Compare C-statistics (concordance statistics) between two survival models, using either bootstrap resampling (Harrell's C) or Uno's C with perturbation-resampling (from the survC1 package). Returns confidence intervals and a p-value for the difference in C-statistics. Useful for evaluating and comparing predictive performance of survival models. Methods implemented for Uno's C are described in Uno et al. (2011) <doi:10.1002/sim.4154>.

Graphically display the (causal) effect of a continuous variable on a time-to-event outcome using multiple different types of plots based on g-computation. Those functions include, among others, survival area plots, survival contour plots, survival quantile plots and 3D surface plots. Due to the use of g-computation, all plot allow confounder-adjustment naturally. For details, see Robin Denz, Nina Timmesfeld (2023) <doi:10.1097/EDE.0000000000001630>.

This package provides a function toolkit to facilitate reproducible RNA-Seq Differential Gene Expression (DGE) analysis (Law (2015) <doi:10.12688/f1000research.9005.3>). The tools include both analysis work-flow and utility functions: mapping/unit conversion, count normalization, accounting for unknown covariates, and more. This is a complement/cohort to the DGEobj package that provides a flexible container to manage and annotate Differential Gene Expression analysis results.

Fire behavior prediction models, including the Scott & Reinhardt's (2001) Rothermel Wildland Fire Modelling System <DOI:10.2737/RMRS-RP-29> and Alexander et al.'s (2006) Crown Fire Initiation & Spread model <DOI:10.1016/j.foreco.2006.08.174>. Also contains sample datasets, estimation of fire behavior prediction model inputs (e.g., fuel moisture, canopy characteristics, wind adjustment factor), results visualization, and methods to estimate fire weather hazard.

This package provides access to low-level operating system mechanisms for performing atomic operations on shared data structures. Mutexes provide shared and exclusive locks. Semaphores act as counters. Message queues move text strings from one process to another. All these interprocess communication (IPC) tools can optionally block with or without a timeout. Implemented using the cross-platform boost C++ library <https://www.boost.org/doc/libs/release/libs/interprocess/>.

Rcpp implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations. There is also a function to handle time varying parameters. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.

Obtain information on peak flow data from the National River Flow Archive (NRFA) in the United Kingdom, either from the Peak Flow Dataset files <https://nrfa.ceh.ac.uk/data/peak-flow-dataset> once these have been downloaded to the user's computer or using the NRFA's API. These files are in a format suitable for direct use in the WINFAP software, hence the name of the package.

Explore, diagnose, and compare variant calls using filters. The VariantTools package supports a workflow for loading data, calling single sample variants and tumor-specific somatic mutations or other sample-specific variant types (e.g., RNA editing). Most of the functions operate on alignments (BAM files) or datasets of called variants. The user is expected to have already aligned the reads with a separate tool, e.g., GSNAP via gmapR.

The scRepertoire package was built to process data derived from the 10x Genomics Chromium Immune Profiling for both TCR and Ig enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. It also allows for general analysis of single-cell clonotype information without the use of expression information. The package functions as a wrapper for Startrac and powerTCR R packages.

This package provides a Python 3 ported version of Python 2.7’s random module. It has also been back-ported to work in Python 2.6.

In Python 3, the implementation of randrange() was changed, so that even with the same seed you get different sequences in Python 2 and 3.

This package closes that gap, allowing stable random number generation between the different Python versions.

Calculates the optimal price of assets (such as airline flight seats, hotel room bookings) whose value becomes zero after a fixed ``expiry date''. Assumes potential customers arrive (possibly in groups) according to a known inhomogeneous Poisson process. Also assumes a known time-varying elasticity of demand (price sensitivity) function. Uses elementary techniques based on ordinary differential equations. Uses the package deSolve to effect the solution of these differential equations.

Colour vision models, colour spaces and colour thresholds. Provides flexibility to build user-defined colour vision models for n number of photoreceptor types. Includes Vorobyev & Osorio (1998) Receptor Noise Limited models <doi:10.1098/rspb.1998.0302>, Chittka (1992) colour hexagon <doi:10.1007/BF00199331>, and Endler & Mielke (2005) model <doi:10.1111/j.1095-8312.2005.00540.x>. Models have been extended to accept any number of photoreceptor types.

Estimation, model selection and goodness-of-fit of (1) factor copula models for mixed continuous and discrete data in Kadhem and Nikoloulopoulos (2021) <doi:10.1111/bmsp.12231>; (2) bi-factor and second-order copula models for item response data in Kadhem and Nikoloulopoulos (2023) <doi:10.1007/s11336-022-09894-2>; (3) factor tree copula models for item response data in Kadhem and Nikoloulopoulos (2022) <arXiv:2201.00339>.

Machine learning based package to predict anti-angiogenic peptides using heterogeneous sequence descriptors. AntAngioCOOL exploits five descriptor types of a peptide of interest to do prediction including: pseudo amino acid composition, k-mer composition, k-mer composition (reduced alphabet), physico-chemical profile and atomic profile. According to the obtained results, AntAngioCOOL reached to a satisfactory performance in anti-angiogenic peptide prediction on a benchmark non-redundant independent test dataset.

Models for detecting concreteness in natural language. This package is built in support of Yeomans (2021) <doi:10.1016/j.obhdp.2020.10.008>, which reviews linguistic models of concreteness in several domains. Here, we provide an implementation of the best-performing domain-general model (from Brysbaert et al., (2014) <doi:10.3758/s13428-013-0403-5>) as well as two pre-trained models for the feedback and plan-making domains.

Implementation of the scaling functions presented in "General statistical scaling laws for stability in ecological systems" by Clark et al in Ecology Letters <DOI:10.1111/ele.13760>. Includes functions for extrapolating variability, resistance, and resilience across spatial and ecological scales, as well as a basic simulation function for producing time series, and a regression routine for generating unbiased parameter estimates. See the main text of the paper for more details.

Addresses tasks along the pipeline from raw data to analysis and visualization for eye-tracking data. Offers several popular types of analyses, including linear and growth curve time analyses, onset-contingent reaction time analyses, as well as several non-parametric bootstrapping approaches. For references to the approach see Mirman, Dixon & Magnuson (2008) <doi:10.1016/j.jml.2007.11.006>, and Barr (2008) <doi:10.1016/j.jml.2007.09.002>.

Backends implementing the Future API <doi:10.32614/RJ-2021-048>, as defined by the future package, should use the tests provided by this package to validate that they meet the minimal requirements of the Future API. The tests can be performed easily from within R or from outside of R from the command line making it straightforward to include them in package tests and in Continuous Integration (CI) pipelines.

Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) <https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf>; Haralick et al. (1973) <doi:10.1109/TSMC.1973.4309314>) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix.

Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018) <doi:10.1080/10543406.2017.1377728> provide two theoretical methods (influence function and local influence) based on the area under the receiver operating characteristic curve (AUC) to quantify the numerical impact of each observation to the overall AUC. Alternative graphical tools, cumulative lift charts, are proposed to reveal the existences and approximate locations of those influential observations through data visualization.

This package provides a shiny app to visualize the knowledge networks for the code concepts. Using co-occurrence matrices of EHR codes from Veterans Affairs (VA) and Massachusetts General Brigham (MGB), the knowledge extraction via sparse embedding regression (KESER) algorithm was used to construct knowledge networks for the code concepts. Background and details about the method can be found at Chuan et al. (2021) <doi:10.1038/s41746-021-00519-z>.

This package provides GIS and map utilities, plus additional modeling tools for developing cellular automata, dynamic raster models, and agent based models in SpaDES'. Included are various methods for spatial spreading, spatial agents, GIS operations, random map generation, and others. See ?SpaDES.tools for an categorized overview of these additional tools. The suggested package NLMR can be installed from the following repository: (<https://PredictiveEcology.r-universe.dev>).

The German national forest inventory uses angle count sampling, a sampling method first published as `Bitterlich, W.: Die Winkelzählmessung. Allgemeine Forst- und Holzwirtschaftliche Zeitung, 58. Jahrg., Folge 11/12 vom Juni 1947` and extended by Grosenbaugh (<https://academic.oup.com/jof/article-abstract/50/1/32/4684174>) as probability proportional to size sampling. When plots are located near stand boundaries, their sizes and hence their probabilities need to be corrected.

The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations.