Org Ref is an Emacs library that provides rich support for citations, labels and cross-references in Org mode.

The basic idea of Org Ref is that it defines a convenient interface to insert citations from a reference database (e.g., from BibTeX files), and a set of functional Org links for citations, cross-references and labels that export properly to LaTeX, and that provide clickable functionality to the user. Org Ref interfaces with Helm BibTeX to facilitate citation entry, and it can also use RefTeX.

It also provides a fairly large number of utilities for finding bad citations, extracting BibTeX entries from citations in an Org file, and functions to create and modify BibTeX entries from a variety of sources, most notably from a DOI.

Org Ref is especially suitable for Org documents destined for LaTeX export and scientific publication. Org Ref is also useful for research documents and notes.

Tool for easy prior construction and visualization. It helps to formulates joint prior distributions for variance parameters in latent Gaussian models. The resulting prior is robust and can be created in an intuitive way. A graphical user interface (GUI) can be used to choose the joint prior, where the user can click through the model and select priors. An extensive guide is available in the GUI. The package allows for direct inference with the specified model and prior. Using a hierarchical variance decomposition, we formulate a joint variance prior that takes the whole model structure into account. In this way, existing knowledge can intuitively be incorporated at the level it applies to. Alternatively, one can use independent variance priors for each model components in the latent Gaussian model. Details can be found in the accompanying scientific paper: Hem, Fuglstad, Riebler (2024, Journal of Statistical Software, <doi:10.18637/jss.v110.i03>).

Org-Babel support for evaluating rust code. Much of this is modeled after `ob-C'. Just like the `ob-C', you can specify :flags headers when compiling with the "rust run" command. Unlike `ob-C', you can also specify :args which can be a list of arguments to pass to the binary. If you quote the value passed into the list, it will use `ob-ref to find the reference data. If you do not include a main function or a package name, `ob-rust will provide it for you and it's the only way to properly use very limited implementation: - currently only support :results output. ; Requirements: - You must have rust and cargo installed and the rust and cargo should be in your `exec-path rust command. - rust-script - `rust-mode is also recommended for syntax highlighting and formatting. Not this particularly needs it, it just assumes you have it.

This package implements the Mixed Treatment-State Causal Model (MTSCM), a Bayesian framework for estimating causal effects of clinical interventions on bounded continuous outcomes in longitudinal observational studies with irregular visits. The methodology is specifically designed for periodontal disease research, where discrete treatments and continuous disease states (e.g., proportion of periodontal pockets exceeding 3 mm) reciprocally influence one another under dynamic feedback. The package integrates a double-censored Tobit likelihood to handle boundary mass at zero and one, subject-specific random effects to capture within-subject correlation, and flexible tree-based ensemble priors (standard BART and Soft BART) to model complex nonlinear interactions without parametric restrictions. Causal identification is established under the potential outcomes framework via the G-computation formula, with key estimands including the Mixed Average Potential Outcome (MAPO) and the Mixed Probability of Disease Resolution (MPDR). The package provides functions for model fitting, posterior inference, and causal estimand estimation.

Simulate inventory policies with and without forecasting, facilitate inventory analysis calculations such as stock levels and re-order points,pricing and promotions calculations. The package includes calculations of inventory metrics, stock-out calculations and ABC analysis calculations. The package includes revenue management techniques such as Multi-product optimization,logit and polynomial model optimization. The functions are referenced from : 1-Harris, Ford W. (1913). "How many parts to make at once". Factory, The Magazine of Management. 2- Nahmias, S. Production and Operations Analysis. McGraw-Hill International Edition. 3-Silver, E.A., Pyke, D.F., Peterson, R. Inventory Management and Production Planning and Scheduling. 4-Ballou, R.H. Business Logistics Management. 5-MIT Micromasters Program. 6- Columbia University course for supply and demand analysis. 8- Price Elasticity of Demand MATH 104,Mark Mac Lean (with assistance from Patrick Chan) 2011W For further details or correspondence :<www.linkedin.com/in/haythamomar>, <www.rescaleanalytics.com>.

Treats language model evaluations as statistical experiments and supplies the inference they require. Provides central limit theorem and cluster-robust standard errors for evaluation scores, paired and unpaired model comparisons, variance decomposition when several responses are drawn per question, control-variate variance reduction, multiplicity adjustment across benchmark suites, and power and minimum detectable effect calculations for planning evaluations, following Miller (2024) <doi:10.48550/arXiv.2411.00640>. For evaluations scored by a model judge, implements agreement statistics against a human gold standard and prediction-powered inference (Angelopoulos et al. 2023) <doi:10.1126/science.adi6000> with the power-tuned estimator of Angelopoulos, Bates and Jordan (2023) <doi:10.48550/arXiv.2311.01453>, so a small set of human labels debiases a large set of judge scores. Leaderboards are supported through bootstrap rank intervals and Bradley-Terry ratings (Bradley and Terry 1952) <doi:10.2307/2334029>. Accepts scores from any evaluation harness.

This package provides a workflow for probing, plotting, and checking cross-level interaction effects in two-level mixed-effects models fitted with lme4 (Bates et al., 2015) <doi:10.18637/jss.v067.i01>. Implements simple slopes analysis following Aiken and West (1991, ISBN:9780761907121), Johnson-Neyman intervals following Johnson and Fay (1950) <doi:10.1007/BF02288864> and Bauer and Curran (2005) <doi:10.1207/s15327906mbr4003_5>, and grand- or group-mean centering as described in Enders and Tofighi (2007) <doi:10.1037/1082-989X.12.2.121>. Tests and intervals use Satterthwaite degrees of freedom via lmerTest (Kuznetsova et al., 2017) <doi:10.18637/jss.v082.i13> by default, with Kenward-Roger and between-cluster alternatives. Also provides confidence and new-cluster prediction intervals for simple slopes in random-slope models, contour plots of predicted outcomes over the predictor-by-moderator space, and leave-one-cluster-out influence diagnostics for the interaction.

stJoincount facilitates the application of join count analysis to spatial transcriptomic data generated from the 10x Genomics Visium platform. This tool first converts a labeled spatial tissue map into a raster object, in which each spatial feature is represented by a pixel coded by label assignment. This process includes automatic calculation of optimal raster resolution and extent for the sample. A neighbors list is then created from the rasterized sample, in which adjacent and diagonal neighbors for each pixel are identified. After adding binary spatial weights to the neighbors list, a multi-categorical join count analysis is performed to tabulate "joins" between all possible combinations of label pairs. The function returns the observed join counts, the expected count under conditions of spatial randomness, and the variance calculated under non-free sampling. The z-score is then calculated as the difference between observed and expected counts, divided by the square root of the variance.

This package provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined ggplot2 charts. Datasets include atmospheric CO2, methane, emissions, instrumental and proxy temperature records, CMIP6 projections, sea levels, Arctic/Antarctic sea-ice, Hurricanes, Wildfires, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory <https://gml.noaa.gov/ccgg/trends/data.html>, Global Carbon Project <https://www.globalcarbonproject.org/carbonbudget/>, NASA GISTEMP <https://data.giss.nasa.gov/gistemp/>, National Snow and Sea Ice Data Center <https://nsidc.org/home>, CSIRO <https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/>, NOAA Laboratory for Satellite Altimetry <https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/> and HURDAT Atlantic Hurricane Database <https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html>, Vostok Paleo carbon dioxide and temperature data: <doi:10.3334/CDIAC/ATG.009>.

Included are two main interfaces, bentcable.ar() and bentcable.dev.plot(), for fitting and diagnosing bent-cable regressions for autoregressive time-series data (Chiu and Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time series or otherwise - Chiu, Lockhart and Routledge 2006, <doi:10.1198/016214505000001177>). Some components in the package can also be used as stand-alone functions. The bent cable (linear-quadratic-linear) generalizes the broken stick (linear-linear), which is also handled by this package. Version 0.2 corrected a glitch in the computation of confidence intervals for the CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved robustness of the error-message producing mechanism. Version 0.3.1 improves the NAMESPACE file of the package. It is the author's intention to distribute any future updates via GitHub.

This package provides a large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set.

Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified. Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data. Cho P, Bent B, Wittmann A, et al. (2020) <https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract> American Diabetes Association (2020) <https://professional.diabetes.org/diapro/glucose_calc> Kovatchev B (2019) <doi:10.1177/1932296819826111> Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3> Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017> Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>.

In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. Here we use cyclic shift permutations to identify recurrent copy number alterations in a single cohort or recurrent copy number differences in two cohorts based on a common set of genomic markers. Additional functionality is provided to perform downstream analyses, including the creation of summary files and graphics. DiNAMIC.Duo builds upon the original DiNAMIC package of Walter et al. (2011) <doi:10.1093/bioinformatics/btq717> and leverages the theory developed in Walter et al. (2015) <doi:10.1093/biomet/asv046>. An article describing DiNAMIC.Duo by Walter et al. (2022) can be found at <doi: 10.1093/bioinformatics/btac542>.

This package provides a unified toolkit for out-of-sample forecast dominance testing. Covers unconditional and conditional equal and superior predictive ability, encompassing, and nested-model comparison. Implements the Diebold-Mariano test with the Harvey, Leybourne, and Newbold (1997) <doi:10.1016/S0169-2070(96)00719-4> small-sample correction; the Clark-West MSFE-adjusted statistic (Clark and West, 2007) <doi:10.1016/j.jeconom.2006.05.023>; the ENC-NEW encompassing test of Clark and McCracken (2001) <doi:10.1016/S0304-4076(01)00071-9>; the Giacomini-White conditional equal predictive ability test (Giacomini and White, 2006) <doi:10.1111/j.1468-0262.2006.00718.x>; Hansen's superior predictive ability test (Hansen, 2005) <doi:10.1198/073500105000000063>; the conditional superior predictive ability test of Li, Liao, and Quaedvlieg (2022) <doi:10.1093/restud/rdab039>; and the uniform and average multi-horizon SPA tests of Quaedvlieg (2021) <doi:10.1080/07350015.2019.1620074>.

This package provides tools for antimicrobial resistance surveillance, epidemiological analysis, temporal trend detection, early warning detection, spatial cluster identification, and risk factor analysis. The package supports analysis of antimicrobial resistance patterns, resistance to multiple antimicrobial classes, temporal surveillance, and spatial epidemiology for applications in veterinary, medical, and One Health research. Antimicrobial resistance surveillance approaches are informed by guidelines from WHO (2023) <https://www.who.int/publications/i/item/9789240076600> and WOAH (2024) <https://www.woah.org/fileadmin/Home/eng/Health_standards/tahc/2024/en_chapitre_antibio_harmonisation.htm>. Statistical methods include cumulative sum (CUSUM) monitoring (Page, 1954) <doi:10.1093/biomet/41.1-2.100>, exponentially weighted moving average (EWMA) monitoring (Roberts, 1959) <doi:10.1080/00401706.1959.10489860>, Local Moran's I spatial analysis (Anselin, 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x>, and multidrug- and extensively drug-resistant classification (Magiorakos et al., 2012) <doi:10.1111/j.1469-0691.2011.03570.x>.

Tests whether the linear hypothesis of a model is correct specified using Dominguez-Lobato test. Also Ramsey's RESET (Regression Equation Specification Error Test) test is implemented and Wald tests can be carried out. Although RESET test is widely used to test the linear hypothesis of a model, Dominguez and Lobato (2019) proposed a novel approach that generalizes well known specification tests such as Ramsey's. This test relies on wild-bootstrap; this package implements this approach to be usable with any function that fits linear models and is compatible with the update() function such as stats'::lm(), lfe'::felm() and forecast'::Arima(), for ARMA (autoregressiveâ moving-average) models. Also the package can handle custom statistics such as Cramer von Mises and Kolmogorov Smirnov, described by the authors, and custom distributions such as Mammen (discrete and continuous) and Rademacher. Manuel A. Dominguez & Ignacio N. Lobato (2019) <doi:10.1080/07474938.2019.1687116>.

This package provides methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored. We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results. We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.

Analyzing genetic data obtained from pooled samples. This package can read in Fragment Analysis output files, process the data, and score peaks, as well as facilitate various analyses, including cluster analysis, calculation of genetic distances and diversity indices, as well as bootstrap resampling for statistical inference. Specifically tailored to handle genetic data efficiently, researchers can explore population structure, genetic differentiation, and genetic relatedness among samples. We updated some functions from Covarrubias-Pazaran et al. (2016) <doi:10.1186/s12863-016-0365-6> to allow for the use of new file formats and referenced the following to write our genetic analysis functions: Long et al. (2022) <doi:10.1038/s41598-022-04776-0>, Jost (2008) <doi:10.1111/j.1365-294x.2008.03887.x>, Nei (1973) <doi:10.1073/pnas.70.12.3321>, Foulley et al. (2006) <doi:10.1016/j.livprodsci.2005.10.021>, Chao et al. (2008) <doi:10.1111/j.1541-0420.2008.01010.x>.

This package provides a collection of R functions that are widely used by the Petersen Lab. Included are functions for various purposes, including evaluating the accuracy of judgments and predictions, performing scoring of assessments, generating correlation matrices, conversion of data between various types, data management, psychometric evaluation, extensions related to latent variable modeling, various plotting capabilities, and other miscellaneous useful functions. By making the package available, we hope to make our methods reproducible and replicable by others and to help others perform their data processing and analysis methods more easily and efficiently. The codebase is provided in Petersen (2025) <doi:10.5281/zenodo.7602890> and on CRAN': <doi: 10.32614/CRAN.package.petersenlab>. The package is described in "Principles of Psychological Assessment: With Applied Examples in R" (Petersen, 2024, 2025a) <doi:10.1201/9781003357421>, <doi:10.25820/work.007199>, <doi:10.5281/zenodo.6466589> and in "Fantasy Football Analytics: Statistics, Prediction, and Empiricism Using R" (Petersen, 2025b).

This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage.

Characterises the environment surrounding point locations by computing land-cover composition within circular buffers directly from vector polygons, without conversion to a raster grid. For each site and each class it returns the exact surface area inside the buffer and a distance-decay weighted "effective" area in which the kernel is integrated over polygon geometry rather than evaluated at the polygon centroid, avoiding the large bias the centroid approximation introduces for elongated features passing close to the site. Polygons may overlap, so class areas are not constrained to sum to the buffer area. Intended for buffer-based exposure assessment and fine-scale spatial epidemiology, where the relevant scale is tens of metres and global land-cover products are too coarse: land-use regression around air-quality monitors, green space around residential addresses, vector-surveillance traps, and comparable designs. The classification dictionary is user-supplied, and point features and distances to off-buffer reference features are recorded alongside the areas.

This package provides spatial datasets ready to use for ecological modelling and raster companion data for prediction: Neanderthal presence during the Last Interglacial (Benito et al. 2017 <doi:10.1111/jbi.12845>); Plant diversity metrics for the World's Ecoregions (Maestre et al. 2021 <doi:10.1111/nph.17398>); tree richness across the Americas (Benito et al. 2013 <doi:10.1111/2041-210X.12022>); plant communities from the Sierra Nevada (Spain) with future climate scenarios (Benito et al. 2013 <doi:10.1111/2041-210X.12022>); butterfly-plant interaction data from Sierra Nevada (Spain) (Benito et al. 2011 <doi:10.1007/s10584-010-0015-3>); plant species occurrences in Andalusia (Spain) (Benito et al. 2014 <doi:10.1111/ddi.12148>); presence of the plant Linaria nigricans and greenhouses (Benito et al. 2009 <doi:10.1007/s10531-009-9604-8>); global NDVI and environmental predictors, and European oak species occurrences. All datasets include pre-processed environmental predictors ready for statistical modelling.

Implementation of the unity forest (UFO) framework (Hornung & Hapfelmeier, 2026, <doi:10.48550/arXiv.2601.07003>). UFOs are a random forest variant designed to better take covariates with purely interaction-based effects into account, including interactions for which none of the involved covariates exhibits a marginal effect. While this framework tends to improve discrimination and predictive accuracy compared to standard random forests, it also facilitates the identification and interpretation of (marginal or interactive) effects: In addition to the UFO algorithm for tree construction, the package includes the unity variable importance measure (unity VIM), which quantifies covariate effects under the conditions in which they are strongest - either marginally or within subgroups defined by interactions - as well as covariate-representative tree roots (CRTRs) that provide interpretable visualizations of these conditions. Categorical and continuous outcomes are supported. This package is a fork of the R package ranger (main author: Marvin N. Wright), which implements random forests using an efficient C++ backend.

Experimental data for use with the MutSeqR vignette and examples. This dataset is taken from LeBlanc et al., 2022. 24 MutaMouse animals were exposed to one of three doses of benzo[a]pyrene or a vehicle control for 28 days by oral gavage. 28 days after the end of the exposure, bone marrow of the femurs was harvested from euthanized animals. DNA extraction was conducted via DNeasy Blood and Tissue kit. DNA samples were sequenced using TwinStrand's Duplex Sequencing on the Mouse Mutagenesis Panel at > 10,000 depth. The Mouse Mutagenesis Panel comprises 20 2.4kb genomic targets with one located on each mouse autosome (two on chromosome 1). Pre-processing of sequence reads was redone since publication using an updated version of TwinStrand's Mutagenesis App (v. 3.20.1) which produced tabular mutation data files for each sample. Data contained herein are only those required for running MutSeqR examples and vignette.