rStats.js

R functions in rStats.js

Generated by node scripts/r-index.mjs from the doc comments. Functions of other packages are written package::function(). How the arguments are written: the guide for JavaScript developers; each function in detail: the API reference.

Special functions (`rStats.js/special`)

R	rStats.js	Description
`besselI()`	`besselI`	Modified Bessel function of the first kind.
`besselJ()`	`besselJ`	Bessel function of the first kind.
`besselK()`	`besselK`	Modified Bessel function of the second kind.
`besselY()`	`besselY`	Bessel function of the second kind.
`beta()`	`beta`	Beta function B(a, b).
`choose()`	`choose`	Binomial coefficient.
`digamma()`	`digamma`	Digamma function, the derivative of `lgamma`.
`pracma::erfcinv()`	`erfcinv`	Inverse of the complementary error function: the x with erfc(x) = z, for 0 <= z <= 2.
`pracma::erfinv()`	`erfinv`	Inverse of the error function: the x with erf(x) = y, for -1 <= y <= 1.
`factorial()`	`factorial`	Factorial.
`gamma()`	`gamma`	Gamma function.
`pracma::gammainc()`	`gammainc`	The non-regularized incomplete gamma functions gamma(a, x) and Gamma(a, x) and their logs.
`lbeta()`	`lbeta`	Natural logarithm of the absolute value of the beta function, log\|B(a, b)\|.
`lchoose()`	`lchoose`	Logarithm of the absolute value of the binomial coefficient.
`lfactorial()`	`lfactorial`	Logarithm of the factorial.
`lgamma()`	`lgamma`	Natural logarithm of the absolute value of the gamma function, log\|Gamma(x)\|.
`psigamma()`	`psigamma`	Polygamma function, the derivative of order `deriv` of `digamma`.
`trigamma()`	`trigamma`	Trigamma function, the second derivative of `lgamma`.
(not in R)	`erf`	Error function, erf(x) = 2 / sqrt(pi) * integral from 0 to x of exp(-t^2) dt.
(not in R)	`erfc`	Complementary error function, erfc(x) = 1 - erf(x), accurate in the upper tail.
(not in R)	`lgamma1p`	lgamma(1 + x) = log\|Gamma(1 + x)\|, accurate near x = 0 and x = 1, where it is near 0.
(not in R)	`log1mexp`	log(1 - exp(-x)) for x >= 0, accurate for both small and large x; note the sign of the argument.
(not in R)	`log1pexp`	log(1 + exp(x)), without overflow for large x and accurate for very negative x.
(not in R)	`log1pmx`	log(1 + x) - x, accurate for small \|x\| (where log1p(x) - x would cancel).
(not in R)	`perm`	Number of ordered selections of k items from n: n! / (n - k)!, defined for real n >= 0 as Gamma(n + 1) / Gamma(n - k + 1).

Probability distributions and random numbers (`rStats.js/distributions`)

R	rStats.js	Description
`dbeta()`	`dbeta`	Density of the beta distribution.
`dbinom()`	`dbinom`	Probability mass function of the binomial distribution.
`dcauchy()`	`dcauchy`	Density of the Cauchy distribution.
`dchisq()`	`dchisq`	Density of the chi-squared distribution.
`dexp()`	`dexp`	Density of the exponential distribution.
`df()`	`df`	Density of the F distribution.
`dgamma()`	`dgamma`	Density of the gamma distribution.
`dgeom()`	`dgeom`	Probability mass function of the geometric distribution.
`dhyper()`	`dhyper`	Probability mass function of the hypergeometric distribution.
`extraDistr::dinvgamma()`	`dinvgamma`	Density of the inverse gamma distribution.
`extraDistr::dkumar()`	`dkumar`	Density of the Kumaraswamy distribution.
`extraDistr::dlaplace()`	`dlaplace`	Density of the Laplace distribution.
`dlnorm()`	`dlnorm`	Density of the log-normal distribution.
`dlogis()`	`dlogis`	Density of the logistic distribution.
`dmultinom()`	`dmultinom`	Probability function of the multinomial distribution.
`dnbinom()`	`dnbinom`	Probability mass function of the negative binomial distribution.
`dnorm()`	`dnorm`	Density of the normal distribution.
`extraDistr::dpareto()`	`dpareto`	Density of the Pareto distribution.
`dpois()`	`dpois`	Probability mass function of the Poisson distribution.
`dsignrank()`	`dsignrank`	Probability mass function of the distribution of the Wilcoxon signed rank statistic.
`dt()`	`dt`	Density of the t distribution.
`extraDistr::dtriang()`	`dtriang`	Density of the triangular distribution.
`dunif()`	`dunif`	Density of the uniform distribution.
`dweibull()`	`dweibull`	Density of the Weibull distribution.
`dwilcox()`	`dwilcox`	Probability mass function of the distribution of the Wilcoxon rank sum statistic.
`parallel::nextRNGStream()`	`nextRNGStream`	The start of the next stream of L’Ecuyer-CMRG: the state advanced by 2^127 steps.
`parallel::nextRNGSubStream()`	`nextRNGSubStream`	The start of the next substream of L’Ecuyer-CMRG: the state advanced by 2^76 steps.
`pbeta()`	`pbeta`	Distribution function of the beta distribution.
`pbinom()`	`pbinom`	Distribution function of the binomial distribution.
`pcauchy()`	`pcauchy`	Distribution function of the Cauchy distribution.
`pchisq()`	`pchisq`	Distribution function of the chi-squared distribution.
`pexp()`	`pexp`	Distribution function of the exponential distribution.
`pf()`	`pf`	Distribution function of the F distribution.
`pgamma()`	`pgamma`	Distribution function of the gamma distribution.
`pgeom()`	`pgeom`	Distribution function of the geometric distribution.
`phyper()`	`phyper`	Distribution function of the hypergeometric distribution.
`extraDistr::pinvgamma()`	`pinvgamma`	Distribution function of the inverse gamma distribution.
`extraDistr::pkumar()`	`pkumar`	Distribution function of the Kumaraswamy distribution.
`extraDistr::plaplace()`	`plaplace`	Distribution function of the Laplace distribution.
`plnorm()`	`plnorm`	Distribution function of the log-normal distribution.
`plogis()`	`plogis`	Distribution function of the logistic distribution.
`mvtnorm::pmvnorm()`	`pbvnorm`	Probability that a standard bivariate normal pair (X, Y) with correlation `rho` lies in the rectangle `lower ≤ (X, Y) ≤ upper`.
`pnbinom()`	`pnbinom`	Distribution function of the negative binomial distribution.
`pnorm()`	`pnorm`	Distribution function of the normal distribution.
`extraDistr::ppareto()`	`ppareto`	Distribution function of the Pareto distribution.
`ppois()`	`ppois`	Distribution function of the Poisson distribution.
`psignrank()`	`psignrank`	Distribution function of the distribution of the Wilcoxon signed rank statistic.
`pt()`	`pt`	Distribution function of the t distribution.
`extraDistr::ptriang()`	`ptriang`	Distribution function of the triangular distribution.
`ptukey()`	`ptukey`	Distribution function of the studentized range distribution.
`punif()`	`punif`	Distribution function of the uniform distribution.
`pweibull()`	`pweibull`	Distribution function of the Weibull distribution.
`pwilcox()`	`pwilcox`	Distribution function of the distribution of the Wilcoxon rank sum statistic.
`qbeta()`	`qbeta`	Quantile function of the beta distribution.
`qbinom()`	`qbinom`	Quantile function of the binomial distribution.
`qcauchy()`	`qcauchy`	Quantile function of the Cauchy distribution.
`qchisq()`	`qchisq`	Quantile function of the chi-squared distribution.
`qexp()`	`qexp`	Quantile function of the exponential distribution.
`qf()`	`qf`	Quantile function of the F distribution.
`qgamma()`	`qgamma`	Quantile function of the gamma distribution.
`qgeom()`	`qgeom`	Quantile function of the geometric distribution.
`qhyper()`	`qhyper`	Quantile function of the hypergeometric distribution.
`extraDistr::qinvgamma()`	`qinvgamma`	Quantile function of the inverse gamma distribution.
`extraDistr::qkumar()`	`qkumar`	Quantile function of the Kumaraswamy distribution.
`extraDistr::qlaplace()`	`qlaplace`	Quantile function of the Laplace distribution.
`qlnorm()`	`qlnorm`	Quantile function of the log-normal distribution.
`qlogis()`	`qlogis`	Quantile function of the logistic distribution.
`qnbinom()`	`qnbinom`	Quantile function of the negative binomial distribution.
`qnorm()`	`qnorm`	Quantile function of the normal distribution.
`extraDistr::qpareto()`	`qpareto`	Quantile function of the Pareto distribution.
`qpois()`	`qpois`	Quantile function of the Poisson distribution.
`qsignrank()`	`qsignrank`	Quantile function of the distribution of the Wilcoxon signed rank statistic.
`qt()`	`qt`	Quantile function of the t distribution.
`extraDistr::qtriang()`	`qtriang`	Quantile function of the triangular distribution.
`qtukey()`	`qtukey`	Quantile function of the studentized range distribution.
`qunif()`	`qunif`	Quantile function of the uniform distribution.
`qweibull()`	`qweibull`	Quantile function of the Weibull distribution.
`qwilcox()`	`qwilcox`	Quantile function of the distribution of the Wilcoxon rank sum statistic.
`r2dtable()`	`r2dtable`	Random two-way tables with given row and column totals.
`.Random.seed`	`getRandomSeed`	Returns a copy of the current generator state, as reading R’s `.Random.seed` does.
`.Random.seed`	`setRandomSeed`	Restores a generator state, including its kinds, as assigning R’s `.Random.seed` does.
`rbeta()`	`rbeta`	Random numbers from the beta distribution.
`rbinom()`	`rbinom`	Random numbers from the binomial distribution.
`rcauchy()`	`rcauchy`	Random numbers from the Cauchy distribution.
`rchisq()`	`rchisq`	Random numbers from the chi-squared distribution.
`rexp()`	`rexp`	Random numbers from the exponential distribution.
`rf()`	`rf`	Random numbers from the F distribution.
`rgamma()`	`rgamma`	Random numbers from the gamma distribution.
`rgeom()`	`rgeom`	Random numbers from the geometric distribution.
`rhyper()`	`rhyper`	Random numbers from the hypergeometric distribution.
`extraDistr::rinvgamma()`	`rinvgamma`	Random numbers from the inverse gamma distribution.
`extraDistr::rkumar()`	`rkumar`	Random numbers from the Kumaraswamy distribution.
`extraDistr::rlaplace()`	`rlaplace`	Random numbers from the Laplace distribution.
`rlnorm()`	`rlnorm`	Random numbers from the log-normal distribution.
`rlogis()`	`rlogis`	Random numbers from the logistic distribution.
`rmultinom()`	`rmultinom`	Random vectors from the multinomial distribution.
`rnbinom()`	`rnbinom`	Random numbers from the negative binomial distribution.
`RNGkind()`	`RNGkind`	Reads or changes the kinds of random number generation.
`rnorm()`	`rnorm`	Random numbers from the normal distribution.
`extraDistr::rpareto()`	`rpareto`	Random numbers from the Pareto distribution.
`rpois()`	`rpois`	Random numbers from the Poisson distribution.
`rsignrank()`	`rsignrank`	Random numbers from the distribution of the Wilcoxon signed rank statistic.
`rt()`	`rt`	Random numbers from Student’s t distribution.
`extraDistr::rtriang()`	`rtriang`	Random numbers from the triangular distribution.
`runif()`	`runif`	Random numbers from the uniform distribution.
`rweibull()`	`rweibull`	Random numbers from the Weibull distribution.
`rwilcox()`	`rwilcox`	Random numbers from the distribution of the Wilcoxon rank sum statistic.
`sample.int()`	`sampleInt`	Random sample of integers from `0..n-1`.
`sample()`	`sample`	Random sample of the elements of an array.
`set.seed()`	`setSeed`	Seeds the random number generator.
(not in R)	`darcsine`	Density of the arcsine distribution on (a, b), f(x) = 1 / (pi sqrt((x - a)(b - x))).
(not in R)	`distSummary`	Mean, variance, standard deviation, median, mode, skewness and excess kurtosis of a distribution, given by the name of its d/p/q/r functions and their parameters.
(not in R)	`parcsine`	Distribution function of the arcsine distribution on (a, b).
(not in R)	`qarcsine`	Quantile function of the arcsine distribution on (a, b).
(not in R)	`rarcsine`	Random numbers from the arcsine distribution on (a, b), by inversion.
(not in R)	`setWarningHandler`	Sets the function that receives R’s warnings, and returns the one it replaces.

Hypothesis tests and models (`rStats.js/tests`)

R	rStats.js	Description
`nortest::ad.test()`	`adTest`	Anderson-Darling test for normality.
`psych::alpha()`	`cronbachAlpha`	Cronbach’s alpha of a set of items, standardized alpha, Guttman’s lambda 6 and the mean inter-item correlation, and alpha with each item dropped.
`anova()`	`anova`	Analysis of variance of linear models fitted from a formula.
`car::Anova()`	`Anova`	Type II or type III tests of the terms of a linear model.
`anova()`	`glmAnova`	Analysis of deviance, for one `glm` fit or for several fits of the same data (`anova(fit1, fit2, ...)`).
`ansari.test()`	`ansariTest`	Ansari-Bradley test that two samples differ in scale.
`afex::aov_ez()`	`aovEz`	Repeated-measures, between-subjects and mixed analysis of variance from long data.
`aov()`	`aov`	Analysis of variance.
`pROC::auc()`	`auc`	The area under a ROC curve, or the partial area between two specificities or sensitivities.
`bartlett.test()`	`bartlettTest`	Bartlett’s test that the variances of several normal samples are equal.
`binom.test()`	`binomTest`	Exact test of a simple null hypothesis about the probability of success in Bernoulli trials.
`DescTools::BinomCI()`	`binomCI`	Confidence interval for a binomial proportion x/n.
`DescTools::BinomDiffCI()`	`binomDiffCI`	Confidence interval for the difference p1 - p2 of two independent proportions x1/n1 and x2/n2.
`DescTools::BinomRatioCI()`	`binomRatioCI`	Confidence interval for the ratio p1/p2 of two independent proportions x1/n1 and x2/n2.
`BlandAltmanLeh::bland.altman.stats()`	`blandAltman`	Bland-Altman analysis of two methods of measurement: the mean difference, the limits of agreement and their confidence intervals.
`lawstat::brunner.munzel.test()`	`brunnerMunzelTest`	Brunner-Munzel test that two samples have the relative effect P(X < Y) + P(X = Y)/2 = 1/2, without assuming equal variances or shapes.
`chisq.test()`	`chisqTest`	Pearson’s chi-squared test.
`pROC::ci.auc()`	`ciAuc`	Confidence interval of the area under a ROC curve, by DeLong’s method or the bootstrap.
`DescTools::CochranQTest()`	`cochranQTest`	Cochran’s Q test for binary responses in an unreplicated complete block design.
`lmtest::coefci()`	`coefci`	Confidence intervals of the coefficients of an `lm` or `glm` fit with a given covariance matrix.
`lmtest::coeftest()`	`coeftest`	Tests of the coefficients of an `lm` or `glm` fit with a given covariance matrix.
`pwr::cohen.ES()`	`cohenES`	Cohen’s conventional effect sizes.
`effectsize::cohens_d()`	`cohensD`	Cohen’s d: the standardized difference of means of two samples, of paired samples, or of one sample against `mu`, with a confidence interval from the noncentral t distribution.
`confint()`	`confint`	Confidence intervals for the coefficients of a linear model, for an `lm` fit.
`confint()`	`glmConfint`	Profile likelihood confidence intervals for the coefficients of a generalized linear model, for a `glm` fit.
`cooks.distance()`	`glmCooksDistance`	Cook’s distances, for the rows of non-zero prior weight, for a `glm` fit.
`pROC::coords()`	`coords`	Coordinates of a ROC curve: sensitivity, specificity, the counts, predictive values, likelihood ratios and the criteria of the best threshold, at every point of the curve, at the best threshold(s) or at given thresholds.
`cor.test()`	`corTest`	Test for correlation between paired samples.
`survival::coxph()`	`coxph`	Cox proportional hazards regression.
`nortest::cvm.test()`	`cvmTest`	Cramer-von Mises test for normality.
`PMCMRplus::dscfAllPairsTest()`	`dscfAllPairsTest`	Dwass-Steel-Critchlow-Fligner all-pairs test of pairwise Mann-Whitney statistics.
`dunn.test::dunn.test()`	`dunnTest`	Dunn’s test of all pairs of mean ranks.
`PMCMRplus::dunnettTest()`	`dunnettTest`	Dunnett’s many-to-one test of means against the control, with the pooled variance.
`epiR::epi.tests()`	`diagnosticTests`	Sensitivity, specificity, predictive values, likelihood ratios, accuracy, the diagnostic odds ratio and the true and apparent prevalence of a binary test, each with a confidence interval, from its 2 × 2 table.
`effectsize::epsilon_squared()`	`epsilonSquared`	Epsilon squared of the terms of an analysis-of-variance table, also of repeated-measures designs.
`pwr::ES.h()`	`esH`	Effect size h of two proportions.
`pwr::ES.w1()`	`esW1`	Effect size w of a goodness-of-fit test, `sqrt(sum((p1 - p0)^2 / p0))`.
`pwr::ES.w2()`	`esW2`	Effect size w of a test of association, for a matrix of cell probabilities: with the row sums r and column sums c, `p0 = r c'` and `sqrt(sum((p - p0)^2 / p0))`.
`effectsize::eta_squared()`	`etaSquared`	Eta squared of the terms of an analysis-of-variance table, with confidence intervals from the noncentral F distribution.
`Exact::exact.test()`	`boschlooTest`	Boschloo’s exact unconditional test for a 2 x 2 table whose rows are two independent binomial samples.
`factanal()`	`factanal`	Maximum-likelihood factor analysis of a data matrix or, with `options.covmat`, of a covariance matrix.
`fisher.test()`	`fisherTest`	Fisher’s exact test for count data.
`fligner.test()`	`flignerTest`	Fligner-Killeen test of homogeneity of variances.
`friedman.test()`	`friedmanTest`	Friedman rank sum test for an unreplicated complete block design.
`PMCMRplus::gamesHowellTest()`	`gamesHowellTest`	Games-Howell all-pairs test of means with Welch’s standard errors and degrees of freedom.
`multcomp::glht()`	`glhtDunnett`	Dunnett’s many-to-one comparisons of a one-way ANOVA with simultaneous p-values.
`glm()`	`glm`	Fit a generalized linear model by iteratively reweighted least squares.
`DescTools::GoodmanKruskalGamma()`	`goodmanKruskalGamma`	Goodman and Kruskal’s gamma of a table of two ordered classifications, with its asymptotic standard error and a Wald confidence interval.
`DescTools::GTest()`	`gTest`	Log-likelihood ratio (G) test of independence in a contingency table, or of goodness of fit of counts to given probabilities.
`hatvalues()`	`glmHatvalues`	Diagonal of the hat matrix of the last weighted least squares step, for the rows of non-zero prior weight, for a `glm` fit.
`effectsize::hedges_g()`	`hedgesG`	Hedges’ g.
`irr::icc()`	`icc`	Intraclass correlation coefficient with its F test and confidence interval.
`psych::ICC()`	`ICC`	The six intraclass correlations of Shrout and Fleiss (1979), with their F tests and confidence intervals.
`DescTools::JonckheereTerpstraTest()`	`jonckheereTest`	Jonckheere-Terpstra test of equal distributions against ordered alternatives.
`irr::kappa2()`	`kappa2`	Cohen’s kappa for two raters, unweighted or weighted, with the z test of kappa = 0.
`irr::kappam.fleiss()`	`kappamFleiss`	Fleiss’ kappa for several raters, with the z test of kappa = 0 for Fleiss’ kappa.
`kruskal.test()`	`kruskalTest`	Kruskal-Wallis rank sum test.
`ks.test()`	`ksTest`	Kolmogorov-Smirnov test.
`PMCMRplus::kwAllPairsDunnTest()`	`kwAllPairsDunnTest`	Dunn’s all-pairs test of mean ranks with adjusted p-values.
`PMCMRplus::kwManyOneDunnTest()`	`kwManyOneDunnTest`	Dunn’s many-to-one test of mean ranks against the control.
`coin::lbl_test()`	`lblTest`	Asymptotic linear-by-linear association test of a table of two ordered classifications.
`car::leveneTest()`	`leveneTest`	Levene’s test for homogeneity of variance across groups.
`nortest::lillie.test()`	`lillieTest`	Lilliefors test for normality.
`lm()`	`lm`	Fit a linear model from a formula.
`mantelhaen.test()`	`mantelhaenTest`	Cochran-Mantel-Haenszel chi-squared test of conditional independence in a set of contingency tables, one per stratum.
`PMCMRplus::manyOneUTest()`	`manyOneUTest`	Steel’s many-to-one rank test in its normal approximation: the Mann-Whitney statistic of each group against the control, each pair ranked on its own, standardised with the tie-corrected variance.
`mauchly.test()`	`mauchlyTest`	Mauchly’s test of sphericity of the measurements of each subject, on the contrasts spanned by `M` and orthogonal to `X`.
`mcnemar.test()`	`mcnemarTest`	McNemar’s chi-squared test for symmetry of rows and columns in a square table of counts.
`mcr::mcreg()`	`mcreg`	Method comparison regression of y on x.
`model.matrix()`	`modelMatrix`	The model matrix of a formula on a data frame, with the attributes of R’s `terms()`.
`mood.test()`	`moodTest`	Mood’s two-sample test of scale.
`epitools::oddsratio()`	`oddsRatio`	Odds ratio of a 2 x 2 table with its confidence interval and three p-values of independence.
`effectsize::omega_squared()`	`omegaSquared`	Omega squared of the terms of an analysis-of-variance table, also of repeated-measures designs.
`psych::omega()`	`omega`	McDonald’s omega from a Schmid-Leiman transformation of an oblique minres factor solution.
`oneway.test()`	`onewayTest`	Test that several normal samples have equal means, without or with the assumption of equal variances.
`p.adjust()`	`pAdjust`	Adjusts p-values for multiple comparisons.
`pairwise.prop.test()`	`pairwisePropTest`	Pairwise two-group tests of equal proportions, with adjusted p-values.
`pairwise.t.test()`	`pairwiseTTest`	Pairwise t tests between groups, with adjusted p-values.
`pairwise.wilcox.test()`	`pairwiseWilcoxTest`	Pairwise Wilcoxon rank sum tests between groups, with adjusted p-values.
`psych::partial.r()`	`partialR`	Partial correlation of columns `i` and `j` of `x` given all its other columns.
`nortest::pearson.test()`	`pearsonTest`	Pearson chi-square test for normality, with classes of equal probability under the fitted normal distribution.
`poisson.test()`	`poissonTest`	Exact test of a Poisson rate, or comparison of two Poisson rates.
`polycor::polychor()`	`polychor`	Polychoric correlation of two ordinal variables.
`polycor::polyserial()`	`polyserial`	Polyserial correlation of a numeric variable and an ordinal one.
`power.anova.test()`	`powerAnovaTest`	Power of the F test of a balanced one-way analysis of variance, or one of the other quantities for a given power.
`power.prop.test()`	`powerPropTest`	Power of the test of two proportions, or the sample size, a probability or the significance level for a given power.
`power.t.test()`	`powerTTest`	Power of a one- or two-sample t test, or the sample size, difference, standard deviation or significance level for a given power.
`predict()`	`glmPredict`	Predictions of a generalized linear model with their standard errors, for a `glm` fit.
`predict()`	`predict`	Predictions of a linear model at new values of the predictors, with standard errors and optionally intervals, for an `lm` fit.
`prop.test()`	`propTest`	Test of equal or given proportions.
`prop.trend.test()`	`propTrendTest`	Chi-squared test for trend in proportions.
`pwr::pwr.2p.test()`	`pwr2pTest`	Power of the test of two proportions with equal sample sizes, or the effect size, sample size or significance level for a given power.
`pwr::pwr.2p2n.test()`	`pwr2p2nTest`	Power of the test of two proportions with unequal sample sizes, or the effect size, a sample size or the significance level for a given power.
`pwr::pwr.anova.test()`	`pwrAnovaTest`	Power of the F test of a balanced one-way analysis of variance, or the number of groups, sample size, effect size or significance level for a given power.
`pwr::pwr.chisq.test()`	`pwrChisqTest`	Power of a chi-squared test, or the effect size, number of observations or significance level for a given power.
`pwr::pwr.f2.test()`	`pwrF2Test`	Power of the F test of a general linear model, or the degrees of freedom, effect size or significance level for a given power.
`pwr::pwr.norm.test()`	`pwrNormTest`	Power of the test of a mean of a normal distribution with known variance, or the effect size, sample size or significance level for a given power.
`pwr::pwr.p.test()`	`pwrPTest`	Power of the test of one proportion, or the effect size, sample size or significance level for a given power.
`pwr::pwr.r.test()`	`pwrRTest`	Power of the test of a correlation, or the sample size, correlation or significance level for a given power.
`pwr::pwr.t.test()`	`pwrTTest`	Power of a t test of means, or the sample size, effect size or significance level for a given power.
`pwr::pwr.t2n.test()`	`pwrT2nTest`	Power of a two-sample t test with unequal sample sizes, or a sample size, the effect size or the significance level for a given power.
`quade.test()`	`quadeTest`	Quade test for an unreplicated complete block design.
`residuals()`	`glmResiduals`	Residuals of a generalized linear model, one per row, for a `glm` fit.
`pROC::roc.test()`	`rocTest`	Tests the difference of the areas under two ROC curves, by DeLong’s method or the bootstrap.
`pROC::roc()`	`roc`	The ROC curve of a numeric predictor of a binary response.
`rstandard()`	`glmRstandard`	Standardized Pearson or deviance residuals, for the rows of non-zero prior weight, for a `glm` fit.
`nortest::sf.test()`	`sfTest`	Shapiro-Francia test for normality.
`shapiro.test()`	`shapiroTest`	Shapiro-Wilk test of normality.
`DescTools::StuartMaxwellTest()`	`stuartMaxwellTest`	Stuart-Maxwell test of marginal homogeneity of a square table.
`summary()`	`aovSummary`	The table of an analysis of variance fitted by `aov`.
`summary()`	`glmSummary`	Summary of a generalized linear model: the dispersion, the coefficient table and the covariance of the estimates, for a `glm` fit.
`survival::survdiff()`	`survdiff`	Test of the difference between survival curves: the G-rho family of Harrington and Fleming, the log-rank test for `rho = 0`.
`survival::survfit()`	`survfit`	Kaplan-Meier estimate of the survival function with Greenwood’s standard error and pointwise confidence intervals.
`TOSTER::t_TOST()`	`tTOST`	Two one-sided t tests of equivalence, or minimal-effect tests, for one sample, two samples or paired samples, with the standardised mean difference and its interval.
`t.test()`	`tTest`	Student’s t-test.
`equivalence::tost()`	`tost`	Two one-sided t tests of equivalence of two means.
`TOSTER::tsum_TOST()`	`tsumTOST`	`tTOST` from summary statistics: means, standard deviations, sample sizes and, for paired samples, the correlation of the pairs.
`TukeyHSD()`	`TukeyHSD`	Tukey’s honest significant differences between the group means of a one-way ANOVA.
`PMCMRplus::tukeyTest()`	`tukeyTest`	Tukey’s all-pairs test of means.
`var.test()`	`varTest`	F test that the variances of two normal samples have a given ratio.
`sandwich::vcovCL()`	`vcovCL`	Clustered covariance matrix of the coefficients of an `lm` or `glm` fit.
`sandwich::vcovHC()`	`vcovHC`	Heteroskedasticity-consistent covariance matrix of the coefficients of an `lm` or `glm` fit.
`wilcox.test()`	`wilcoxTest`	Wilcoxon signed rank and rank sum tests.
`BSDA::z.test()`	`zTest`	z test for the mean of one sample, or the difference of the means of two, with known standard deviations.
(not in R)	`binomDiffEquivalenceTest`	Test of equivalence of two independent proportions x1/n1 and x2/n2 within a margin.
(not in R)	`setWarningHandler`	Sets the function that receives R’s warnings, and returns the one it replaces.

Linear algebra (`rStats.js/linalg`)

R	rStats.js	Description
`-`	`subtract`	Element-wise difference with R’s recycling.
`*`	`multiply`	Element-wise product with R’s recycling.
`%*%`	`matMul`	Matrix product.
`/`	`divide`	Element-wise quotient with R’s recycling.
`+`	`add`	Element-wise sum with R’s recycling.
`backsolve()`	`backsolve`	Solves the triangular system `r y = x` with a vector right-hand side.
`cbind()`	`cbind`	Combine vectors and matrices as columns.
`chol()`	`chol`	Cholesky decomposition.
`chol2inv()`	`chol2inv`	Inverse of a matrix from its Cholesky factor: computes `(t(R) R)^-1`.
`crossprod()`	`crossprod`	Matrix cross-product `t(x) %% y`, or `t(x) %% x` when `y` is omitted.
`det()`	`det`	Determinant of a square matrix.
`determinant()`	`determinant`	Determinant as modulus and sign.
`diag()`	`diag`	Matrix diagonals, diagonal matrices and identity matrices.
`eigen()`	`eigen`	Eigenvalues and eigenvectors of a square matrix.
`expm::expm()`	`expm`	Matrix exponential.
`forwardsolve()`	`forwardsolve`	Solves the triangular system `l y = x` with a vector right-hand side.
`MASS::ginv()`	`ginv`	Moore-Penrose generalized inverse.
`kappa()`	`kappa`	Condition number of a matrix, estimated or exact.
`kronecker()`	`kronecker`	Kronecker product.
`Matrix::lu()`	`lu`	LU decomposition with partial pivoting, `x = P L U`.
`matrix()`	`matrix`	Build a matrix from data, recycled to fill it.
`norm()`	`norm`	Matrix norm.
`outer()`	`outer`	Outer product of two vectors: the matrix of `fun(x[i], y[j])`.
`prcomp()`	`prcomp`	Principal components of the columns of `x`.
`qr.coef()`	`qrCoef`	Least-squares coefficients for a vector response.
`qr.fitted()`	`qrFitted`	Least-squares fitted values for a vector response: the projection of `y` onto the space spanned by the first `k` pivoted columns.
`qr.Q()`	`qrQ`	Recovers the orthogonal matrix `Q` from a QR decomposition.
`qr.qty()`	`qrQty`	Computes `t(Q) %*% y` for a vector `y`.
`qr.qy()`	`qrQy`	Computes `Q %*% y` for a vector `y`.
`qr.R()`	`qrR`	Recovers the upper triangular matrix `R` from a QR decomposition.
`qr.resid()`	`qrResid`	Least-squares residuals for a vector response.
`qr.solve()`	`qrSolve`	Solves `a x = b` in the least-squares sense using a QR decomposition.
`qr.X()`	`qrX`	Reconstructs the original matrix from a QR decomposition, with columns back in their original order.
`qr()`	`qr`	QR decomposition.
`rbind()`	`rbind`	Combine vectors and matrices as rows.
`rcond()`	`rcond`	Estimate of the reciprocal condition number of a matrix.
`Matrix::Schur()`	`schur`	Real Schur decomposition of a square matrix.
`seq()`	`seq`	Regular sequences.
`solve()`	`solve`	Inverse of a square matrix.
`svd()`	`svd`	Singular value decomposition.
`t()`	`t`	Transpose.
`tcrossprod()`	`tcrossprod`	Matrix cross-product `x %% t(y)`, or `x %% t(x)` when `y` is omitted.

Descriptive statistics (`rStats.js/descriptive`)

R	rStats.js	Description
`aggregate()`	`aggregate`	The function applied to the values of each group, as a data frame.
`DescTools::CoefVar()`	`cv`	Coefficient of variation sd / mean.
`complete.cases()`	`completeCases`	Which rows have no missing value.
`cor()`	`cor`	Correlation of two vectors, or correlation matrix of the columns of a matrix.
`cov()`	`cov`	Covariance of two vectors, or covariance matrix of the columns of a matrix.
`cummax()`	`cummax`	Running maxima.
`cummin()`	`cummin`	Running minima.
`cumprod()`	`cumprod`	Running products.
`cumsum()`	`cumsum`	Running sums.
`cut()`	`cut`	Interval numbers of the values, or NaN outside the breaks.
`diff()`	`diff`	Lagged and iterated differences.
`ecdf()`	`ecdf`	Empirical distribution function of `x`: a function returning the fraction of values at or below its argument.
`findInterval()`	`findInterval`	For each value, the number of break points at or below it.
`fivenum()`	`fivenum`	Tukey’s five-number summary.
`DescTools::Gmean()`	`geometricMean`	Geometric mean exp(mean(log x)).
`hist()`	`hist`	Histogram counts.
`IQR()`	`IQR`	Interquartile range, the difference of the 0.75 and 0.25 quantiles.
`e1071::kurtosis()`	`kurtosis`	Sample excess kurtosis.
`mad()`	`mad`	Median absolute deviation, scaled.
`mean()`	`mean`	Arithmetic mean, optionally trimmed.
`DescTools::MeanAD()`	`meanAD`	Mean absolute deviation from the centre.
`median()`	`median`	Median of the values.
`merge()`	`merge`	Join two data frames on key columns.
`DescTools::Mode()`	`modes`	The most frequent values and their frequency.
`na.omit()`	`naOmit`	The values that are not missing.
`tidyr::pivot_longer()`	`pivotLonger`	Reshapes a data frame from wide to long: the selected columns become rows.
`tidyr::pivot_wider()`	`pivotWider`	Reshapes a data frame from long to wide: the values of `namesFrom` become columns, filled from `valuesFrom`.
`pretty()`	`pretty`	About n + 1 equally spaced “round” values covering the range of `x`.
`prod()`	`prod`	Product of the values.
`quantile()`	`quantile`	Sample quantiles.
`range()`	`range`	`[min, max]` of the values.
`rank()`	`rank`	Sample ranks.
`scale()`	`scale`	Centre and scale the columns of a matrix.
`sd()`	`sd`	Sample standard deviation.
`e1071::skewness()`	`skewness`	Sample skewness.
`split()`	`split`	The values of `x` divided into groups.
`subset()`	`subset`	The rows of a data frame that meet a condition, and optionally some of its columns.
`sum()`	`sum`	Sum of the values.
`table()`	`table`	Frequency table of the distinct values.
`tabulate()`	`tabulate`	Counts of the integers 1..nbins in `bin`.
`tapply()`	`tapply`	The function applied to the values of each group.
`unique()`	`unique`	Distinct values in order of first appearance.
`var()`	`variance`	Sample variance.
`weighted.mean()`	`weightedMean`	Weighted mean `sum(x w) / sum(w)`.
(not in R)	`fromRows`	A data frame from an array of row objects, the usual shape of JSON and of parsed CSV.
(not in R)	`toRows`	The rows of a data frame as an array of objects, the inverse of `fromRows`.

Numerical methods (`rStats.js/numerics`)

R	rStats.js	Description
`approx()`	`approx`	Linear or constant interpolation.
`pracma::barylag()`	`barylag`	Lagrange interpolation in barycentric form through the points (xi, yi).
`numDeriv::grad()`	`grad`	Gradient of a scalar function of a vector.
`numDeriv::hessian()`	`hessian`	Hessian matrix of a scalar function of a vector, by Richardson extrapolation.
`integrate()`	`integrate`	Integral of a function of one variable over a finite or infinite range.
`numDeriv::jacobian()`	`jacobian`	Jacobian of a vector function of a vector: row i holds the derivatives of the i-th value.
`deSolve::ode()`	`ode`	Solve the initial value problem y’ = func(t, y, parms), y(times[0]) = y, with one fixed step per interval of `times`.
`optim()`	`optim`	General-purpose minimisation of `fn` from the starting point `par`.
`optimHess()`	`optimHess`	Numerically differentiated Hessian of fn at par, from central differences of the gradient.
`optimize()`	`optimize`	Minimum of a function of one variable on an interval, by Brent’s golden-section and parabolic search.
`spline()`	`spline`	Cubic spline interpolation at given or equally spaced points.
`splinefun()`	`splinefun`	The cubic spline through the points, as a function of `x` and the order of derivative.
`splinefunH()`	`splinefunH`	The cubic Hermite spline with values `y` and slopes `m` at the points `x`, as a function of `x` and the order of derivative; linear outside the data.
`uniroot()`	`uniroot`	A root of a function of one variable in an interval, by Brent’s zeroin.
(not in R)	`hermiteInterp`	The polynomial of degree 2n - 1 through n points with given values and derivatives, evaluated at `x`.

Mixed models (`rStats.js/mixed`)

R	rStats.js	Description
`lme4::anova()`	`glmerAnova`	The ANOVA table of the fixed-effects terms of a generalized linear mixed model fitted by `glmer`, or, given an array of fits, the likelihood-ratio comparison of the models.
`lmerTest::anova()`	`lmerAnova`	The ANOVA table of a linear mixed model fitted by `lmer`, with Satterthwaite’s denominator degrees of freedom (lmerTest’s `anova()`), or, given an array of fits, the likelihood-ratio comparison of the models (lme4’s `anova()` of several fits).
`lmerTest::contest()`	`lmerContest`	Tests of contrasts L β = rhs of the fixed effects of a linear mixed model fitted by `lmer`, with Satterthwaite’s degrees of freedom.
`lme4::glmer()`	`glmer`	Fit a generalized linear mixed model by the Laplace approximation, by adaptive Gauss-Hermite quadrature or with the fixed effects from the penalised IRLS.
`lme4::lmer()`	`lmer`	Fit a linear mixed model by REML or maximum likelihood.
`lme4::predict()`	`glmerPredict`	Predictions of a generalized linear mixed model fitted by `glmer`, at the rows of the fit or at new data, with all, some or none of the random-effects terms, on the link or the response scale, optionally with their standard errors.
`lme4::predict()`	`lmerPredict`	Predictions of a linear mixed model fitted by `lmer`, at the rows of the fit or at new data, with all, some or none of the random-effects terms, optionally with their standard errors.
`lme4::summary()`	`glmerSummary`	The summary of a generalized linear mixed model fitted by `glmer`: the coefficient table with p-values, the correlation of the fixed effects, the quantiles of the scaled residuals, the information criteria, the variance components and the numbers of groups.
`lmerTest::summary()`	`lmerSummary`	The coefficient table of a linear mixed model fitted by `lmer`, with Satterthwaite’s degrees of freedom and the p-values of the t-tests: the `summary()` of a fit by lmerTest.
(not in R)	`setWarningHandler`	Sets the function that receives R’s warnings, and returns the one it replaces.

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rStats.js

R functions in rStats.js

Special functions (rStats.js/special)

Probability distributions and random numbers (rStats.js/distributions)

Hypothesis tests and models (rStats.js/tests)

Linear algebra (rStats.js/linalg)

Descriptive statistics (rStats.js/descriptive)

Numerical methods (rStats.js/numerics)

Mixed models (rStats.js/mixed)