Games that can be played in the R console. Includes coin flip, hangman, jumble, magic 8 ball, poker, rock paper scissors, shut the box, spelling bee, and 2048.

Supports maximum likelihood inference for the Pearson VII distribution with shape parameter 3/2 and free location and scale parameters. This distribution is relevant when estimating the velocity of processive motor proteins with random detachment.

Simulate the dynamic of lion populations using a specific Individual-Based Model (IBM) compiled in C.

Power analysis and sample size determination for moderation, mediation, and moderated mediation in models fitted by structural equation modelling using the lavaan package by Rosseel (2012) <doi:10.18637/jss.v048.i02> or by multiple regression. The package manymome by Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z> is used to specify the indirect paths or conditional indirect paths to be tested.

This package implements the pcgen algorithm, which is a modified version of the standard pc-algorithm, with specific conditional independence tests and modified orientation rules. pcgen extends the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of direct genetic effects.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

Implementation of PCMRS (Partial Credit Model with Response Styles) as proposed in by Tutz, Schauberger and Berger (2018) <doi:10.1177/0146621617748322> . PCMRS is an extension of the regular partial credit model. PCMRS allows for an additional person parameter that characterizes the response style of the person. By taking the response style into account, the estimates of the item parameters are less biased than in partial credit models.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Household questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).

This package provides a simple interface for extracting various elements from the publicly available PubMed XML files, incorporating PubMed's regular updates, and combining the data with the NIH Open Citation Collection. See Schoenbachler and Hughey (2021) <doi:10.7717/peerj.11071>.

Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities.

This package provides a profile boosting framework for feature selection in parametric models. It offers a unified interface pboost() and several wrapped models, including linear model, generalized linear models, quantile regression, Cox proportional hazards model, beta regression, spatial auto-regressive models.

This package provides a set of Study Data Tabulation Model (SDTM) datasets constructed by modifying the pharmaversesdtm package to meet J&J Innovative Medicine's standard data structure for Clinical and Statistical Programming.

Manipulation and analysis of phylogenetically simulated data sets and phylogenetically based analyses using GLS.

This package provides functions and data sets for the text Probability and Statistics with R, Second Edition.

Analyze spatial phylogenetic diversity patterns. Use your data on an evolutionary tree and geographic distributions of the terminal taxa to compute diversity and endemism metrics, test significance with null model randomization, analyze community turnover and biotic regionalization, and perform spatial conservation prioritizations. All functions support quantitative community data in addition to binary data.

This package provides path_chain class and functions, which facilitates loading and saving directory structure in YAML configuration files via config package. The file structure you created during exploration can be transformed into legible section in the config file, and then easily loaded for further usage.

This package provides a unified, computation-friendly framework for penalized principal machines (P2M), a class of sparse sufficient dimension reduction (SDR) estimators for regression and binary classification. Principal machines (PM) estimate the central subspace by solving a family of convex-loss problems over several cutoffs; their penalized counterparts (P2M) add a row-group sparsity penalty so that dimension reduction and variable selection are performed simultaneously. All estimators are fitted by a single group coordinate descent (GCD) algorithm that accommodates least squares, logistic, asymmetric least squares, L2-hinge, hinge (support vector machine, SVM) and quantile losses, together with the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD) penalty and the minimax concave penalty (MCP). Methods are described in Li, Artemiou and Li (2011) <doi:10.1214/11-AOS932>, Shin and Artemiou (2017) <doi:10.1016/j.csda.2016.12.003>, Artemiou, Dong and Shin (2021) <doi:10.1016/j.patcog.2020.107768> and Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>.

Support for a variety of commonly used precision agriculture operations. Includes functions to download and process raw satellite images from Sentinel-2 <https://documentation.dataspace.copernicus.eu/APIs/OData.html>. Includes functions that download vegetation index statistics for a given period of time, without the need to download the raw images <https://documentation.dataspace.copernicus.eu/APIs/SentinelHub/Statistical.html>. There are also functions to download and visualize weather data in a historical context. Lastly, the package also contains functions to process yield monitor data. These functions can build polygons around recorded data points, evaluate the overlap between polygons, clean yield data, and smooth yield maps.

This package provides functions that facilitate the elaboration of population pyramids.

Provide multinomial design methods under intersection-union test (IUT) and union-intersection test (UIT) scheme for Phase II trial. The design types include : Minimax (minimize the maximum sample size), Optimal (minimize the expected sample size), Admissible (minimize the Bayesian risk) and Maxpower (maximize the exact power level).

Location- and scale-invariant Box-Cox and Yeo-Johnson power transformations allow for transforming variables with distributions distant from 0 to normality. Transformers are implemented as S4 objects. These allow for transforming new instances to normality after optimising fitting parameters on other data. A test for central normality allows for rejecting transformations that fail to produce a suitably normal distribution, independent of sample number.

Analyse prescription drug deliveries to calculate several indicators of polypharmacy corresponding to the various definitions found in the literature. Bjerrum, L., Rosholm, J. U., Hallas, J., & Kragstrup, J. (1997) <doi:10.1007/s002280050329>. Chan, D.-C., Hao, Y.-T., & Wu, S.-C. (2009a) <doi:10.1002/pds.1712>. Fincke, B. G., Snyder, K., Cantillon, C., Gaehde, S., Standring, P., Fiore, L., ... Gagnon, D.R. (2005) <doi:10.1002/pds.966>. Hovstadius, B., Astrand, B., & Petersson, G. (2009) <doi:10.1186/1472-6904-9-11>. Hovstadius, B., Astrand, B., & Petersson, G. (2010) <doi:10.1002/pds.1921>. Kennerfalk, A., Ruigómez, A., Wallander, M.-A., Wilhelmsen, L., & Johansson, S. (2002) <doi:10.1345/aph.1A226>. Masnoon, N., Shakib, S., Kalisch-Ellett, L., & Caughey, G. E. (2017) <doi:10.1186/s12877-017-0621-2>. Narayan, S. W., & Nishtala, P. S. (2015) <doi:10.1007/s40801-015-0020-y>. Nishtala, P. S., & Salahudeen, M. S. (2015) <doi:10.1159/000368191>. Park, H. Y., Ryu, H. N., Shim, M. K., Sohn, H. S., & Kwon, J. W. (2016) <doi:10.5414/cp202484>. Veehof, L., Stewart, R., Haaijer-Ruskamp, F., & Jong, B. M. (2000) <doi:10.1093/fampra/17.3.261>.

This package provides tools for the practical management of financial portfolios: backtesting investment and trading strategies, computing profit/loss and returns, analysing trades, handling lists of transactions, reporting, and more. The package provides a small set of reliable, efficient and convenient tools for processing and analysing trade/portfolio data. The manual provides all the details; it is available from <https://enricoschumann.net/R/packages/PMwR/manual/PMwR.html>. Examples and descriptions of new features are provided at <https://enricoschumann.net/notes/PMwR/>.

This package provides a comprehensive framework for planning and executing analyses in R. It provides a structured approach to running the same function multiple times with different arguments, executing multiple functions on the same datasets, and creating systematic analyses across multiple strata or variables. The framework is particularly useful for applying the same analysis across multiple strata (e.g., locations, age groups), running statistical methods on multiple variables (e.g., exposures, outcomes), generating multiple tables or graphs for reports, and creating systematic surveillance analyses. Key features include efficient data management, structured analysis planning, flexible execution options, built-in debugging tools, and hash-based caching.