Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions.

Various kinds of designs for (industrial) experiments can be created. The package uses, and sometimes enhances, design generation routines from other packages. So far, response surface designs from package rsm', Latin hypercube samples from packages lhs and DiceDesign', and D-optimal designs from package AlgDesign have been implemented.

Detrend fluorescence microscopy image series for fluorescence fluctuation and correlation spectroscopy ('FCS and FFS') analysis. This package contains functionality published in a 2016 paper <doi:10.1093/bioinformatics/btx434> but it has been extended since then with the Robin Hood algorithm and thus contains unpublished work.

This package implements a generalized linear model approach for detecting differentially expressed genes across treatment groups in count data. The package supports both quasi-Poisson and negative binomial models to handle over-dispersion, ensuring robust identification of differential expression. It allows for the inclusion of treatment effects and gene-wise covariates, as well as normalization factors for accurate scaling across samples. Additionally, it incorporates statistical significance testing with options for p-value adjustment and log2 fold range thresholds, making it suitable for RNA-seq analysis as described in by Xu et al., (2024) <doi:10.1371/journal.pone.0300565>.

This package provides a port of the python diffcp package. Computes the derivative of the optimal solution map of a convex cone program, treating the program as an implicit function of its data (constraint matrix, offset, objective coefficients, and optionally a quadratic), mirroring Agrawal et al. (2019) <doi:10.48550/arXiv.1904.09043>.

Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.

Decomposing value added growth into explanatory factors. A cost constrained value added function is defined to specify the production frontier. Industry estimates can also be aggregated using a weighted average approach. Details about the methodology and data can be found in Diewert and Fox (2018) <doi:10.1093/oxfordhb/9780190226718.013.19> and Zeng, Parsons, Diewert and Fox (2018) <https://www.business.unsw.edu.au/research-site/centreforappliedeconomicresearch-site/Documents/emg2018-6_SZeng_EMG-Slides.pdf>.

This package provides data transformations, estimation utilities, predictive evaluation measures and simulation functions for discrete time survival analysis.

This package provides a flexible container to manage and annotate Differential Gene Expression (DGE) analysis results (Smythe et. al (2015) <doi:10.1093/nar/gkv007>). The DGEobj has data slots for row (gene), col (samples), assays (matrix n-rows by m-samples dimensions) and metadata (not keyed to row, col, or assays). A set of accessory functions to deposit, query and retrieve subsets of a data workflow has been provided. Attributes are used to capture metadata such as species and gene model, including reproducibility information such that a 3rd party can access a DGEobj history to see how each data object was created or modified. Since the DGEobj is customizable and extensible it is not limited to RNA-seq analysis types of workflows -- it can accommodate nearly any data analysis workflow that starts from a matrix of assays (rows) by samples (columns).

Helpers functions to process, analyse, and visualize the output of single locus species delimitation methods. For full functionality, please install suggested software at <https://legallab.github.io/delimtools/articles/install.html>.

This package performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.

Fast fitting of generalised linear models on moderately large datasets, by taking an initial sample, fitting in memory, then evaluating the score function for the full data in the database. Thomas Lumley <doi:10.1080/10618600.2019.1610312>.

Fits disaggregation regression models using TMB ('Template Model Builder'). When the response data are aggregated to polygon level but the predictor variables are at a higher resolution, these models can be useful. Regression models with spatial random fields. The package is described in detail in Nandi et al. (2023) <doi:10.18637/jss.v106.i11>.

Several tests for differential methylation in methylation array data, including one-sided differential mean and variance test. Methods used in the package refer to Dai, J, Wang, X, Chen, H and others (2021) "Incorporating increased variability in discovering cancer methylation markers", Biostatistics, submitted.

This package provides vectorised functions for computing p-values of various common discrete statistical tests, as described e.g. in Agresti (2002) <doi:10.1002/0471249688>, including their distributions. Exact and approximate computation methods are provided. For exact ones, several procedures of determining two-sided p-values are included, which are outlined in more detail in Hirji (2006) <doi:10.1201/9781420036190>.

Distributed Online Mean Tests is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform mean tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, Domean ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for mean testing. The philosophy of Domean is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.

This package creates interactive draggable plots from grouped projected sf geometry. The primary deliverable is a browser-based D3 helper where regions and labels can be moved freely; users drag, then copy or download the resulting offset tables. Labels can be derived automatically with make_region_labels(), supplied directly with as_drag_labels(), and their moved positions saved and restored with read_label_state() and apply_label_state(). Hierarchical spatial datasets are supported via hierarchy detection, upload profiling, make_hierarchy_key(), and inherit_layout(), which recommend parent-child groupings and propagate parent-level drag offsets to finer child groupings. Automatic starting layouts are provided by suggest_offsets() using radial, grid, or directional algorithms. Spatial file diagnostics are available through dragmapr_diagnostics(). When a reproducible static image is also needed, render_dragged_map() reconstructs the layout as a ggplot2 plot from the source geometry plus the exported offset tables. Project bundles can be written with write_dragmapr_project() and rendered with render_dragmapr_project(). The interactive layer is built on the D3 library: Bostock, Ogievetsky and Heer (2011) <doi:10.1109/TVCG.2011.185>. Spatial data handling uses the sf package: Pebesma (2018) <doi:10.32614/RJ-2018-009>.

Bayesian Beta Regression, adapted for bounded discrete responses, commonly seen in survey responses. Estimation is done via Markov Chain Monte Carlo sampling, using a Gibbs wrapper around univariate slice sampler (Neal (2003) <DOI:10.1214/aos/1056562461>), as implemented in the R package MfUSampler (Mahani and Sharabiani (2017) <DOI: 10.18637/jss.v078.c01>).

This package provides tools for constructing and auditing longitudinal decision paths from panel data. Implements a decision infrastructure framework for representing institutional AI systems as generators of time-ordered binary decision sequences. Provides functions to build path objects from panel data, summarise per-unit descriptors (dosage, switching rate, onset, duration, longest run), compute the Decision Reliability Index (DRI) following Cronbach (1951) <doi:10.1007/BF02310555>, estimate Shannon decision-path entropy following Shannon (1948) <doi:10.1002/j.1538-7305.1948.tb01338.x>, classify systems by infrastructure type (static, periodic, continuous, human-in-the-loop), and evaluate subgroup disparities in decision exposure and stability. Applications include education, policy, health, and organisational research.

This package implements a Bayesian algorithm for overcoming weak separation in Bayesian latent class analysis. Reference: Li et al. (2023) <arXiv:2306.04700>.

Compute the dynamic threshold panel model suggested by (Stephanie Kremer, Alexander Bick and Dieter Nautz (2013) <doi:10.1007/s00181-012-0553-9>) in which they extended the (Hansen (1999) <doi: 10.1016/S0304-4076(99)00025-1>) original static panel threshold estimation and the Caner and (Hansen (2004) <doi:10.1017/S0266466604205011>) cross-sectional instrumental variable threshold model, where generalized methods of moments type estimators are used.

Computes small-sample degrees of freedom adjustment for heteroskedasticity robust standard errors, and for clustered standard errors in linear regression. See Imbens and Kolesár (2016) <doi:10.1162/REST_a_00552> for a discussion of these adjustments.

Ecological Metadata Language or EML is a long-established format for describing ecological datasets to facilitate sharing and re-use. Because EML is effectively a modified xml schema, however, it is challenging to write and manipulate for non-expert users. delma supports users to write metadata statements in R Markdown or Quarto markdown format, and parse them to EML and (optionally) back again.

Compressed spatial vector data originally from <https://dawadocs.dataforsyningen.dk/> saved as Simple Features, SF, objects with data on population, age and gender from Statistics Denmark <https://www.dst.dk/da/>.