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CompQuadForm - Distribution Function of Quadratic Forms in Normal Variables

Computes the distribution function of quadratic forms in normal variables using Imhof's method, Davies's algorithm, Farebrother's algorithm or Liu et al.'s algorithm.

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cpp

8.24 score 4 stars 122 dependents 346 scripts 43k downloads

AnalyzeFMRI - Functions for Analysis of fMRI Datasets Stored in the ANALYZE or 'NIFTI' Format

Functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or 'NIFTI' format. Note that the latest version of 'XQuartz' seems to be necessary under MacOS.

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openblas

3.31 score 206 scripts 435 downloads

PoweR - Computation of Power and Level Tables for Hypothesis Tests

Computes power and level tables for goodness-of-fit tests for the normal, Laplace, and uniform distributions. Generates output in 'LaTeX' format to facilitate reporting and reproducibility. Explanatory graphs help visualize the statistical power of test statistics under various alternatives. For more details, see Lafaye De Micheaux and Tran (2016) <doi:10.18637/jss.v069.i03>.

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cpp

1.95 score 89 scripts 325 downloads

groc - Generalized Regression on Orthogonal Components

Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) <doi:10.18637/jss.v065.i01>.

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cpp

1.65 score 1 dependents 15 scripts 314 downloads

ConvergenceConcepts - Seeing Convergence Concepts in Action

This is a pedagogical package, designed to help students understanding convergence of random variables. It provides a way to investigate interactively various modes of convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d. random variables. Visualisation of simulated sample paths is possible through interactive plots. The approach is illustrated by examples and exercises through the function 'investigate', as described in Lafaye de Micheaux and Liquet (2009) <doi:10.1198/tas.2009.0032>. The user can study his/her own sequences of random variables.

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1.48 score 10 scripts 3.0k downloads

nortestARMA - Neyman Smooth Tests of Normality for the Errors of ARMA Models

Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.

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cpp

1.15 score 14 scripts 264 downloads

asympTest - A Simple R Package for Classical Parametric Statistical Tests and Confidence Intervals in Large Samples

One and two sample mean and variance tests (differences and ratios) are considered. The test statistics are all expressed in the same form as the Student t-test, which facilitates their presentation in the classroom. This contribution also fills the gap of a robust (to non-normality) alternative to the chi-square single variance test for large samples, since no such procedure is implemented in standard statistical software.

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1.04 score 11 scripts 425 downloads

IndependenceTests - Non-Parametric Tests of Independence Between Random Vectors

Non-parametric tests of independence (mutual or serial) between some quantitative random vectors, as described in Bilodeau M. and Lafaye de Micheaux P. (2009) <doi:10.1016/j.jspi.2008.11.006>, in Beran R., Bilodeau M. and Lafaye de Micheaux P. (2007) <doi:10.1016/j.jmva.2007.01.009> and in Fan Y., Lafaye de Micheaux P., Penev S. and Salopek D. (2017) <doi:10.1016/j.jmva.2016.09.014>.

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openblasglibccpp

1.00 score 4 scripts 2 downloads

ECGofTestDx - A Goodness-of-Fit Test for Elliptical Distributions with Diagnostic Capabilities

A goodness-of-fit test for elliptical distributions with diagnostic capabilities. Gilles R. Ducharme, Pierre Lafaye de Micheaux (2020) <doi:10.1016/j.jmva.2020.104602>.

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1.00 score 1 scripts 212 downloads

HellCor - The Hellinger Correlation

Empirical value of the Hellinger correlation, a measure of dependence between two continuous random variables. More details can be found in Geenens and Lafaye De Micheaux (2019) <arXiv:1810.10276v4>.

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cpp

1.00 score 5 scripts 210 downloads

rPowerSampleSize - Sample Size Computations Controlling the Type-II Generalized Family-Wise Error Rate

The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.

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1.00 score 9 scripts 240 downloads