Showing 14 of total 14 results (show query)
xrobin
pROC:Display and Analyze ROC Curves
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
Maintained by Xavier Robin. Last updated 5 months ago.
bootstrappingcovariancehypothesis-testingmachine-learningplotplottingrocroc-curvevariancecpp
125 stars 15.18 score 16k scripts 445 dependents
indrajeetpatil
ggstatsplot:'ggplot2' Based Plots with Statistical Details
Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests included in the plots themselves. It provides an easier syntax to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms, dot plots, dot-and-whisker plots) or categorical (pie and bar charts) data. Currently, it supports the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian versions of t-test/ANOVA, correlation analyses, contingency table analysis, meta-analysis, and regression analyses. References: Patil (2021) <doi:10.21105/joss.03236>.
Maintained by Indrajeet Patil. Last updated 1 months ago.
bayes-factorsdatasciencedatavizeffect-sizeggplot-extensionhypothesis-testingnon-parametric-statisticsregression-modelsstatistical-analysis
2.1k stars 14.46 score 3.0k scripts 1 dependents
spatstat
spatstat.explore:Exploratory Data Analysis for the 'spatstat' Family
Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Maintained by Adrian Baddeley. Last updated 13 days ago.
cluster-detectionconfidence-intervalshypothesis-testingk-functionroc-curvesscan-statisticssignificance-testingsimulation-envelopesspatial-analysisspatial-data-analysisspatial-sharpeningspatial-smoothingspatial-statistics
1 stars 10.18 score 67 scripts 150 dependents
karoliskoncevicius
matrixTests:Fast Statistical Hypothesis Tests on Rows and Columns of Matrices
Functions to perform fast statistical hypothesis tests on rows/columns of matrices. The main goals are: 1) speed via vectorization, 2) output that is detailed and easy to use, 3) compatibility with tests implemented in R (like those available in the 'stats' package).
Maintained by Karolis Koncevičius. Last updated 1 years ago.
anovafasthypothesis-testingmatrixrowst-testwilcoxon-test
36 stars 7.61 score 272 scripts 9 dependents
bioc
onlineFDR:Online error rate control
This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online multiple hypothesis testing, where hypotheses arrive in a stream. In this framework, a null hypothesis is rejected based on the evidence against it and on the previous rejection decisions.
Maintained by David S. Robertson. Last updated 5 months ago.
multiplecomparisonsoftwarestatisticalmethoderror-rate-controlfdrfwerhypothesis-testingcpp
14 stars 6.88 score 26 scripts
hendersontrent
correctR:Corrected Test Statistics for Comparing Machine Learning Models on Correlated Samples
Calculate a set of corrected test statistics for cases when samples are not independent, such as when classification accuracy values are obtained over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.
Maintained by Trent Henderson. Last updated 2 months ago.
hypothesis-testingmachine-learningstatistics
14 stars 5.62 score 8 scripts 1 dependents
ncchung
jaccard:Testing similarity between binary datasets using Jaccard/Tanimoto coefficients
Calculate statistical significance of Jaccard/Tanimoto similarity coefficients.
Maintained by Neo Christopher Chung. Last updated 5 years ago.
binary-datahypothesis-testingjaccardsimilaritystatisticstanimotocpp
5 stars 5.03 score 85 scripts
sfcheung
semlrtp:Likelihood Ratio Test P-Values for Structural Equation Models
Computes likelihood ratio test (LRT) p-values for free parameters in a structural equation model. Currently supports models fitted by the 'lavaan' package by Rosseel (2012) <doi:10.18637/jss.v048.i02>.
Maintained by Shu Fai Cheung. Last updated 2 months ago.
hypothesis-testinglavaanstructural-equation-modeling
4.98 score 24 scripts
vusaverse
vvdoctor:Statistical Test App with R 'shiny'
Provides a user-friendly R 'shiny' app for performing various statistical tests on datasets. It allows users to upload data in numerous formats and perform statistical analyses. The app dynamically adapts its options based on the selected columns and supports both single and multiple column comparisons. The app's user interface is designed to streamline the process of selecting datasets, columns, and test options, making it easy for users to explore and interpret their data. The underlying functions for statistical tests are well-organized and can be used independently within other R scripts.
Maintained by Tomer Iwan. Last updated 11 months ago.
hypothesis-testingr-r-shinyshiny-appsshiny-rstatistical-testsstatisticsstats
7 stars 4.24 score 3 scripts
roga11
MSTest:Hypothesis Testing for Markov Switching Models
Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2024) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_2024_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels_20241015.pdf> that can be used to identify the number of regimes in Markov switching models.
Maintained by Gabriel Rodriguez Rondon. Last updated 1 months ago.
autoregressivebootstraphypothesis-testinglikelihood-ratio-testmarkov-chainmomentsmonte-carlonon-linearregime-switchingtime-seriesopenblascppopenmp
5 stars 4.18 score 3 scripts
timbeechey
opa:An Implementation of Ordinal Pattern Analysis
Quantifies hypothesis to data fit for repeated measures and longitudinal data, as described by Thorngate (1987) <doi:10.1016/S0166-4115(08)60083-7> and Grice et al., (2015) <doi:10.1177/2158244015604192>. Hypothesis and data are encoded as pairwise relative orderings which are then compared to determine the percentage of orderings in the data that are matched by the hypothesis.
Maintained by Timothy Beechey. Last updated 1 years ago.
data-analysishypothesis-testinglongitudinalordinalrcpprepeated-measuresstatisticscpp
1 stars 3.70 score 2 scripts
jorgecastillomateo
RecordTest:Inference Tools in Time Series Based on Record Statistics
Statistical tools based on the probabilistic properties of the record occurrence in a sequence of independent and identically distributed continuous random variables. In particular, tools to prepare a time series as well as distribution-free trend and change-point tests and graphical tools to study the record occurrence. Details about the implemented tools can be found in Castillo-Mateo et al. (2023a) <doi:10.18637/jss.v106.i05> and Castillo-Mateo et al. (2023b) <doi:10.1016/j.atmosres.2023.106934>.
Maintained by Jorge Castillo-Mateo. Last updated 2 years ago.
hypothesis-testingrecord-breaking
1 stars 3.70 score 3 scripts
paulnorthrop
smovie:Some Movies to Illustrate Concepts in Statistics
Provides movies to help students to understand statistical concepts. The 'rpanel' package <https://cran.r-project.org/package=rpanel> is used to create interactive plots that move to illustrate key statistical ideas and methods. There are movies to: visualise probability distributions (including user-supplied ones); illustrate sampling distributions of the sample mean (central limit theorem), the median, the sample maximum (extremal types theorem) and (the Fisher transformation of the) product moment correlation coefficient; examine the influence of an individual observation in simple linear regression; illustrate key concepts in statistical hypothesis testing. Also provided are dpqr functions for the distribution of the Fisher transformation of the correlation coefficient under sampling from a bivariate normal distribution.
Maintained by Paul J. Northrop. Last updated 1 years ago.
central-limit-theoremcorrelation-coefficientextremal-types-theoremextremeshypothesis-testinglikelihood-ratio-testlinear-regressionlog-likelihoodmovieprobability-distributionsregressionscore-teststatistical-conceptsstatisticsstatistics-learningteachingteaching-materialstest-statisticwald-test
1 stars 3.70 score 10 scripts
alextkalinka
hint:Tools for Hypothesis Testing Based on Hypergeometric Intersection Distributions
Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories. <arXiv:1305.0717>.
Maintained by Alex T. Kalinka. Last updated 3 years ago.
combinatoricsdiscrete-mathematicsfrequentist-statisticshypergeometric-distributionhypothesis-testingprobabilitycpp
2.90 score 16 scripts