R-universe search: lineup

kbroman

lineup:Lining Up Two Sets of Measurements

Tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. Broman et al. (2015) <doi:10.1534/g3.115.019778>.

Maintained by Karl W Broman. Last updated 8 months ago.

60.0 match 4 stars 5.23 score 85 scripts

dicook

nullabor:Tools for Graphical Inference

Tools for visual inference. Generate null data sets and null plots using permutation and simulation. Calculate distance metrics for a lineup, and examine the distributions of metrics.

Maintained by Di Cook. Last updated 1 months ago.

18.0 match 57 stars 10.38 score 370 scripts 2 dependents

colintredoux

r4lineups:Statistical Inference on Lineup Fairness

Since the early 1970s eyewitness testimony researchers have recognised the importance of estimating properties such as lineup bias (is the lineup biased against the suspect, leading to a rate of choosing higher than one would expect by chance?), and lineup size (how many reasonable choices are in fact available to the witness? A lineup is supposed to consist of a suspect and a number of additional members, or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup measures are descriptive, in the first instance, but since the earliest articles in the literature researchers have recognised the importance of reasoning inferentially about them. This package contains functions to compute various properties of laboratory or police lineups, and is intended for use by researchers in forensic psychology and/or eyewitness testimony research. Among others, the r4lineups package includes functions for calculating lineup proportion, functional size, various estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity ratio, ROC curves for confidence x accuracy data and the degree of similarity of faces in a lineup.

Maintained by Colin Tredoux. Last updated 7 years ago.

38.0 match 2.58 score 38 scripts

lineupjs

lineupjs:'HTMLWidget' Wrapper of 'LineUp' for Visual Analysis of Multi-Attribute Rankings

'LineUp' is an interactive technique designed to create, visualize and explore rankings of items based on a set of heterogeneous attributes. This is a 'htmlwidget' wrapper around the JavaScript library 'LineUp.js'. It is designed to be used in 'R Shiny' apps and 'R Markddown' files. Due to an outdated 'webkit' version of 'RStudio' it won't work in the integrated viewer.

Maintained by Samuel Gratzl. Last updated 3 years ago.

crosstalk htmlwidget htmlwidgets lineup ranking shiny

17.9 match 55 stars 4.76 score 21 scripts

jimmyday12

fitzRoy:Easily Scrape and Process AFL Data

An easy package for scraping and processing Australia Rules Football (AFL) data. 'fitzRoy' provides a range of functions for accessing publicly available data from 'AFL Tables' <https://afltables.com/afl/afl_index.html>, 'Footy Wire' <https://www.footywire.com> and 'The Squiggle' <https://squiggle.com.au>. Further functions allow for easy processing, cleaning and transformation of this data into formats that can be used for analysis.

Maintained by James Day. Last updated 2 months ago.

6.2 match 134 stars 10.74 score 324 scripts

sportsdataverse

hoopR:Access Men's Basketball Play by Play Data

A utility to quickly obtain clean and tidy men's basketball play by play data. Provides functions to access live play by play and box score data from ESPN<https://www.espn.com> with shot locations when available. It is also a full NBA Stats API<https://www.nba.com/stats/> wrapper. It is also a scraping and aggregating interface for Ken Pomeroy's men's college basketball statistics website<https://kenpom.com>. It provides users with an active subscription the capability to scrape the website tables and analyze the data for themselves.

Maintained by Saiem Gilani. Last updated 1 years ago.

basketball college-basketball espn kenpom nba nba-analytics nba-api nba-data nba-statistics nba-stats nba-stats-api ncaa ncaa-basketball ncaa-bracket ncaa-players ncaa-ratings ncaam sportsdataverse

8.5 match 91 stars 6.93 score 261 scripts

jaseziv

worldfootballR:Extract and Clean World Football (Soccer) Data

Allow users to obtain clean and tidy football (soccer) game, team and player data. Data is collected from a number of popular sites, including 'FBref', transfer and valuations data from 'Transfermarkt'<https://www.transfermarkt.com/> and shooting location and other match stats data from 'Understat'<https://understat.com/>. It gives users the ability to access data more efficiently, rather than having to export data tables to files before being able to complete their analysis.

Maintained by Jason Zivkovic. Last updated 1 months ago.

fbref football football-data soccer-data sports-data transfermarkt understat

3.9 match 506 stars 9.89 score 516 scripts 2 dependents

sportsdataverse

wehoop:Access Women's Basketball Play by Play Data

A utility for working with women's basketball data. A scraping and aggregating interface for the WNBA Stats API <https://stats.wnba.com/> and ESPN's <https://www.espn.com> women's college basketball and WNBA statistics. It provides users with the capability to access the game play-by-plays, box scores, standings and results to analyze the data for themselves.

Maintained by Saiem Gilani. Last updated 8 months ago.

college-basketball espn espn-stats ncaa ncaa-basketball professional-basketball-data sportsdataverse wnba wnba-players wnba-stats womens-basketball

6.8 match 28 stars 5.36 score 54 scripts

ffverse

ffscrapr:API Client for Fantasy Football League Platforms

Helps access various Fantasy Football APIs by handling authentication and rate-limiting, forming appropriate calls, and returning tidy dataframes which can be easily connected to other data sources.

Maintained by Tan Ho. Last updated 5 months ago.

api-client fantasy-football fantasy-football-api

3.9 match 84 stars 8.07 score 178 scripts 1 dependents

billpetti

baseballr:Acquiring and Analyzing Baseball Data

Provides numerous utilities for acquiring and analyzing baseball data from online sources such as 'Baseball Reference' <https://www.baseball-reference.com/>, 'FanGraphs' <https://www.fangraphs.com/>, and the 'MLB Stats' API <https://www.mlb.com/>.

Maintained by Saiem Gilani. Last updated 4 months ago.

baseball pitchfx sabermetrics statcast

3.4 match 380 stars 8.98 score 582 scripts

cran

AdvancedBasketballStats:Advanced Basketball Statistics

Provides different functionalities and calculations used in the world of basketball to analyze the statistics of the players, the statistics of the teams, the statistics of the quintets and the statistics of the plays. For more details of the calculations included in the package can be found in the book Basketball on Paper written by Dean Oliver.

Maintained by Francisco Javier Cantero. Last updated 4 years ago.

12.5 match 2.00 score

capnrefsmmat

regressinator:Simulate and Diagnose (Generalized) Linear Models

Simulate samples from populations with known covariate distributions, generate response variables according to common linear and generalized linear model families, draw from sampling distributions of regression estimates, and perform visual inference on diagnostics from model fits.

Maintained by Alex Reinhart. Last updated 5 months ago.

statistics

3.6 match 4 stars 6.08 score 25 scripts

cran

BAwiR:Analysis of Basketball Data

Collection of tools to work with European basketball data. Functions available are related to friendly web scraping, data management and visualization. Data were obtained from <https://www.euroleaguebasketball.net/euroleague/>, <https://www.euroleaguebasketball.net/eurocup/> and <https://www.acb.com/>, following the instructions of their respectives robots.txt files, when available. Box score data are available for the three leagues. Play-by-play data are also available for the Spanish league. Methods for analysis include a population pyramid, 2D plots, circular plots of players' percentiles, plots of players' monthly/yearly stats, team heatmaps, team shooting plots, team four factors plots, cross-tables with the results of regular season games, maps of nationalities, combinations of lineups, possessions-related variables, timeouts, performance by periods, personal fouls and offensive rebounds. Please see Vinue (2020) <doi:10.1089/big.2018.0124> and Vinue (2024) <doi:10.1089/big.2023.0177>.

Maintained by Guillermo Vinue. Last updated 30 days ago.

7.7 match 1 stars 2.60 score

gpilgrim2670

SwimmeR:Data Import, Cleaning, and Conversions for Swimming Results

The goal of the 'SwimmeR' package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions. To that end 'SwimmeR' allows results to be read in from .html sources, like 'Hy-Tek' real time results pages, '.pdf' files, 'ISL' results, 'Omega' results, and (on a development basis) '.hy3' files. Once read in, 'SwimmeR' can convert swimming times (performances) between the computationally useful format of seconds reported to the '100ths' place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community. 'SwimmeR' can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', 'SCM', 'SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data. This is a developmental package, not yet mature.

Maintained by Greg Pilgrim. Last updated 2 years ago.

3.5 match 4 stars 4.53 score 17 scripts

ffverse

ffsimulator:Simulate Fantasy Football Seasons

Uses bootstrap resampling to run fantasy football season simulations supported by historical rankings and 'nflfastR' data, calculating optimal lineups, and returning aggregated results.

Maintained by Tan Ho. Last updated 5 months ago.

fantasy-football simulation

2.8 match 17 stars 5.17 score 44 scripts

tengmcing

autovi:Auto Visual Inference with Computer Vision Models

Provides automated visual inference of residual plots using computer vision models, facilitating diagnostic checks for classical normal linear regression models.

Maintained by Weihao Li. Last updated 4 months ago.

3.5 match 1 stars 4.13 score 16 scripts

paithiov909

shikakusphere:Miscellaneous Functions for Japanese Mahjong

A collection of miscellaneous functions for Japanese mahjong that wraps C++ sources of 'shanten-number' <https://github.com/tomohxx/shanten-number> and 'cmajiang' <https://github.com/TadaoYamaoka/cmajiang>.

Maintained by Akiru Kato. Last updated 16 days ago.

mahjong rcpp cpp

3.3 match 4 stars 3.41 score 5 scripts

yuerany

fullROC:Plot Full ROC Curves using Eyewitness Lineup Data

Enable researchers to adjust identification rates using the 1/(lineup size) method, generate the full receiver operating characteristic (ROC) curves, and statistically compare the area under the curves (AUC). References: Yueran Yang & Andrew Smith. (2022). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves". Behavior Research Methods. <doi:10.3758/s13428-022-01807-6>, Andrew Smith, Yueran Yang, & Gary Wells. (2020). "Distinguishing between investigator discriminability and eyewitness discriminability: A method for creating full receiver operating characteristic curves of lineup identification performance". Perspectives on Psychological Science, 15(3), 589-607. <doi:10.1177/1745691620902426>.

Maintained by Yueran Yang. Last updated 2 years ago.

5.4 match 2.00 score

openvolley

ovscout2:An R Shiny App for Scouting Volleyball Match Files

Functions for scouting volleyball match files.

Maintained by Ben Raymond. Last updated 10 months ago.

1.8 match 6 stars 3.78 score 8 scripts

cran

unitedR:Assessment and Evaluation of Formations in United

United is a software tool which can be downloaded at the following website <http://www.schroepl.net/pbm/software/united/>. In general, it is a virtual manager game for football teams. This package contains helpful functions for determining an optimal formation for a virtual match in United. E.g. knowing that the opponent has a strong defensive it is advisable to beat him in the midfield. Furthermore, this package contains functions for computing the optimal usage of hardness in a game.

Maintained by David Schindler. Last updated 5 years ago.

2.0 match 2.00 score 6 scripts

openvolley

ovscout:An R Shiny App for Editing, Syncing, and Scouting Volleyball Match Files

Functions for video synchronisation, scout file editing, and more from volleyball match files.

Maintained by Ben Raymond. Last updated 2 years ago.

1.8 match 2 stars 2.00 score 2 scripts

rwoldford

qqtest:Self Calibrating Quantile-Quantile Plots for Visual Testing

Provides the function qqtest which incorporates uncertainty in its qqplot display(s) so that the user might have a better sense of the evidence against the specified distributional hypothesis. qqtest draws a quantile quantile plot for visually assessing whether the data come from a test distribution that has been defined in one of many ways. The vertical axis plots the data quantiles, the horizontal those of a test distribution. The default behaviour generates 1000 samples from the test distribution and overlays the plot with shaded pointwise interval estimates for the ordered quantiles from the test distribution. A small number of independently generated exemplar quantile plots can also be overlaid. Both the interval estimates and the exemplars provide different comparative information to assess the evidence provided by the qqplot for or against the hypothesis that the data come from the test distribution (default is normal or gaussian). Finally, a visual test of significance (a lineup plot) can also be displayed to test the null hypothesis that the data come from the test distribution.

Maintained by Wayne Oldford. Last updated 5 years ago.

0.5 match 1 stars 4.18 score 30 scripts

kbroman

lineup2:Lining Up Two Sets of Measurements

Tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. This is a revised version of the 'lineup' package, to be more general and not tied to the 'qtl' package.

Maintained by Karl W Broman. Last updated 2 years ago.

cpp

0.5 match 3.85 score 14 scripts

anniesbooth

runexp:Softball Run Expectancy using Markov Chains and Simulation

Implements two methods of estimating runs scored in a softball scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical distribution using multinomial random simulation. Scores are based on player-specific input probabilities (out, single, double, triple, walk, and homerun). Optional inputs include probability of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether a player is "fast" (e.g. the player could stretch home). These probabilities may be calculated from common player statistics that are publicly available on team's webpages. Scores are evaluated based on a nine-player lineup and may be used to compare lineups, evaluate base scenarios, and compare the offensive potential of individual players. Manuscript forthcoming. See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for implementation of discrete Markov chains.

Maintained by Annie Sauer. Last updated 4 years ago.

0.8 match 1.00 score 1 scripts

paulowhite

wally:The Wally Calibration Plot for Risk Prediction Models

A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.

Maintained by Paul F. Blanche. Last updated 5 years ago.

0.5 match 1.00 score 7 scripts