Showing 94 of total 94 results (show query)
tirgit
missCompare:Intuitive Missing Data Imputation Framework
Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as 'mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; 'missForest', described by Stekhoven and Buhlmann (2012) <doi:10.1093/bioinformatics/btr597>; 'missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and 'pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind 'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. 'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance.
Maintained by Tibor V. Varga. Last updated 4 years ago.
comparisoncomparison-benchmarksimputationimputation-algorithmimputation-methodsimputationskolmogorov-smirnovmissingmissing-datamissing-data-imputationmissing-status-checkmissing-valuesmissingnesspost-imputation-diagnosticsrmse
15.0 match 39 stars 5.89 score 40 scripts
alexkowa
EnvStats:Package for Environmental Statistics, Including US EPA Guidance
Graphical and statistical analyses of environmental data, with focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. Major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. Numerous built-in data sets from regulatory guidance documents and environmental statistics literature. Includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, <doi:10.1007/978-1-4614-8456-1>).
Maintained by Alexander Kowarik. Last updated 16 days ago.
6.6 match 26 stars 12.80 score 2.4k scripts 46 dependents
drg-123
NSM3:Functions and Datasets to Accompany Hollander, Wolfe, and Chicken - Nonparametric Statistical Methods, Third Edition
Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods. Exact procedures are performed when computationally possible. Monte Carlo and Asymptotic procedures are performed otherwise. For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.
Maintained by Grant Schneider. Last updated 4 months ago.
18.7 match 1 stars 3.77 score 115 scripts 1 dependents
pnovack-gottshall
KScorrect:Lilliefors-Corrected Kolmogorov-Smirnov Goodness-of-Fit Tests
Implements the Lilliefors-corrected Kolmogorov-Smirnov test for use in goodness-of-fit tests, suitable when population parameters are unknown and must be estimated by sample statistics. P-values are estimated by simulation. Can be used with a variety of continuous distributions, including normal, lognormal, univariate mixtures of normals, uniform, loguniform, exponential, gamma, and Weibull distributions. Functions to generate random numbers and calculate density, distribution, and quantile functions are provided for use with the log uniform and mixture distributions.
Maintained by Phil Novack-Gottshall. Last updated 6 years ago.
13.5 match 3 stars 4.35 score 50 scripts 1 dependents
laplacesdemonr
LaplacesDemon:Complete Environment for Bayesian Inference
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
Maintained by Henrik Singmann. Last updated 12 months ago.
3.8 match 93 stars 13.45 score 1.8k scripts 60 dependents
iscience-kn
dropR:Dropout Analysis by Condition
Analysis and visualization of dropout between conditions in surveys and (online) experiments. Features include computation of dropout statistics, comparing dropout between conditions (e.g. Chi square), analyzing survival (e.g. Kaplan-Meier estimation), comparing conditions with the most different rates of dropout (Kolmogorov-Smirnov) and visualizing the result of each in designated plotting functions. Sources: Andrea Frick, Marie-Terese Baechtiger & Ulf-Dietrich Reips (2001) <https://www.researchgate.net/publication/223956222_Financial_incentives_personal_information_and_drop-out_in_online_studies>; Ulf-Dietrich Reips (2002) "Standards for Internet-Based Experimenting" <doi:10.1027//1618-3169.49.4.243>.
Maintained by Annika Tave Overlander. Last updated 4 months ago.
dropoutexperimentspsychologysocial-science
7.9 match 6 stars 6.06 score 16 scripts
yanyachen
MLmetrics:Machine Learning Evaluation Metrics
A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.
Maintained by Yachen Yan. Last updated 11 months ago.
4.0 match 69 stars 11.09 score 2.2k scripts 20 dependents
fishr-core-team
FSA:Simple Fisheries Stock Assessment Methods
A variety of simple fish stock assessment methods.
Maintained by Derek H. Ogle. Last updated 2 months ago.
fishfisheriesfisheries-managementfisheries-stock-assessmentpopulation-dynamicsstock-assessment
4.0 match 68 stars 11.08 score 1.7k scripts 6 dependents
jasjeetsekhon
Matching:Multivariate and Propensity Score Matching with Balance Optimization
Provides functions for multivariate and propensity score matching and for finding optimal balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance has been obtained are also provided. For details, see the paper by Jasjeet Sekhon (2007, <doi:10.18637/jss.v042.i07>).
Maintained by Jasjeet Singh Sekhon. Last updated 5 months ago.
4.0 match 24 stars 10.36 score 852 scripts 10 dependents
mmaechler
sfsmisc:Utilities from 'Seminar fuer Statistik' ETH Zurich
Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich, some of which were ported from S-plus in the 1990s. For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()', pretty arrows, etc. For robustness, have a robust F test and robust range(). For system support, notably on Linux, provides 'Sys.*()' functions with more access to system and CPU information. Finally, miscellaneous utilities such as simple efficient prime numbers, integer codes, Duplicated(), toLatex.numeric() and is.whole().
Maintained by Martin Maechler. Last updated 5 months ago.
3.5 match 11 stars 10.87 score 566 scripts 119 dependents
jeromeecoac
seewave:Sound Analysis and Synthesis
Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound). This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) <doi:10.1080/09524622.2008.9753600> and Sueur (2018) <doi:10.1007/978-3-319-77647-7>.
Maintained by Jerome Sueur. Last updated 1 years ago.
4.0 match 18 stars 8.84 score 880 scripts 23 dependents
obouaziz
robusTest:Calibrated Correlation and Two-Sample Tests
Implementation of corrected two-sample tests. A corrected version of the Pearson and Kendall correlation tests, the Mann-Whitney (Wilcoxon) rank sum test, the Wilcoxon signed rank test and a variance test are implemented. The package also proposes a test for the median and an independence test between two continuous variables of Kolmogorov-Smirnov's type. All these corrected tests are asymptotically calibrated in the sense that the probability of rejection under the null hypothesis is asymptotically equal to the level of the test. See <doi:10.48550/arXiv.2211.08784> for more details on the statistical tests.
Maintained by Olivier Bouaziz. Last updated 9 months ago.
11.0 match 3.18 score 4 scripts
cdowd
twosamples:Fast Permutation Based Two Sample Tests
Fast randomization based two sample tests. Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at <arXiv:2007.01360> or <https://codowd.com/public/DTS.pdf> for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others.
Maintained by Connor Dowd. Last updated 2 years ago.
5.0 match 17 stars 6.88 score 62 scripts 8 dependents
zwenyu
ecp:Non-Parametric Multiple Change-Point Analysis of Multivariate Data
Implements various procedures for finding multiple change-points from Matteson D. et al (2013) <doi:10.1080/01621459.2013.849605>, Zhang W. et al (2017) <doi:10.1109/ICDMW.2017.44>, Arlot S. et al (2019). Two methods make use of dynamic programming and pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change- points as well as other summary information.
Maintained by Wenyu Zhang. Last updated 7 months ago.
6.7 match 1 stars 5.07 score 103 scripts 18 dependents
uligges
nortest:Tests for Normality
Five omnibus tests for testing the composite hypothesis of normality.
Maintained by Uwe Ligges. Last updated 10 years ago.
3.6 match 9.13 score 3.5k scripts 155 dependents
bioc
slingshot:Tools for ordering single-cell sequencing
Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction.
Maintained by Kelly Street. Last updated 5 months ago.
clusteringdifferentialexpressiongeneexpressionrnaseqsequencingsoftwaresinglecelltranscriptomicsvisualization
2.7 match 283 stars 12.01 score 1.0k scripts 4 dependentsbioc
GSAR:Gene Set Analysis in R
Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure.
Maintained by Yasir Rahmatallah. Last updated 5 months ago.
softwarestatisticalmethoddifferentialexpression
7.1 match 4.38 score 7 scripts
mthrun
AdaptGauss:Gaussian Mixture Models (GMM)
Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) <DOI:10.3390/ijms161025897>.
Maintained by Michael Thrun. Last updated 2 years ago.
5.0 match 1 stars 6.12 score 25 scripts 5 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
4.0 match 36 stars 7.60 score 272 scripts 8 dependents
uclahs-cds
BoutrosLab.plotting.general:Functions to Create Publication-Quality Plots
Contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 <doi:10.1186/s12859-019-2610-2>.
Maintained by Paul Boutros. Last updated 5 months ago.
3.6 match 12 stars 8.36 score 414 scripts 6 dependents
tomasfryda
h2o:R Interface for the 'H2O' Scalable Machine Learning Platform
R interface for 'H2O', the scalable open source machine learning platform that offers parallelized implementations of many supervised and unsupervised machine learning algorithms such as Generalized Linear Models (GLM), Gradient Boosting Machines (including XGBoost), Random Forests, Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Generalized Additive Models (GAM), ANOVA GLM, Cox Proportional Hazards, K-Means, PCA, ModelSelection, Word2Vec, as well as a fully automatic machine learning algorithm (H2O AutoML).
Maintained by Tomas Fryda. Last updated 1 years ago.
3.6 match 3 stars 8.20 score 7.8k scripts 11 dependents
braunlab-nu
fasano.franceschini.test:Fasano-Franceschini Test: A Multivariate Kolmogorov-Smirnov Two-Sample Test
An implementation of the two-sample multivariate Kolmogorov-Smirnov test described by Fasano and Franceschini (1987) <doi:10.1093/mnras/225.1.155>. This test evaluates the null hypothesis that two i.i.d. random samples were drawn from the same underlying probability distribution. The data can be of any dimension, and can be of any type (continuous, discrete, or mixed).
Maintained by Connor Puritz. Last updated 1 years ago.
6.6 match 6 stars 4.36 score 38 scripts
gmgeorg
LambertW:Probabilistic Models to Analyze and Gaussianize Heavy-Tailed, Skewed Data
Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away.
Maintained by Georg M. Goerg. Last updated 1 years ago.
gaussianizegaussianize-dataheavy-tailedheavy-tailed-distributionsleptokurtosisnormal-distributionnormalizationskewed-datastatisticscpp
3.4 match 10 stars 8.17 score 78 scripts 13 dependents
ekstroem
MESS:Miscellaneous Esoteric Statistical Scripts
A mixed collection of useful and semi-useful diverse statistical functions, some of which may even be referenced in The R Primer book. See Ekstrøm, C. T. (2016). The R Primer. 2nd edition. Chapman & Hall.
Maintained by Claus Thorn Ekstrøm. Last updated 29 days ago.
biostatisticspower-analysisstatistical-analysisstatistical-methodsstatistical-modelsopenblascpp
3.4 match 4 stars 7.76 score 328 scripts 13 dependents
cran
fBasics:Rmetrics - Markets and Basic Statistics
Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management.
Maintained by Georgi N. Boshnakov. Last updated 7 months ago.
3.6 match 2 stars 7.11 score 129 dependents
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 2 months ago.
cluster-processcox-point-processgibbs-processkernel-densitynetwork-analysispoint-processpoisson-processspatial-analysisspatial-dataspatial-data-analysisspatial-statisticsspatstatstatistical-methodsstatistical-modelsstatistical-testsstatistics
1.5 match 200 stars 16.32 score 5.5k scripts 41 dependents
carloscinelli
benford.analysis:Benford Analysis for Data Validation and Forensic Analytics
Provides tools that make it easier to validate data using Benford's Law.
Maintained by Carlos Cinelli. Last updated 6 years ago.
3.5 match 62 stars 5.66 score 74 scripts
josh-mc
discretefit:Simulated Goodness-of-Fit Tests for Discrete Distributions
Implements fast Monte Carlo simulations for goodness-of-fit (GOF) tests for discrete distributions. This includes tests based on the Chi-squared statistic, the log-likelihood-ratio (G^2) statistic, the Freeman-Tukey (Hellinger-distance) statistic, the Kolmogorov-Smirnov statistic, the Cramer-von Mises statistic as described in Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828>, and the root-mean-square statistic, see Perkins, Tygert, and Ward (2011) <doi:10.1016/j.amc.2011.03.124>.
Maintained by Josh McCormick. Last updated 3 years ago.
4.5 match 1 stars 4.18 score 7 scripts 1 dependentsbioc
dks:The double Kolmogorov-Smirnov package for evaluating multiple testing procedures.
The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated.
Maintained by Jeffrey T. Leek. Last updated 5 months ago.
multiplecomparisonqualitycontrol
5.6 match 3.30 score 1 scripts
mmm-uca
RcmdrPlugin.UCA:UCA Rcmdr Plug-in
Some extensions to Rcmdr (R Commander), randomness test, variance test for one normal sample and predictions using active model, made by R-UCA project and used in teaching statistics at University of Cadiz (UCA).
Maintained by Manuel Munoz-Marquez. Last updated 6 months ago.
7.8 match 2.34 score 11 scripts
qddyy
LearnNonparam:'R6'-Based Flexible Framework for Permutation Tests
Implements non-parametric tests from Higgins (2004, ISBN:0534387756), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with 'Rcpp' for efficiency and 'R6' for flexible, object-oriented design, the package provides a unified framework for performing or creating custom permutation tests.
Maintained by Yan Du. Last updated 2 months ago.
hypothesis-testnonparametric-statisticspermutation-testcpp
3.6 match 6 stars 5.01 score 2 scriptsbioc
CaDrA:Candidate Driver Analysis
Performs both stepwise and backward heuristic search for candidate (epi)genetic drivers based on a binary multi-omics dataset. CaDrA's main objective is to identify features which, together, are significantly skewed or enriched pertaining to a given vector of continuous scores (e.g. sample-specific scores representing a phenotypic readout of interest, such as protein expression, pathway activity, etc.), based on the union occurence (i.e. logical OR) of the events.
Maintained by Reina Chau. Last updated 5 months ago.
microarrayrnaseqgeneexpressionsoftwarefeatureextraction
2.4 match 24 stars 7.19 score 12 scripts
christinaheinze
CondIndTests:Nonlinear Conditional Independence Tests
Code for a variety of nonlinear conditional independence tests: Kernel conditional independence test (Zhang et al., UAI 2011, <arXiv:1202.3775>), Residual Prediction test (based on Shah and Buehlmann, <arXiv:1511.03334>), Invariant environment prediction, Invariant target prediction, Invariant residual distribution test, Invariant conditional quantile prediction (all from Heinze-Deml et al., <arXiv:1706.08576>).
Maintained by Christina Heinze-Deml. Last updated 5 years ago.
3.5 match 17 stars 4.91 score 32 scripts 1 dependents
abusjahn
wrappedtools:Useful Wrappers Around Commonly Used Functions
The main functionalities of 'wrappedtools' are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well.
Maintained by Andreas Busjahn. Last updated 5 months ago.
descriptive-statisticstest-statistic
3.6 match 2 stars 4.70 score 8 scripts
yml2017xiao
Peacock.test:Two and Three Dimensional Kolmogorov-Smirnov Two-Sample Tests
The original definition of the two and three dimensional Kolmogorov-Smirnov two-sample test statistics given by Peacock (1983) is implemented. Two R-functions: peacock2 and peacock3, are provided to compute the test statistics in two and three dimensional spaces, respectively. Note the Peacock test is different from the Fasano and Franceschini test (1987). The latter is a variant of the Peacock test.
Maintained by Yuanhui Xiao. Last updated 9 years ago.
16.9 match 1.00 score 10 scripts
iohprofiler
IOHanalyzer:Data Analysis Part of 'IOHprofiler'
The data analysis module for the Iterative Optimization Heuristics Profiler ('IOHprofiler'). This module provides statistical analysis methods for the benchmark data generated by optimization heuristics, which can be visualized through a web-based interface. The benchmark data is usually generated by the experimentation module, called 'IOHexperimenter'. 'IOHanalyzer' also supports the widely used 'COCO' (Comparing Continuous Optimisers) data format for benchmarking.
Maintained by Diederick Vermetten. Last updated 10 months ago.
3.3 match 24 stars 5.10 score 13 scripts
alanarnholt
PASWR:Probability and Statistics with R
Functions and data sets for the text Probability and Statistics with R.
Maintained by Alan T. Arnholt. Last updated 3 years ago.
3.5 match 2 stars 4.70 score 241 scriptsspatstat
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 1 months ago.
cluster-detectionconfidence-intervalshypothesis-testingk-functionroc-curvesscan-statisticssignificance-testingsimulation-envelopesspatial-analysisspatial-data-analysisspatial-sharpeningspatial-smoothingspatial-statistics
1.5 match 1 stars 10.17 score 67 scripts 148 dependentsbioc
benchdamic:Benchmark of differential abundance methods on microbiome data
Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.
Maintained by Matteo Calgaro. Last updated 4 months ago.
metagenomicsmicrobiomedifferentialexpressionmultiplecomparisonnormalizationpreprocessingsoftwarebenchmarkdifferential-abundance-methods
2.7 match 6 stars 5.73 score 8 scripts
munterfi
eRTG3D:Empirically Informed Random Trajectory Generation in 3-D
Creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories. Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. <https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf>. Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. <doi:10.5167/uzh-130652>.
Maintained by Merlin Unterfinger. Last updated 3 years ago.
3dbirdsconditional-empirical-random-walkgliding-and-soaringmachine-learningmovement-ecologyrandom-trajectory-generatorrandom-walksimulationtrajectory-generation
2.7 match 6 stars 5.71 score 19 scripts
benrenard
HydroPortailStats:'HydroPortail' Statistical Functions
Statistical functions used in the French 'HydroPortail' <https://hydro.eaufrance.fr/>. This includes functions to estimate distributions, quantile curves and uncertainties, along with various other utilities. Technical details are available (in French) in Renard (2016) <https://hal.inrae.fr/hal-02605318>.
Maintained by Benjamin Renard. Last updated 4 months ago.
hydrologystatistical-distributionsstatistics
4.0 match 3 stars 3.78 score 1 scripts
alanarnholt
PASWR2:Probability and Statistics with R, Second Edition
Functions and data sets for the text Probability and Statistics with R, Second Edition.
Maintained by Alan T. Arnholt. Last updated 3 years ago.
3.5 match 1 stars 4.24 score 260 scriptsspatstat
spatstat.linnet:Linear Networks Functionality of the 'spatstat' Family
Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. 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 function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. 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. Random point patterns on a network can be generated using a variety of models.
Maintained by Adrian Baddeley. Last updated 2 months ago.
density-estimationheat-equationkernel-density-estimationnetwork-analysispoint-processesspatial-data-analysisstatistical-analysisstatistical-inferencestatistical-models
1.5 match 6 stars 9.64 score 35 scripts 43 dependentscran
dgof:Discrete Goodness-of-Fit Tests
A revision to the stats::ks.test() function and the associated ks.test.Rd help page. With one minor exception, it does not change the existing behavior of ks.test(), and it adds features necessary for doing one-sample tests with hypothesized discrete distributions. The package also contains cvm.test(), for doing one-sample Cramer-von Mises goodness-of-fit tests.
Maintained by Taylor B. Arnold. Last updated 5 months ago.
4.0 match 1 stars 3.36 score 4 dependents
hectorrdb
Ecume:Equality of 2 (or k) Continuous Univariate and Multivariate Distributions
We implement (or re-implements in R) a variety of statistical tools. They are focused on non-parametric two-sample (or k-sample) distribution comparisons in the univariate or multivariate case. See the vignette for more info.
Maintained by Hector Roux de Bezieux. Last updated 10 months ago.
2.7 match 1 stars 4.86 score 16 scripts 3 dependents
klebermsousa
jackstrap:Correcting Nonparametric Frontier Measurements for Outliers
Provides method used to check whether data have outlier in efficiency measurement of big samples with data envelopment analysis (DEA). In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.
Maintained by Kleber Morais de Sousa. Last updated 5 years ago.
deajackstrapnonparametricoutlier-detection
3.2 match 1 stars 3.85 score 14 scripts
aibrt
FreqProf:Frequency Profiles Computing and Plotting
Tools for generating an informative type of line graph, the frequency profile, which allows single behaviors, multiple behaviors, or the specific behavioral patterns of individual subjects to be graphed from occurrence/nonoccurrence behavioral data.
Maintained by Ronald E. Robertson. Last updated 9 years ago.
3.3 match 2 stars 3.48 score 7 scripts
simontrimborn
gofCopula:Goodness-of-Fit Tests for Copulae
Several Goodness-of-Fit (GoF) tests for Copulae are provided. A new hybrid test, Zhang et al. (2016) <doi:10.1016/j.jeconom.2016.02.017> is implemented which supports all of the individual tests in the package, e.g. Genest et al. (2009) <doi:10.1016/j.insmatheco.2007.10.005>. Estimation methods for the margins are provided and all the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. For reproducibility of results, the functions support the definition of seeds. Also all the tests support automatized parallelization of the bootstrapping tasks. The package provides an interface to perform new GoF tests by submitting the test statistic.
Maintained by Simon Trimborn. Last updated 3 years ago.
3.4 match 3.16 score 29 scripts
martenthompson
agfh:Agnostic Fay-Herriot Model for Small Area Statistics
Implements the Agnostic Fay-Herriot model, an extension of the traditional small area model. In place of normal sampling errors, the sampling error distribution is estimated with a Gaussian process to accommodate a broader class of distributions. This flexibility is most useful in the presence of bounded, multi-modal, or heavily skewed sampling errors.
Maintained by Marten Thompson. Last updated 2 years ago.
3.8 match 2.70 score 2 scriptscran
AFR:Toolkit for Regression Analysis of Kazakhstan Banking Sector Data
Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data. The package includes functions from existing R packages adapted for banking sector of Kazakhstan. The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians.
Maintained by Sultan Zhaparov. Last updated 6 months ago.
2.7 match 3.18 score
ebner-kit
gofgamma:Goodness-of-Fit Tests for the Gamma Distribution
We implement various classical tests for the composite hypothesis of testing the fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the Cramer-von Mises test, the Anderson Darling test and the Watson test. For each test a parametric bootstrap procedure is implemented, as considered in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851>. The recent procedures presented in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851> and Betsch & Ebner (2019) <doi:10.1007/s00184-019-00708-7> are implemented. Estimation of parameters of the gamma law are implemented using the method of Bhattacharya (2001) <doi:10.1080/00949650108812100>.
Maintained by Bruno Ebner. Last updated 5 years ago.
7.8 match 1.00 score 2 scripts
danielamattei
Rita:Automated Transformations, Normality Testing, and Reporting
Automated performance of common transformations used to fulfill parametric assumptions of normality and identification of the best performing method for the user. Output for various normality tests (Thode, 2002) corresponding to the best performing method and a descriptive statistical report of the input data in its original units (5-number summary and mathematical moments) are also presented. Lastly, the Rankit, an empirical normal quantile transformation (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard use cases and facilitate adoption. <DOI: 10.1201/9780203910894>. <DOI: 10.22237/jmasm/1257034080>.
Maintained by Daniel Mattei. Last updated 3 years ago.
3.8 match 2.00 score 1 scriptsebner-kit
gofIG:Goodness-of-Fit Tests for the Inverse Gaussian Distribution
We implement various tests for the composite hypothesis of testing the fit to the family of inverse Gaussian distributions. Included are methods presented by Allison, J.S., Betsch, S., Ebner, B., and Visagie, I.J.H. (2022) <doi:10.48550/arXiv.1910.14119>, as well as two tests from Henze and Klar (2002) <doi:10.1023/A:1022442506681>. Additionally, the package implements a test proposed by Baringhaus and Gaigall (2015) <doi:10.1016/j.jmva.2015.05.013>. For each test a parametric bootstrap procedure is implemented.
Maintained by Bruno Ebner. Last updated 5 months ago.
7.1 match 1.00 score
sth1402
GGMridge:Gaussian Graphical Models Using Ridge Penalty Followed by Thresholding and Reestimation
Estimation of partial correlation matrix using ridge penalty followed by thresholding and reestimation. Under multivariate Gaussian assumption, the matrix constitutes an Gaussian graphical model (GGM).
Maintained by Shannon T. Holloway. Last updated 1 years ago.
3.6 match 1.89 score 13 scripts 2 dependents
kylebgorman
ldamatch:Selection of Statistically Similar Research Groups
Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.
Maintained by Kyle Gorman. Last updated 11 months ago.
3.3 match 2.00 score 9 scripts
blunde1
agtboost:Adaptive and Automatic Gradient Boosting Computations
Fast and automatic gradient tree boosting designed to avoid manual tuning and cross-validation by utilizing an information theoretic approach. This makes the algorithm adaptive to the dataset at hand; it is completely automatic, and with minimal worries of overfitting. Consequently, the speed-ups relative to state-of-the-art implementations can be in the thousands while mathematical and technical knowledge required on the user are minimized.
Maintained by Berent Ånund Strømnes Lunde. Last updated 3 years ago.
3.8 match 1.72 score 52 scriptscran
CDFt:Downscaling and Bias Correction via Non-Parametric CDF-Transform
Statistical downscaling and bias correction (model output statistics) method based on cumulative distribution functions (CDF) transformation. See Michelangeli, Vrac, Loukos (2009) Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 36, L11708, <doi:10.1029/2009GL038401>. ; and Vrac, Drobinski, Merlo, Herrmann, Lavaysse, Li, Somot (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769-2784, www.nat-hazards-earth-syst-sci.net/12/2769/2012/, <doi:10.5194/nhess-12-2769-2012>.
Maintained by Mathieu Vrac. Last updated 4 years ago.
3.8 match 1.52 score 11 scripts 1 dependentscran
MN:Matrix Normal Distribution
Density computation, random matrix generation and maximum likelihood estimation of the matrix normal distribution. References: Pocuca N., Gallaugher M. P., Clark K. M. & McNicholas P. D. (2019). Assessing and Visualizing Matrix Variate Normality. <doi:10.48550/arXiv.1910.02859> and the relevant wikipedia page.
Maintained by Michail Tsagris. Last updated 10 months ago.
3.6 match 1.48 score 1 dependentsbioc
GeneExpressionSignature:Gene Expression Signature based Similarity Metric
This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest.
Maintained by Yang Cao. Last updated 5 months ago.
1.0 match 1 stars 5.00 score 5 scriptsbioc
scShapes:A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data
We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic.
Maintained by Malindrie Dharmaratne. Last updated 5 months ago.
rnaseqsinglecellmultiplecomparisongeneexpression
1.0 match 8 stars 4.90 score 6 scripts
cuining1
DistributionTest:Powerful Goodness-of-Fit Tests Based on the Likelihood Ratio
Provides new types of omnibus tests which are generally much more powerful than traditional tests (including the Kolmogorov-Smirnov, Cramer-von Mises and Anderson-Darling tests),see Zhang (2002) <doi:10.1111/1467-9868.00337>.
Maintained by Ning Cui. Last updated 5 years ago.
4.6 match 1.00 score
hanjunwei-lab
MiRSEA:'MicroRNA' Set Enrichment Analysis
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Maintained by Junwei Han. Last updated 5 years ago.
statisticspathwaysmicrornaenrichment analysis
1.0 match 4.51 score 16 scriptscran
acid:Analysing Conditional Income Distributions
Functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level.
Maintained by Alexander Sohn. Last updated 9 years ago.
4.4 match 1.00 score
jiaxiangbu
rawKS:Easily Get True-Positive Rate and False-Positive Rate and KS Statistic
The Kolmogorov-Smirnov (K-S) statistic is a standard method to measure the model strength for credit risk scoring models. This package calculates the K–S statistic and plots the true-positive rate and false-positive rate to measure the model strength. This package was written with the credit marketer, who uses risk models in conjunction with his campaigns. The users could read more details from Thrasher (1992) <doi:10.1002/dir.4000060408> and 'pyks' <https://pypi.org/project/pyks/>.
Maintained by Jiaxiang Li. Last updated 5 years ago.
1.0 match 3 stars 4.18 score 5 scriptscran
GSSE:Genotype-Specific Survival Estimation
We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees. We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function. The estimators are calculated via an expectation-maximization algorithm.
Maintained by Baosheng Liang. Last updated 9 years ago.
3.8 match 1.00 scorecran
BenfordTests:Statistical Tests for Evaluating Conformity to Benford's Law
Several specialized statistical tests and support functions for determining if numerical data could conform to Benford's law.
Maintained by Dieter William Joenssen. Last updated 10 years ago.
3.6 match 1.00 score
ashipunov
kldtools:Kullback-Leibler Divergence and Other Tools to Analyze Frequencies
Most importantly, calculates Kullback-Leibler Divergence (KLD), Turing's perspective estimator and their confidence intervals.
Maintained by Alexey Shipunov. Last updated 3 years ago.
3.6 match 1.00 score 3 scripts
umich-biostatistics
AEenrich:Adverse Event Enrichment Tests
We extend existing gene enrichment tests to perform adverse event enrichment analysis. Unlike the continuous gene expression data, adverse event data are counts. Therefore, adverse event data has many zeros and ties. We propose two enrichment tests. One is a modified Fisher's exact test based on pre-selected significant adverse events, while the other is based on a modified Kolmogorov-Smirnov statistic. We add Covariate adjustment to improve the analysis."Adverse event enrichment tests using VAERS" Shuoran Li, Lili Zhao (2020) <arXiv:2007.02266>.
Maintained by Michael Kleinsasser. Last updated 2 years ago.
1.0 match 3 stars 3.48 score 1 scripts
statcompute
vasicek:Miscellaneous Functions for Vasicek Distribution
Provide a collection of miscellaneous R functions related to the Vasicek distribution with the intent to make the lives of risk modelers easier.
Maintained by WenSui Liu. Last updated 4 years ago.
3.4 match 1.00 score
guangbaog
Dogoftest:Distributed Online Goodness-of-Fit Tests for Distributed Datasets
Distributed Online Goodness-of-Fit Test can process the distributed datasets. The philosophy of the package is described in Guo G.(2024) <doi:10.1016/j.apm.2024.115709>.
Maintained by Guangbao Guo. Last updated 26 days ago.
3.4 match 1.00 score
cran
SCBiclust:Identifies Mean, Variance, and Hierarchically Clustered Biclusters
Identifies a bicluster, a submatrix of the data such that the features and observations within the submatrix differ from those not contained in submatrix, using a two-step method. In the first step, observations in the bicluster are identified to maximize the sum of weighted between cluster feature differences. The method is described in Helgeson et al. (2020) <doi:10.1111/biom.13136>. 'SCBiclust' can be used to identify biclusters which differ based on feature means, feature variances, or more general differences.
Maintained by Erika S. Helgeson. Last updated 3 years ago.
3.3 match 1.00 score
haydarde
CryptRndTest:Statistical Tests for Cryptographic Randomness
Performs cryptographic randomness tests on a sequence of random integers or bits. Included tests are greatest common divisor, birthday spacings, book stack, adaptive chi-square, topological binary, and three random walk tests (Ryabko and Monarev, 2005) <doi:10.1016/j.jspi.2004.02.010>. Tests except greatest common divisor and birthday spacings are not covered by standard test suites. In addition to the chi-square goodness-of-fit test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are also generated by some of the cryptographic randomness tests.
Maintained by Haydar Demirhan. Last updated 3 years ago.
1.0 match 2.20 score 16 scripts
xiaomangmang
iZID:Identify Zero-Inflated Distributions
Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) <doi:10.1109/BHI.2019.8834661>. With the package, user can also find tools to simulate random deviates from zero inflated or hurdle models and obtain maximum likelihood estimate of unknown parameters in these models.
Maintained by Lei Wang. Last updated 5 years ago.
1.0 match 1.70 score 6 scripts
cran
SOPIE:Non-Parametric Estimation of the Off-Pulse Interval of a Pulsar
Provides functions to non-parametrically estimate the off-pulse interval of a source function originating from a pulsar. The technique is based on a sequential application of P-values obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.
Maintained by Willem Daniel Schutte. Last updated 3 years ago.
1.0 match 1.00 scorecran
stodom:Estimating Consistent Tests for Stochastic Dominance
Stochastic dominance tests help ranking different distributions. The package implements the consistent test for stochastic dominance by Barrett and Donald (2003) <doi:10.1111/1468-0262.00390>. Specifically, it implements Barrett and Donald's Kolmogorov-Smirnov type tests for first- and second-order stochastic dominance based on bootstrapping 2 and 1.
Maintained by Sergei Schaub. Last updated 1 years ago.
1.0 match 1.00 score
markusboenn
fitteR:Fit Hundreds of Theoretical Distributions to Empirical Data
Systematic fit of hundreds of theoretical univariate distributions to empirical data via maximum likelihood estimation. Fits are reported and summarized by a data.frame, a csv file or a 'shiny' app (here with additional features like visual representation of fits). All output formats provide assessment of goodness-of-fit by the following methods: Kolmogorov-Smirnov test, Shapiro-Wilks test, Anderson-Darling test.
Maintained by Markus Boenn. Last updated 3 years ago.
1.0 match 1.00 score 6 scripts
leilamarvian
Hassani.Silva:A Test for Comparing the Predictive Accuracy of Two Sets of Forecasts
A non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a statistically significant difference between the distribution of forecast errors, and secondly it exploits the principles of stochastic dominance to determine whether the forecasts with the lower error also reports a stochastically smaller error than forecasts from a competing model, and thereby enables distinguishing between the predictive accuracy of forecasts. KSPA test has been described in : Hassani and Silva (2015) <doi:10.3390/econometrics3030590>.
Maintained by Leila Marvian Mashhad. Last updated 2 years ago.
1.0 match 1 stars 1.00 score
niloufardousti
AZIAD:Analyzing Zero-Inflated and Zero-Altered Data
Description: Computes maximum likelihood estimates of general, zero-inflated, and zero-altered models for discrete and continuous distributions. It also performs Kolmogorov-Smirnov (KS) tests and likelihood ratio tests for general, zero-inflated, and zero-altered data. Additionally, it obtains the inverse of the Fisher information matrix and confidence intervals for the parameters of general, zero-inflated, and zero-altered models. The package simulates random deviates from zero-inflated or hurdle models to obtain maximum likelihood estimates. Based on the work of Aldirawi et al. (2022) <doi:10.1007/s42519-021-00230-y> and Dousti Mousavi et al. (2023) <doi:10.1080/00949655.2023.2207020>.
Maintained by Niloufar Dousti Mousavi. Last updated 11 months ago.
1.0 match 1.00 score
