Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). The package provides implementations of optimisation heuristics (Differential Evolution, Genetic Algorithms, Particle Swarm Optimisation, Simulated Annealing and Threshold Accepting), and other optimisation tools, such as grid search and greedy search. There are also functions for the valuation of financial instruments such as bonds and options, for portfolio selection and functions that help with stochastic simulations.

In the working paper titled "Why You Should Never Use the Hodrick-Prescott Filter", James D. Hamilton proposes a new alternative to economic time series filtering. The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) <doi:10.3386/w23429>.

Extends package nat (NeuroAnatomy Toolbox) by providing objects and functions for handling template brains.

The aim of nosoi (pronounced no.si) is to provide a flexible agent-based stochastic transmission chain/epidemic simulator (Lequime et al. Methods in Ecology and Evolution 11:1002-1007). It is named after the daimones of plague, sickness and disease that escaped Pandora's jar in the Greek mythology. nosoi is able to take into account the influence of multiple variable on the transmission process (e.g. dual-host systems (such as arboviruses), within-host viral dynamics, transportation, population structure), alone or taken together, to create complex but relatively intuitive epidemiological simulations.

This package provides a comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - scatter plot to visualize local inconsistency (Wilson et al., 2026) <doi:10.1017/rsm.2026.10082>; - league table with network meta-analysis results; - comparison-adjusted funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for poset (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>; - network meta-regression with a single continuous or binary covariate (Kwarteng et al., 2026) <doi:10.21203/rs.3.rs-8235913/v1>; - subgroup network meta-analysis.

This package provides a set of functions providing the implementation of the network meta-analysis model with dose-response relationships, predicted values of the fitted model and dose-response plots in a frequentist way.

The number of distinct alleles observed in a DNA mixture is informative of the number of contributors to the mixture. The package provides methods for computing the probability distribution of the number of distinct alleles in a mixture for a given set of allele frequencies. The mixture contributors may be related according to a provided pedigree.

Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in Blume et al. (2016) <doi: 10.1016/j.mex.2016.05.006> and van Someren et al. (1999) <doi: 10.3109/07420529908998724>. Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days.

This package contains methods described by Dennis Helsel in his book "Statistics for Censored Environmental Data using Minitab and R" (2011) and courses and videos at <https://practicalstats.com>. This package incorporates functions of NADA and adds new functionality.

Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided. Version 1.1.0 includes high-performance C++ implementations via Rcpp'/'RcppArmadillo for 5-15x faster MCMC sampling, along with modern ggplot2'-based visualizations with colorblind-friendly palettes.

This package provides a fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.

This package provides a comprehensive toolkit for calculating and visualizing Nitrogen Use Efficiency (NUE) indicators in agricultural research. The package implements 23 parameters categorized into fertilizer-based, plant-based, soil-based, isotope-based, ecology-based, and system-based indicators based on Congreves et al. (2021) <doi:10.3389/fpls.2021.637108>. Key features include vectorized calculations for paired-plot experimental designs, batch processing capabilities for handling large datasets, and built-in visualization tools using ggplot2'. Designed to streamline the workflow from raw agronomic data to publication-ready metrics and plots.

This package contains data, code, and figures from Hill et al. 2018a (Journal of Experimental Marine Biology and Ecology; <DOI: 10.1016/j.jembe.2018.07.006>) and Hill et al. 2018b (Data In Brief <DOI: 10.1016/j.dib.2018.09.133>). Datasets document plant allometry, stem heights, nutrient and stable isotope content, and sediment denitrification enzyme assays. The data and analysis offer an examination of nitrogen uptake and allocation in two salt marsh plant species.

Generates LaTeX code for drawing well-formatted neural network diagrams with TikZ'. Users have to define number of neurons on each layer, and optionally define neuron connections they would like to keep or omit, layers they consider to be oversized and neurons they would like to draw with lighter color. They can also specify the title of diagram, color, opacity of figure, labels of layers, input and output neurons. In addition, this package helps to produce LaTeX code for drawing activation functions which are crucial in neural network analysis. To make the code work in a LaTeX editor, users need to install and import some TeX packages including TikZ in the setting of TeX file.

This package provides functions for probability and non-probability sampling design, sample selection, and population estimation tailored to natural resource management. Probability methods include simple random sampling, stratified sampling, systematic sampling, cluster sampling, and probability-proportional-to-size sampling. Non-probability methods include convenience, judgement-based, and quota sampling. Estimation functions cover means, totals, ratio estimators, regression estimators, and the unequal-probability estimator of Horvitz and Thompson (1952, <doi:10.2307/2280784>) for unequal-probability designs. Utilities support biomass, soil-loss, and carbon-stock estimation from field plots. Spatial extensions provide random, systematic, stratified, and raster-weighted sampling within geographic polygons using the sf and terra packages, with extraction of remote-sensing covariates at sample locations. Applications include forest inventory, soil erosion monitoring, watershed studies, and ecological field surveys.

Sends queries to a specified Neo4J graph database, capturing results in a dataframe where appropriate. Other useful functions for the importing and management of data on the Neo4J server and basic local server admin.

Calculates spatial pattern analysis using a T-square sample procedure. This method is based on two measures "x" and "y". "x" - Distance from the random point to the nearest individual. "y" - Distance from individual to its nearest neighbor. This is a methodology commonly used in phytosociology or marine benthos ecology to analyze the species distribution (random, uniform or clumped patterns). Ludwig & Reynolds (1988, ISBN:0471832359).

This package provides tools for working with nonlinear least squares problems. For the estimation of models reliable and robust tools than nls(), where the the Gauss-Newton method frequently stops with singular gradient messages. This is accomplished by using, where possible, analytic derivatives to compute the matrix of derivatives and a stabilization of the solution of the estimation equations. Tools for approximate or externally supplied derivative matrices are included. Bounds and masks on parameters are handled properly.

Cleans and Normalizes FLUOstar DBF and DAT Files obtained from liposome flux assays. Users should verify extended usage of the package on files from other assay types.

Fits non-homogeneous Markov multistate models and misclassification-type hidden Markov models in continuous time to intermittently observed data. Implements the methods in Titman (2011) <doi:10.1111/j.1541-0420.2010.01550.x>. Uses direct numerical solution of the Kolmogorov forward equations to calculate the transition probabilities.

nlmixr2 often has long runtimes. A pipeline toolkit tailored to nlmixr2 workflows leverages targets and nlmixr2 to ease reproducible workflows. nlmixr2targets ensures minimal rework in model development with nlmixr2 and targets by simplifying and standardizing models and datasets.

Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are used, one based on the count matrix, and one using the alignment BAM files directly. Contains several options for every step of the process, as well as tools to quality check and assess the stability of output.

Ensemble functions for outlier/anomaly detection. There is a new ensemble method proposed using Item Response Theory. Existing outlier ensemble methods from Schubert et al (2012) <doi:10.1137/1.9781611972825.90>, Chiang et al (2017) <doi:10.1016/j.jal.2016.12.002> and Aggarwal and Sathe (2015) <doi:10.1145/2830544.2830549> are also included.

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> as well as extensions for multimodal distributions proposed by Alcaraz-Peñalba, A., Franco-Pereira, A., and Pardo, M.C. (2025) <doi:10.1007/s10182-025-00545-2>.