R-universe search: topic:statistical-tests

Showing 12 of total 12 results (show query)

spatstat

spatstat:Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests

Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory 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. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.

Maintained by Adrian Baddeley. Last updated 11 days ago.

cluster-process cox-point-process gibbs-process kernel-density network-analysis point-process poisson-process spatial-analysis spatial-data spatial-data-analysis spatial-statistics spatstat statistical-methods statistical-models statistical-tests statistics

200 stars 16.25 score 5.5k scripts 40 dependents

spatstat

spatstat.data:Datasets for 'spatstat' Family

Contains all the datasets for the 'spatstat' family of packages.

Maintained by Adrian Baddeley. Last updated 17 days ago.

kernel-density point-process spatial-analysis spatial-data spatial-data-analysis spatstat statistical-analysis statistical-methods statistical-tests statistics

6 stars 11.07 score 186 scripts 228 dependents

indrajeetpatil

statsExpressions:Tidy Dataframes and Expressions with Statistical Details

Utilities for producing dataframes with rich details for the most common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and provide a consistent syntax to work with tidy data. These dataframes additionally contain expressions with statistical details, and can be used in graphing packages. This package also forms the statistical processing backend for 'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.

Maintained by Indrajeet Patil. Last updated 1 months ago.

bayesian-inference bayesian-statistics contingency-table correlation effectsize meta-analysis parametric robust robust-statistics statistical-details statistical-tests tidy

312 stars 10.92 score 146 scripts 2 dependents

imbi-heidelberg

DescrTab2:Publication Quality Descriptive Statistics Tables

Provides functions to create descriptive statistics tables for continuous and categorical variables. By default, summary statistics such as mean, standard deviation, quantiles, minimum and maximum for continuous variables and relative and absolute frequencies for categorical variables are calculated. 'DescrTab2' features a sophisticated algorithm to choose appropriate test statistics for your data and provides p-values. On top of this, confidence intervals for group differences of appropriated summary measures are automatically produces for two-group comparison. Tables generated by 'DescrTab2' can be integrated in a variety of document formats, including .html, .tex and .docx documents. 'DescrTab2' also allows printing tables to console and saving table objects for later use.

Maintained by Jan Meis. Last updated 1 years ago.

categorical-variables continuous-variable descriptive-statistics p-values statistical-tests statistics

9 stars 6.71 score 19 scripts 1 dependents

rsquaredacademy

inferr:Inferential Statistics

Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of statistical tests provided in 'stats' package by including additional data types as inputs, expanding and restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.

Maintained by Aravind Hebbali. Last updated 5 months ago.

inference inferential-statistics non-parametric parametric statistical-tests cpp

37 stars 6.10 score 34 scripts

klainfo

ScottKnottESD:The Non-Parametric Scott-Knott Effect Size Difference (ESD) Test

The Non-Parametric Scott-Knott Effect Size Difference (ESD) test is a mean comparison approach that leverages a hierarchical clustering to partition the set of treatment means (e.g., means of variable importance scores, means of model performance) into statistically distinct groups with non-negligible difference [Tantithamthavorn et al., (2018) <doi:10.1109/TSE.2018.2794977>].

Maintained by Chakkrit Tantithamthavorn. Last updated 2 years ago.

defect-prediction-models effect-size multiple-comparisons ranking-algorithm scott-knott statistical-tests

43 stars 5.77 score 68 scripts

nunofachada

micompr:Multivariate Independent Comparison of Observations

A procedure for comparing multivariate samples associated with different groups. It uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. The procedure is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. It is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. This package is described in Fachada et al. (2016) <doi:10.32614/RJ-2016-055>.

Maintained by Nuno Fachada. Last updated 8 months ago.

micompr multivariate multivariate-data multivariate-distributions multivariate-observations non-parametric parametric-tests statistical-analysis statistical-data statistical-methods statistical-tests

3 stars 5.19 score 52 scripts

tsmodels

tstests:Time Series Goodness of Fit and Forecast Evaluation Tests

Goodness of Fit and Forecast Evaluation Tests for timeseries models. Includes, among others, the Generalized Method of Moments (GMM) Orthogonality Test of Hansen (1982), the Nyblom (1989) parameter constancy test, the sign-bias test of Engle and Ng (1993), and a range of tests for value at risk and expected shortfall evaluation.

Maintained by Alexios Galanos. Last updated 5 months ago.

forecasting statistical-tests

5 stars 5.10 score 3 scripts

vathymut

dsos:Dataset Shift with Outlier Scores

Test for no adverse shift in two-sample comparison when we have a training set, the reference distribution, and a test set. The approach is flexible and relies on a robust and powerful test statistic, the weighted AUC. Technical details are in Kamulete, V. M. (2021) <arXiv:1908.04000>. Modern notions of outlyingness such as trust scores and prediction uncertainty can be used as the underlying scores for example.

Maintained by Vathy M. Kamulete. Last updated 2 years ago.

data-drift data-validation dataset-shifts drift-detection machine-learning mlops model-monitoring model-validation performance-monitoring statistical-process-control statistical-tests

2 stars 5.08 score 40 scripts

f8l5h9

spqdep:Testing for Spatial Independence of Cross-Sectional Qualitative Data

Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in <doi:10.1007/s10109-009-0100-1> and <doi:10.1080/13658816.2011.586327>.

Maintained by Fernando Lopez. Last updated 16 hours ago.

categorical-data qualitative-analysis spatial-data-analysis spatial-econometrics statistical-tests

4.48 score 3 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-testing r-r-shiny shiny-apps shiny-r statistical-tests statistics stats

7 stars 4.24 score 3 scripts

sciviews

inferit:Hypothesis Tests and Statistical Distributions for 'SciViews::R'

Statistical distributions (including their visual representation) and hypothesis tests with rich-formatted tabular outputs for the 'SciViews::R' dialect.

Maintained by Philippe Grosjean. Last updated 10 months ago.

sciviews statistical-inference statistical-tests

3.00 score 6 scripts