matrices that are too large to operate on in memory, users can input objects with class ff (see the ff package), where the actual data is stored on disk. In response, this package will implement a block matrix algebra procedure for calculating the principal components (PCs) and bootstrap PCs. Depending on options set by the user, the parallel package can be used to parallelize the calculation of the bootstrap PCs.

Copernicus Atmosphere Monitoring Service (CAMS) radiations service provides time series of global, direct, and diffuse irradiations on horizontal surface, and direct irradiation on normal plane for the actual weather conditions as well as for clear-sky conditions. The geographical coverage is the field-of-view of the Meteosat satellite, roughly speaking Europe, Africa, Atlantic Ocean, Middle East. The time coverage of data is from 2004-02-01 up to 2 days ago. Data are available with a time step ranging from 15 min to 1 month. For license terms and to create an account, please see <http://www.soda-pro.com/web-services/radiation/cams-radiation-service>.

This package provides functions for loading large (10M+ lines) CSV and other delimited files, similar to read.csv, but typically faster and using less memory than the standard R loader. While not entirely general, it covers many common use cases when the types of columns in the CSV file are known in advance. In addition, the package provides a class int64', which represents 64-bit integers exactly when reading from a file. The latter is useful when working with 64-bit integer identifiers exported from databases. The CSV file loader supports common column types including integer', double', string', and int64', leaving further type transformations to the user.

Testing and documenting code that communicates with remote databases can be painful. Although the interaction with R is usually relatively simple (e.g. data(frames) passed to and from a database), because they rely on a separate service and the data there, testing them can be difficult to set up, unsustainable in a continuous integration environment, or impossible without replicating an entire production cluster. This package addresses that by allowing you to make recordings from your database interactions and then play them back while testing (or in other contexts) all without needing to spin up or have access to the database your code would typically connect to.

Traditional phasing programs are limited to diploid organisms. Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haplotype searches in a multiple infection setting. This package is primarily developed as part of the Pf3k project, which is a global collaboration using the latest sequencing technologies to provide a high-resolution view of natural variation in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from patient blood sample, which often contains more than one parasite strain, with unknown proportions. This package is used for deconvoluting mixed haplotypes, and reporting the mixture proportions from each sample.

Computes a series of indices commonly used in the fields of economic geography, economic complexity, and evolutionary economics to describe the location, distribution, spatial organization, structure, and complexity of economic activities. Functions include basic spatial indicators such as the location quotient, the Krugman specialization index, the Herfindahl or the Shannon entropy indices but also more advanced functions to compute different forms of normalized relatedness between economic activities or network-based measures of economic complexity. Most of the functions use matrix calculus and are based on bipartite (incidence) matrices consisting of region - industry pairs. These are described in Balland (2017) <http://econ.geo.uu.nl/peeg/peeg1709.pdf>.

This package provides functions to prepare and analyse eye tracking data of reading exercises. The functions allow some basic data preparations and code fixations as first and second pass. First passes can be further devided into forward and reading. The package further allows for aggregating fixation times per AOI or per AOI and per type of pass (first forward, first rereading, second). These methods are based on Hyönä, Lorch, and Rinck (2003) <doi:10.1016/B978-044451020-4/50018-9> and Hyönä, and Lorch (2004) <doi:10.1016/j.learninstruc.2004.01.001>. It is also possible to convert between metric length and visual degrees.

This package provides a suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. â ideanetâ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, â ideanetâ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding.

This package performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) <doi:10.1007/s10651-007-0035-y>. The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package.

msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data.

Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes to estimate cellular composition in the matched bulk proteomes. The expression of genes and proteins at either bulk level or cell type level can be integrated by Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile, MICSQTL can perform cell-type-specic quantitative trait loci (QTL) mapping to proteins or transcripts based on the input of bulk expression data and the estimated cellular composition per molecule type, without the need for single cell sequencing. We use matched transcriptome-proteome from human brain frontal cortex tissue samples to demonstrate the input and output of our tool.

This package implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) <doi:10.1101/2020.05.12.077776>. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space.

This package contains functions to compute the nonparametric maximum likelihood estimator (MLE) for the bivariate distribution of (X,Y), when realizations of (X,Y) cannot be observed directly. To be more precise, we consider the situation where we observe a set of rectangles that are known to contain the unobservable realizations of (X,Y). We compute the MLE based on such a set of rectangles. The methods can also be used for univariate censored data (see data set cosmesis), and for censored data with competing risks (see data set menopause). The package also provides functions to visualize the observed data and the MLE.

R7RS-small Scheme library for reading and writing RSV (Rows of String Values) data format, a very simple binary format for storing tables of strings. It is a competitor for e.g. CSV (Comma Seperated Values), and TSV (Tab Separated Values). Its main benefit is that the strings are represented as Unicode encoded as UTF-8, and the value and row separators are byte values that are never used in UTF-8, so the strings do not need any error prone escaping and thus can be written and read verbatim.

Specified in https://github.com/Stenway/RSV-Specification and demonstrated in https://www.youtube.com/watch?v=tb_70o6ohMA.

For a balanced design of experiments, this package calculates the sample size required to detect a certain standardized effect size, under a significance level. This package also provides three graphs; detectable standardized effect size vs power, sample size vs detectable standardized effect size, and sample size vs power, which show the mutual relationship between the sample size, power and the detectable standardized effect size. The detailed procedure is described in R. V. Lenth (2006-9) <https://homepage.divms.uiowa.edu/~rlenth/Power/>, Y. B. Lim (1998), M. A. Kastenbaum, D. G. Hoel and K. O. Bowman (1970) <doi:10.2307/2334851>, and Douglas C. Montgomery (2013, ISBN: 0849323312).

Meta-analysis of generalized additive models and generalized additive mixed models. A typical use case is when data cannot be shared across locations, and an overall meta-analytic fit is sought. metagam provides functionality for removing individual participant data from models computed using the mgcv and gamm4 packages such that the model objects can be shared without exposing individual data. Furthermore, methods for meta-analysing these fits are provided. The implemented methods are described in Sorensen et al. (2020), <doi:10.1016/j.neuroimage.2020.117416>, extending previous works by Schwartz and Zanobetti (2000) and Crippa et al. (2018) <doi:10.6000/1929-6029.2018.07.02.1>.

We implement functions allowing for mediation analysis to be performed in cases where the mediator is a count variable with excess zeroes. First a function is provided allowing users to perform analysis for zero-inflated count variables using the marginalized zero-inflated Poisson (MZIP) model (Long et al. 2014 <DOI:10.1002/sim.6293>). Using the counterfactual approach to mediation and MZIP we can obtain natural direct and indirect effects for the overall population. Using delta method processes variance estimation can be performed instantaneously. Alternatively, bootstrap standard errors can be used. We also provide functions for cases with exposure-mediator interactions with four-way decomposition of total effect.

Nonparametric survival function estimates and semiparametric regression for the multivariate failure time data with right-censoring. For nonparametric survival function estimates, the Volterra, Dabrowska, and Prentice-Cai estimates for bivariate failure time data may be computed as well as the Dabrowska estimate for the trivariate failure time data. Bivariate marginal hazard rate regression can be fitted for the bivariate failure time data. Functions are also provided to compute (bootstrap) confidence intervals and plot the estimates of the bivariate survival function. For details, see "The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach", Prentice, R., Zhao, S. (2019, ISBN: 978-1-4822-5657-4), CRC Press.

This package provides methods and tools for forecasting univariate time series using the NARFIMA (Neural AutoRegressive Fractionally Integrated Moving Average) model. It combines neural networks with fractional differencing to capture both nonlinear patterns and long-term dependencies. The NARFIMA model supports seasonal adjustment, Box-Cox transformations, optional exogenous variables, and the computation of prediction intervals. In addition to the NARFIMA model, this package provides alternative forecasting models including NARIMA (Neural ARIMA), NBSTS (Neural Bayesian Structural Time Series), and NNaive (Neural Naive) for performance comparison across different modeling approaches. The methods are based on algorithms introduced by Chakraborty et al. (2025) <doi:10.48550/arXiv.2509.06697>.