This package provides a key-value store data structure. The keys are integers and the values can be any R object. This is like a list but indexed by a set of integers, not necessarily contiguous and possibly negative. The implementation uses a R6 class. These containers are not faster than lists but their usage can be more convenient for certain situations.

This package provides a small collection of various network data sets, to use with the igraph package: the Enron email network, various food webs, interactions in the immunoglobulin protein, the karate club network, Koenigsberg's bridges, visuotactile brain areas of the macaque monkey, UK faculty friendship network, domestic US flights network, etc.

We provide data sets used in the textbook "Introduction to Sports Analytics using R" by Elmore and Urbaczweski (2025).

Takes in vivo toxicokinetic concentration-time data and fits parameters of 1-compartment and 2-compartment models for each chemical. These methods are described in detail in "Informatics for Toxicokinetics" (2025).

Develops a General Equilibrium (GE) Model, which estimates key variables such as wages, the number of residents and workers, the prices of the floor space, and its distribution between commercial and residential use, as in Ahlfeldt et al., (2015) <doi:10.3982/ECTA10876>. By doing so, the model allows understanding the economic influence of different urban policies.

This package provides a pipeline to process nominal mass spectrometry data to create .msp files for untargeted analyses.

Enable user to find the IP addresses which are used as VPN anonymizer, open proxies, web proxies and Tor exits. The package lookup the proxy IP address from IP2Proxy BIN Data file. You may visit <https://lite.ip2location.com> for free database download.

It provides a general framework to analyse dependence between point processes in time. It includes parametric and non-parametric tests to study independence, and functions for generating and analysing different types of dependence.

Calculates various chance-corrected agreement coefficients (CAC) among 2 or more raters are provided. Among the CAC coefficients covered are Cohen's kappa, Conger's kappa, Fleiss kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 coefficients, and Krippendorff's alpha. Multiple sets of weights are proposed for computing weighted analyses. All of these statistical procedures are described in details in Gwet, K.L. (2014,ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, Advanced Analytics, LLC.

We use the ISR to handle with PCA-based missing data with high correlation, and the DISR to handle with distributed PCA-based missing data. The philosophy of the package is described in Guo G. (2024) <doi:10.1080/03610918.2022.2091779>.

Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.

It provides multiple functions that are useful for ecological research and teaching statistics to ecologists. It is based on data analysis courses offered at the Instituto de Ecologà a AC (INECOL). For references and published evidence see, Manrique-Ascencio, et al (2024) <doi:10.1111/gcb.17282>, Manrique-Ascencio et al (2024) <doi:10.1111/plb.13683>, Ruiz-Guerra et al(2017) <doi:10.17129/botsci.812>, Juarez-Fragoso et al (2024) <doi:10.1007/s10980-024-01809-z>, Papaqui-Bello et al (2024) <doi:10.13102/sociobiology.v71i2.10503>.

Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: Mean', LOCF', Interpolation', Moving Average', Seasonal Decomposition', Kalman Smoothing on Structural Time Series models', Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) <doi:10.32614/RJ-2017-009>.

Advanced fuzzy logic based techniques are implemented to compute the similarity among different objects or items. Typically, application areas consist of transforming raw data into the corresponding advanced fuzzy logic representation and determining the similarity between two objects using advanced fuzzy similarity techniques in various fields of research, such as text classification, pattern recognition, software projects, decision-making, medical diagnosis, and market prediction. Functions are designed to compute the membership, non-membership, hesitant-membership, indeterminacy-membership, and refusal-membership for the input matrices. Furthermore, it also includes a large number of advanced fuzzy logic based similarity measure functions to compute the Intuitionistic fuzzy similarity (IFS), Pythagorean fuzzy similarity (PFS), and Spherical fuzzy similarity (SFS) between two objects or items based on their fuzzy relationships. It also includes working examples for each function with sample data sets.

Graphical User Interface allowing to determine the concentration in the sample in CFU per mL or in number of copies per mL provided to qPCR results after with or without PMA treatment. This package is simply to use because no knowledge in R commands is necessary. A graphic represents the standard curve, and a table containing the result for each sample is created.

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>.

Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participantâ s reaction time (in milliseconds). A sample dataset (titled sampledata') is included in this package to practice with.

Implementation of a KL-based scoring rule to assess the quality of different missing value imputations in the broad sense as introduced in Michel et al. (2021) <arXiv:2106.03742>.

This package provides a tiny parser to extract mass spectra data and metadata table of mass spectrometry acquisition properties from mzML, mzXML and netCDF files introduced in <doi:10.1021/acs.jproteome.2c00120>.

Supplements for a book, "iTOS" = "Introduction to the Theory of Observational Studies." Data sets are aHDL from Rosenbaum (2023a) <doi:10.1111/biom.13558> and bingeM from Rosenbaum (2023b) <doi:10.1111/biom.13921>. The function makematch() uses two-criteria matching from Zhang et al. (2023) <doi:10.1080/01621459.2021.1981337> to create the matched data bingeM from binge'. The makematch() function also implements optimal matching (Rosenbaum (1989) <doi:10.2307/2290079>) and matching with fine or near-fine balance (Rosenbaum et al. (2007) <doi:10.1198/016214506000001059> and Yang et al (2012) <doi:10.1111/j.1541-0420.2011.01691.x>). The book makes use of two other R packages, weightedRank and tightenBlock'.

This package provides functions to access data from public RESTful APIs including Nager.Date', World Bank API', and REST Countries API', retrieving real-time or historical data related to Indonesia, such as holidays, economic indicators, and international demographic and geopolitical indicators. The package also includes a curated collection of open datasets focused on Indonesia, covering topics such as consumer prices, poverty probability, food prices by region, tourism destinations, and minimum wage statistics. The package supports reproducible research and teaching by integrating reliable international APIs and structured datasets from public, academic, and government sources. For more information on the APIs, see: Nager.Date <https://date.nager.at/Api>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, and REST Countries API <https://restcountries.com/>.

Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures. For details, see Anderson et al. (2011) <doi:10.1109/TSP.2011.2181836> and Lee et al. (2007) <doi:10.1016/j.sigpro.2007.01.010>.

We propose the inverse probability-of-censoring weighted (IPCW) Kendall's tau to measure the association of the survival trait with biomarkers and Kendall's partial correlation to reflect the relationship of the survival trait with interaction variable conditional on main effects, as described in Wang and Chen (2020) <doi:10.1093/bioinformatics/btaa017>.

This package provides new imputation methods for the mice package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess <doi:10.1080/03610918.2014.911894>.