Calculate the optimal sample size allocation that uses the minimum resources to achieve targeted statistical power in experiments. Perform power analyses with and without accommodating costs and budget. The designs cover single-level and multilevel experiments detecting main, mediation, and moderation effects (and some combinations). The references for the proposed methods include: (1) Shen, Z., & Kelcey, B. (2020). Optimal sample allocation under unequal costs in cluster-randomized trials. Journal of Educational and Behavioral Statistics, 45(4): 446-474. <doi:10.3102/1076998620912418>. (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for three-level multisite cluster-randomized trials. Journal of Research on Educational Effectiveness, 15 (1), 130-150. <doi:10.1080/19345747.2021.1953200>. (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in multisite randomized trials. The Journal of Experimental Education, 90(3), 693-711. <doi:10.1080/00220973.2020.1830361>. (4) Shen, Z., Leite, W., Zhang, H., Quan, J., & Kuang, H. (2025). Using ant colony optimization to identify optimal sample allocations in cluster-randomized trials. The Journal of Experimental Education, 93(1), 167-185. <doi:10.1080/00220973.2024.2306392>. (5) Shen, Z., Li, W., & Leite, W. (in press). Statistical power and optimal design for randomized controlled trials investigating mediation effects. Psychological Methods. <doi:10.1037/met0000698>. (6) Champely, S. (2020). pwr: Basic functions for power analysis (Version 1.3-0) [Software]. Available from <https://CRAN.R-project.org/package=pwr>.

Quickly create numeric matrices for machine learning algorithms that require them. It converts factor columns into onehot vectors.

Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters) first started as an open source project with support of Haag-Streit in 2010. It specifies basic functions that allow many visual field tests to be constructed. As of February 2022 it is fully implemented on the Haag-Streit Octopus 900 and CrewT ImoVifa ('Topcon Tempo') with partial implementations on the Centervue Compass, Kowa AP 7000 and Android phones. It also has a cousin: the R package visualFields', which has tools for analysing and manipulating visual field data.

This package provides a random forest based implementation of the method described in Chapter 7.1.2 (Regression model based anomaly detection) of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It works as follows: Each numeric variable is regressed onto all other variables by a random forest. If the scaled absolute difference between observed value and out-of-bag prediction of the corresponding random forest is suspiciously large, then a value is considered an outlier. The package offers different options to replace such outliers, e.g. by realistic values found via predictive mean matching. Once the method is trained on a reference data, it can be applied to new data.

Maximum homogeneity clustering algorithm for one-dimensional data described in W. D. Fisher (1958) <doi:10.1080/01621459.1958.10501479> via dynamic programming.

Estimates win ratio or Mann-Whitney parameter for two group comparisons using ordered composite endpoints with right censoring as described in Follmann, Fay, Hamasaki, and Evans (2020)<doi:10.1002/sim.7890>.

Determine the sea area where the fishing boat operates. The latitude and longitude of geographic coordinates are used to match oceanic areas and economic sea areas. You can plot the distribution map with dotplot() function. Please refer to Flanders Marine Institute (2020) <doi:10.14284/403>.

Retrieve data from the Our World in Data (OWID) Chart API <https://docs.owid.io/projects/etl/api/>. OWID provides public access to more than 5,000 charts focusing on global problems such as poverty, disease, hunger, climate change, war, existential risks, and inequality.

Robust multi-criteria land-allocation optimization that explicitly accounts for the uncertainty of the indicators in the objective function. Solves the problem of allocating scarce land to various land-use options with regard to multiple, coequal indicators. The method aims to find the land allocation that represents the indicator composition with the best possible trade-off under uncertainty. optimLanduse includes the actual optimization procedure as described by Knoke et al. (2016) <doi:10.1038/ncomms11877> and the post-hoc calculation of the portfolio performance as presented by Gosling et al. (2020) <doi:10.1016/j.jenvman.2020.110248>.

This package provides programmatic access to the Open Experience Sampling Method ('openESM') database (<https://openesmdata.org>), a collection of harmonized experience sampling datasets. The package enables researchers to discover, download, and work with the datasets while ensuring proper citation and license compliance.

An interface between R and the OSRM API. OSRM is a routing service based on OpenStreetMap data. See <http://project-osrm.org/> for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometric distance).

This package provides a simple wrapper for the Octopus Energy API <https://developer.octopus.energy/docs/api/>. It handles authentication, by storing a provided API key and meter details. Implemented endpoints include products for viewing tariff details and consumption for viewing meter consumption data.

This package implements the algorithm in Chen, Wang and Samworth (2020) <arxiv:2003.03668> for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) <doi:10.1093/biomet/asq010>, Xie and Siegmund (2013) <doi:10.1214/13-AOS1094> and Chan (2017) <doi:10.1214/17-AOS1546>.

This package provides a mutable Signal object can report changes to its state, clients could register functions so that they are called whenever the signal is emitted. The signal could be emitted, disconnected, blocked, unblocked, and buffered.

Trains per-horizon probabilistic ensembles from a univariate time series. It supports rpart', glmnet', and kNN engines with flexible residual distributions and heteroscedastic scale models, weighting variants by calibration-aware scores. A Gaussian/t copula couples the marginals to simulate joint forecast paths, returning quantiles, means, and step increments across horizons.

Estimates rates for continuous character evolution under Brownian motion and a new set of Ornstein-Uhlenbeck based Hansen models that allow both the strength of the pull and stochastic motion to vary across selective regimes. Beaulieu et al (2012).

Identifies an optimal transformation of a surrogate marker such that the proportion of treatment effect explained can be inferred based on the transformation of the surrogate and nonparametrically estimates two model-free quantities of this proportion. Details are described in Wang et al (2020) <doi:10.1093/biomet/asz065>.

This package provides a penalized regression framework that can simultaneously estimate the optimal treatment strategy and identify important variables. Appropriate for either censored or uncensored continuous response.

Developed to help researchers who need to model the kinetics of carbon dioxide (CO2) production in alcoholic fermentation of wines, beers and other fermented products. The following models are available for modeling the carbon dioxide production curve as a function of time: 5PL, Gompertz and 4PL. This package has different functions, which applied can: perform the modeling of the data obtained in the fermentation and return the coefficients, analyze the model fit and return different statistical metrics, and calculate the kinetic parameters: Maximum production of carbon dioxide; Maximum rate of production of carbon dioxide; Moment in which maximum fermentation rate occurs; Duration of the latency phase for carbon dioxide production; Carbon dioxide produced until maximum fermentation rate occurs. In addition, a function that generates graphs with the observed and predicted data from the models, isolated and combined, is available. Gava, A., Borsato, D., & Ficagna, E. (2020)."Effect of mixture of fining agents on the fermentation kinetics of base wine for sparkling wine production: Use of methodology for modeling". <doi:10.1016/j.lwt.2020.109660>.

Conduct sensitivity analysis of omitted variable bias in linear econometric models using the methodology presented in Basu (2025) <doi:10.2139/ssrn.4704246>.

Collects a list of your third party R packages, and scans them with the OSS Index provided by Sonatype', reporting back on any vulnerabilities that are found in the third party packages you use.

Interface to OpenStreetMap API for fetching and saving data from/to the OpenStreetMap database (<https://wiki.openstreetmap.org/wiki/API_v0.6>).

This package provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data. The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift. See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.

Computes the pdf, cdf, quantile function, hazard function and generating random numbers for Odd log-logistic family (OLL-G). This family have been developed by different authors in the recent years. See Alizadeh (2019) <doi:10.31801/cfsuasmas.542988> for example.