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statistical.js
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var mathTrig = require('./math-trig');
var text = require('./text');
var jStat = require('jStat').jStat;
var utils = require('./utils');
var error = require('./error');
var misc = require('./miscellaneous');
var SQRT2PI = 2.5066282746310002;
exports.AVEDEV = function() {
var range = utils.parseNumberArray(utils.flatten(arguments));
if (range instanceof Error) {
return range;
}
return jStat.sum(jStat(range).subtract(jStat.mean(range)).abs()[0]) / range.length;
};
exports.AVERAGE = function() {
var range = utils.numbers(utils.flatten(arguments));
var n = range.length;
var sum = 0;
var count = 0;
for (var i = 0; i < n; i++) {
sum += range[i];
count += 1;
}
return sum / count;
};
exports.AVERAGEA = function() {
var range = utils.flatten(arguments);
var n = range.length;
var sum = 0;
var count = 0;
for (var i = 0; i < n; i++) {
var el = range[i];
if (typeof el === 'number') {
sum += el;
}
if (el === true) {
sum++;
}
if (el !== null) {
count++;
}
}
return sum / count;
};
exports.AVERAGEIF = function(range, criteria, average_range) {
average_range = average_range || range;
range = utils.flatten(range);
average_range = utils.parseNumberArray(utils.flatten(average_range));
if (average_range instanceof Error) {
return average_range;
}
var average_count = 0;
var result = 0;
for (var i = 0; i < range.length; i++) {
if (eval(range[i] + criteria)) { // jshint ignore:line
result += average_range[i];
average_count++;
}
}
return result / average_count;
};
exports.AVERAGEIFS = function() {
// Does not work with multi dimensional ranges yet!
//http://office.microsoft.com/en-001/excel-help/averageifs-function-HA010047493.aspx
var args = utils.argsToArray(arguments);
var criteria = (args.length - 1) / 2;
var range = utils.flatten(args[0]);
var count = 0;
var result = 0;
for (var i = 0; i < range.length; i++) {
var condition = '';
for (var j = 0; j < criteria; j++) {
condition += args[2 * j + 1][i] + args[2 * j + 2];
if (j !== criteria - 1) {
condition += '&&';
}
}
if (eval(condition)) { // jshint ignore:line
result += range[i];
count++;
}
}
var average = result / count;
if (isNaN(average)) {
return 0;
} else {
return average;
}
};
exports.BETA = {};
exports.BETA.DIST = function(x, alpha, beta, cumulative, A, B) {
if (arguments.length < 4) {
return error.value;
}
A = (A === undefined) ? 0 : A;
B = (B === undefined) ? 1 : B;
x = utils.parseNumber(x);
alpha = utils.parseNumber(alpha);
beta = utils.parseNumber(beta);
A = utils.parseNumber(A);
B = utils.parseNumber(B);
if (utils.anyIsError(x, alpha, beta, A, B)) {
return error.value;
}
x = (x - A) / (B - A);
return (cumulative) ? jStat.beta.cdf(x, alpha, beta) : jStat.beta.pdf(x, alpha, beta);
};
exports.BETA.INV = function(probability, alpha, beta, A, B) {
A = (A === undefined) ? 0 : A;
B = (B === undefined) ? 1 : B;
probability = utils.parseNumber(probability);
alpha = utils.parseNumber(alpha);
beta = utils.parseNumber(beta);
A = utils.parseNumber(A);
B = utils.parseNumber(B);
if (utils.anyIsError(probability, alpha, beta, A, B)) {
return error.value;
}
return jStat.beta.inv(probability, alpha, beta) * (B - A) + A;
};
exports.BINOM = {};
exports.BINOM.DIST = function(successes, trials, probability, cumulative) {
successes = utils.parseNumber(successes);
trials = utils.parseNumber(trials);
probability = utils.parseNumber(probability);
cumulative = utils.parseNumber(cumulative);
if (utils.anyIsError(successes, trials, probability, cumulative)) {
return error.value;
}
return (cumulative) ? jStat.binomial.cdf(successes, trials, probability) : jStat.binomial.pdf(successes, trials, probability);
};
exports.BINOM.DIST.RANGE = function(trials, probability, successes, successes2) {
successes2 = (successes2 === undefined) ? successes : successes2;
trials = utils.parseNumber(trials);
probability = utils.parseNumber(probability);
successes = utils.parseNumber(successes);
successes2 = utils.parseNumber(successes2);
if (utils.anyIsError(trials, probability, successes, successes2)) {
return error.value;
}
var result = 0;
for (var i = successes; i <= successes2; i++) {
result += mathTrig.COMBIN(trials, i) * Math.pow(probability, i) * Math.pow(1 - probability, trials - i);
}
return result;
};
exports.BINOM.INV = function(trials, probability, alpha) {
trials = utils.parseNumber(trials);
probability = utils.parseNumber(probability);
alpha = utils.parseNumber(alpha);
if (utils.anyIsError(trials, probability, alpha)) {
return error.value;
}
var x = 0;
while (x <= trials) {
if (jStat.binomial.cdf(x, trials, probability) >= alpha) {
return x;
}
x++;
}
};
exports.CHISQ = {};
exports.CHISQ.DIST = function(x, k, cumulative) {
x = utils.parseNumber(x);
k = utils.parseNumber(k);
if (utils.anyIsError(x, k)) {
return error.value;
}
return (cumulative) ? jStat.chisquare.cdf(x, k) : jStat.chisquare.pdf(x, k);
};
exports.CHISQ.DIST.RT = function(x, k) {
if (!x | !k) {
return error.na;
}
if (x < 1 || k > Math.pow(10, 10)) {
return error.num;
}
if ((typeof x !== 'number') || (typeof k !== 'number')) {
return error.value;
}
return 1 - jStat.chisquare.cdf(x, k);
};
exports.CHISQ.INV = function(probability, k) {
probability = utils.parseNumber(probability);
k = utils.parseNumber(k);
if (utils.anyIsError(probability, k)) {
return error.value;
}
return jStat.chisquare.inv(probability, k);
};
exports.CHISQ.INV.RT = function(p, k) {
if (!p | !k) {
return error.na;
}
if (p < 0 || p > 1 || k < 1 || k > Math.pow(10, 10)) {
return error.num;
}
if ((typeof p !== 'number') || (typeof k !== 'number')) {
return error.value;
}
return jStat.chisquare.inv(1.0 - p, k);
};
exports.CHISQ.TEST = function(observed, expected) {
if (arguments.length !== 2) {
return error.na;
}
if ((!(observed instanceof Array)) || (!(expected instanceof Array))) {
return error.value;
}
if (observed.length !== expected.length) {
return error.value;
}
if (observed[0] && expected[0] &&
observed[0].length !== expected[0].length) {
return error.value;
}
var row = observed.length;
var tmp, i, j;
// Convert single-dimension array into two-dimension array
for (i = 0; i < row; i ++) {
if (!(observed[i] instanceof Array)) {
tmp = observed[i];
observed[i] = [];
observed[i].push(tmp);
}
if (!(expected[i] instanceof Array)) {
tmp = expected[i];
expected[i] = [];
expected[i].push(tmp);
}
}
var col = observed[0].length;
var dof = (col === 1) ? row-1 : (row-1)*(col-1);
var xsqr = 0;
var Pi =Math.PI;
for (i = 0; i < row; i ++) {
for (j = 0; j < col; j ++) {
xsqr += Math.pow((observed[i][j] - expected[i][j]), 2) / expected[i][j];
}
}
// Get independency by X square and its degree of freedom
function ChiSq(xsqr, dof) {
var p = Math.exp(-0.5 * xsqr);
if((dof%2) === 1) {
p = p * Math.sqrt(2 * xsqr/Pi);
}
var k = dof;
while(k >= 2) {
p = p * xsqr/k;
k = k - 2;
}
var t = p;
var a = dof;
while (t > 0.0000000001*p) {
a = a + 2;
t = t * xsqr/a;
p = p + t;
}
return 1-p;
}
return Math.round(ChiSq(xsqr, dof) * 1000000) / 1000000;
};
exports.COLUMN = function(matrix, index) {
if (arguments.length !== 2) {
return error.na;
}
if (index < 0) {
return error.num;
}
if (!(matrix instanceof Array) || (typeof index !== 'number')) {
return error.value;
}
if (matrix.length === 0) {
return undefined;
}
return jStat.col(matrix, index);
};
exports.COLUMNS = function(matrix) {
if (arguments.length !== 1) {
return error.na;
}
if (!(matrix instanceof Array)) {
return error.value;
}
if (matrix.length === 0) {
return 0;
}
return jStat.cols(matrix);
};
exports.CONFIDENCE = {};
exports.CONFIDENCE.NORM = function(alpha, sd, n) {
alpha = utils.parseNumber(alpha);
sd = utils.parseNumber(sd);
n = utils.parseNumber(n);
if (utils.anyIsError(alpha, sd, n)) {
return error.value;
}
return jStat.normalci(1, alpha, sd, n)[1] - 1;
};
exports.CONFIDENCE.T = function(alpha, sd, n) {
alpha = utils.parseNumber(alpha);
sd = utils.parseNumber(sd);
n = utils.parseNumber(n);
if (utils.anyIsError(alpha, sd, n)) {
return error.value;
}
return jStat.tci(1, alpha, sd, n)[1] - 1;
};
exports.CORREL = function(array1, array2) {
array1 = utils.parseNumberArray(utils.flatten(array1));
array2 = utils.parseNumberArray(utils.flatten(array2));
if (utils.anyIsError(array1, array2)) {
return error.value;
}
return jStat.corrcoeff(array1, array2);
};
exports.COUNT = function() {
return utils.numbers(utils.flatten(arguments)).length;
};
exports.COUNTA = function() {
var range = utils.flatten(arguments);
return range.length - exports.COUNTBLANK(range);
};
exports.COUNTIN = function (range, value) {
var result = 0;
for (var i = 0; i < range.length; i++) {
if (range[i] === value) {
result++;
}
}
return result;
};
exports.COUNTBLANK = function() {
var range = utils.flatten(arguments);
var blanks = 0;
var element;
for (var i = 0; i < range.length; i++) {
element = range[i];
if (element === null || element === '') {
blanks++;
}
}
return blanks;
};
exports.COUNTIF = function(range, criteria) {
range = utils.flatten(range);
if (!/[<>=!]/.test(criteria)) {
criteria = '=="' + criteria + '"';
}
var matches = 0;
for (var i = 0; i < range.length; i++) {
if (typeof range[i] !== 'string') {
if (eval(range[i] + criteria)) { // jshint ignore:line
matches++;
}
} else {
if (eval('"' + range[i] + '"' + criteria)) { // jshint ignore:line
matches++;
}
}
}
return matches;
};
exports.COUNTIFS = function() {
var args = utils.argsToArray(arguments);
var results = new Array(utils.flatten(args[0]).length);
for (var i = 0; i < results.length; i++) {
results[i] = true;
}
for (i = 0; i < args.length; i += 2) {
var range = utils.flatten(args[i]);
var criteria = args[i + 1];
if (!/[<>=!]/.test(criteria)) {
criteria = '=="' + criteria + '"';
}
for (var j = 0; j < range.length; j++) {
if (typeof range[j] !== 'string') {
results[j] = results[j] && eval(range[j] + criteria); // jshint ignore:line
} else {
results[j] = results[j] && eval('"' + range[j] + '"' + criteria); // jshint ignore:line
}
}
}
var result = 0;
for (i = 0; i < results.length; i++) {
if (results[i]) {
result++;
}
}
return result;
};
exports.COUNTUNIQUE = function () {
return misc.UNIQUE.apply(null, utils.flatten(arguments)).length;
};
exports.COVARIANCE = {};
exports.COVARIANCE.P = function(array1, array2) {
array1 = utils.parseNumberArray(utils.flatten(array1));
array2 = utils.parseNumberArray(utils.flatten(array2));
if (utils.anyIsError(array1, array2)) {
return error.value;
}
var mean1 = jStat.mean(array1);
var mean2 = jStat.mean(array2);
var result = 0;
var n = array1.length;
for (var i = 0; i < n; i++) {
result += (array1[i] - mean1) * (array2[i] - mean2);
}
return result / n;
};
exports.COVARIANCE.S = function(array1, array2) {
array1 = utils.parseNumberArray(utils.flatten(array1));
array2 = utils.parseNumberArray(utils.flatten(array2));
if (utils.anyIsError(array1, array2)) {
return error.value;
}
return jStat.covariance(array1, array2);
};
exports.DEVSQ = function() {
var range = utils.parseNumberArray(utils.flatten(arguments));
if (range instanceof Error) {
return range;
}
var mean = jStat.mean(range);
var result = 0;
for (var i = 0; i < range.length; i++) {
result += Math.pow((range[i] - mean), 2);
}
return result;
};
exports.EXPON = {};
exports.EXPON.DIST = function(x, lambda, cumulative) {
x = utils.parseNumber(x);
lambda = utils.parseNumber(lambda);
if (utils.anyIsError(x, lambda)) {
return error.value;
}
return (cumulative) ? jStat.exponential.cdf(x, lambda) : jStat.exponential.pdf(x, lambda);
};
exports.F = {};
exports.F.DIST = function(x, d1, d2, cumulative) {
x = utils.parseNumber(x);
d1 = utils.parseNumber(d1);
d2 = utils.parseNumber(d2);
if (utils.anyIsError(x, d1, d2)) {
return error.value;
}
return (cumulative) ? jStat.centralF.cdf(x, d1, d2) : jStat.centralF.pdf(x, d1, d2);
};
exports.F.DIST.RT = function(x, d1, d2) {
if (arguments.length !== 3) {
return error.na;
}
if (x < 0 || d1 < 1 || d2 < 1) {
return error.num;
}
if ((typeof x !== 'number') || (typeof d1 !== 'number') || (typeof d2 !== 'number')) {
return error.value;
}
return 1 - jStat.centralF.cdf(x, d1, d2);
};
exports.F.INV = function(probability, d1, d2) {
probability = utils.parseNumber(probability);
d1 = utils.parseNumber(d1);
d2 = utils.parseNumber(d2);
if (utils.anyIsError(probability, d1, d2)) {
return error.value;
}
if (probability <= 0.0 || probability > 1.0) {
return error.num;
}
return jStat.centralF.inv(probability, d1, d2);
};
exports.F.INV.RT = function(p, d1, d2) {
if (arguments.length !== 3) {
return error.na;
}
if (p < 0 || p > 1 || d1 < 1 || d1 > Math.pow(10, 10) || d2 < 1 || d2 > Math.pow(10, 10)) {
return error.num;
}
if ((typeof p !== 'number') || (typeof d1 !== 'number') || (typeof d2 !== 'number')) {
return error.value;
}
return jStat.centralF.inv(1.0 - p, d1, d2);
};
exports.F.TEST = function(array1, array2) {
if (!array1 || !array2) {
return error.na;
}
if (!(array1 instanceof Array) || !(array2 instanceof Array)) {
return error.na;
}
if (array1.length < 2 || array2.length < 2) {
return error.div0;
}
var sumOfSquares = function(values, x1) {
var sum = 0;
for (var i = 0; i < values.length; i++) {
sum +=Math.pow((values[i] - x1), 2);
}
return sum;
};
var x1 = mathTrig.SUM(array1) / array1.length;
var x2 = mathTrig.SUM(array2) / array2.length;
var sum1 = sumOfSquares(array1, x1) / (array1.length - 1);
var sum2 = sumOfSquares(array2, x2) / (array2.length - 1);
return sum1 / sum2;
};
exports.FISHER = function(x) {
x = utils.parseNumber(x);
if (x instanceof Error) {
return x;
}
return Math.log((1 + x) / (1 - x)) / 2;
};
exports.FISHERINV = function(y) {
y = utils.parseNumber(y);
if (y instanceof Error) {
return y;
}
var e2y = Math.exp(2 * y);
return (e2y - 1) / (e2y + 1);
};
exports.FORECAST = function(x, data_y, data_x) {
x = utils.parseNumber(x);
data_y = utils.parseNumberArray(utils.flatten(data_y));
data_x = utils.parseNumberArray(utils.flatten(data_x));
if (utils.anyIsError(x, data_y, data_x)) {
return error.value;
}
var xmean = jStat.mean(data_x);
var ymean = jStat.mean(data_y);
var n = data_x.length;
var num = 0;
var den = 0;
for (var i = 0; i < n; i++) {
num += (data_x[i] - xmean) * (data_y[i] - ymean);
den += Math.pow(data_x[i] - xmean, 2);
}
var b = num / den;
var a = ymean - b * xmean;
return a + b * x;
};
exports.FREQUENCY = function(data, bins) {
data = utils.parseNumberArray(utils.flatten(data));
bins = utils.parseNumberArray(utils.flatten(bins));
if (utils.anyIsError(data, bins)) {
return error.value;
}
var n = data.length;
var b = bins.length;
var r = [];
for (var i = 0; i <= b; i++) {
r[i] = 0;
for (var j = 0; j < n; j++) {
if (i === 0) {
if (data[j] <= bins[0]) {
r[0] += 1;
}
} else if (i < b) {
if (data[j] > bins[i - 1] && data[j] <= bins[i]) {
r[i] += 1;
}
} else if (i === b) {
if (data[j] > bins[b - 1]) {
r[b] += 1;
}
}
}
}
return r;
};
exports.GAMMA = function(number) {
number = utils.parseNumber(number);
if (number instanceof Error) {
return number;
}
if (number === 0) {
return error.num;
}
if (parseInt(number, 10) === number && number < 0) {
return error.num;
}
return jStat.gammafn(number);
};
exports.GAMMA.DIST = function(value, alpha, beta, cumulative) {
if (arguments.length !== 4) {
return error.na;
}
if (value < 0 || alpha <= 0 || beta <= 0) {
return error.value;
}
if ((typeof value !== 'number') || (typeof alpha !== 'number') || (typeof beta !== 'number')) {
return error.value;
}
return cumulative ? jStat.gamma.cdf(value, alpha, beta, true) : jStat.gamma.pdf(value, alpha, beta, false);
};
exports.GAMMA.INV = function(probability, alpha, beta) {
if (arguments.length !== 3) {
return error.na;
}
if (probability < 0 || probability > 1 || alpha <= 0 || beta <= 0) {
return error.num;
}
if ((typeof probability !== 'number') || (typeof alpha !== 'number') || (typeof beta !== 'number')) {
return error.value;
}
return jStat.gamma.inv(probability, alpha, beta);
};
exports.GAMMALN = function(number) {
number = utils.parseNumber(number);
if (number instanceof Error) {
return number;
}
return jStat.gammaln(number);
};
exports.GAMMALN.PRECISE = function(x) {
if (arguments.length !== 1) {
return error.na;
}
if (x <= 0) {
return error.num;
}
if (typeof x !== 'number') {
return error.value;
}
return jStat.gammaln(x);
};
exports.GAUSS = function(z) {
z = utils.parseNumber(z);
if (z instanceof Error) {
return z;
}
return jStat.normal.cdf(z, 0, 1) - 0.5;
};
exports.GEOMEAN = function() {
var args = utils.parseNumberArray(utils.flatten(arguments));
if (args instanceof Error) {
return args;
}
return jStat.geomean(args);
};
exports.GROWTH = function(known_y, known_x, new_x, use_const) {
// Credits: Ilmari Karonen (http://stackoverflow.com/questions/14161990/how-to-implement-growth-function-in-javascript)
known_y = utils.parseNumberArray(known_y);
if (known_y instanceof Error) {
return known_y;
}
// Default values for optional parameters:
var i;
if (known_x === undefined) {
known_x = [];
for (i = 1; i <= known_y.length; i++) {
known_x.push(i);
}
}
if (new_x === undefined) {
new_x = [];
for (i = 1; i <= known_y.length; i++) {
new_x.push(i);
}
}
known_x = utils.parseNumberArray(known_x);
new_x = utils.parseNumberArray(new_x);
if (utils.anyIsError(known_x, new_x)) {
return error.value;
}
if (use_const === undefined) {
use_const = true;
}
// Calculate sums over the data:
var n = known_y.length;
var avg_x = 0;
var avg_y = 0;
var avg_xy = 0;
var avg_xx = 0;
for (i = 0; i < n; i++) {
var x = known_x[i];
var y = Math.log(known_y[i]);
avg_x += x;
avg_y += y;
avg_xy += x * y;
avg_xx += x * x;
}
avg_x /= n;
avg_y /= n;
avg_xy /= n;
avg_xx /= n;
// Compute linear regression coefficients:
var beta;
var alpha;
if (use_const) {
beta = (avg_xy - avg_x * avg_y) / (avg_xx - avg_x * avg_x);
alpha = avg_y - beta * avg_x;
} else {
beta = avg_xy / avg_xx;
alpha = 0;
}
// Compute and return result array:
var new_y = [];
for (i = 0; i < new_x.length; i++) {
new_y.push(Math.exp(alpha + beta * new_x[i]));
}
return new_y;
};
exports.HARMEAN = function() {
var range = utils.parseNumberArray(utils.flatten(arguments));
if (range instanceof Error) {
return range;
}
var n = range.length;
var den = 0;
for (var i = 0; i < n; i++) {
den += 1 / range[i];
}
return n / den;
};
exports.HYPGEOM = {};
exports.HYPGEOM.DIST = function(x, n, M, N, cumulative) {
x = utils.parseNumber(x);
n = utils.parseNumber(n);
M = utils.parseNumber(M);
N = utils.parseNumber(N);
if (utils.anyIsError(x, n, M, N)) {
return error.value;
}
function pdf(x, n, M, N) {
return mathTrig.COMBIN(M, x) * mathTrig.COMBIN(N - M, n - x) / mathTrig.COMBIN(N, n);
}
function cdf(x, n, M, N) {
var result = 0;
for (var i = 0; i <= x; i++) {
result += pdf(i, n, M, N);
}
return result;
}
return (cumulative) ? cdf(x, n, M, N) : pdf(x, n, M, N);
};
exports.INTERCEPT = function(known_y, known_x) {
known_y = utils.parseNumberArray(known_y);
known_x = utils.parseNumberArray(known_x);
if (utils.anyIsError(known_y, known_x)) {
return error.value;
}
if (known_y.length !== known_x.length) {
return error.na;
}
return exports.FORECAST(0, known_y, known_x);
};
exports.KURT = function() {
var range = utils.parseNumberArray(utils.flatten(arguments));
if (range instanceof Error) {
return range;
}
var mean = jStat.mean(range);
var n = range.length;
var sigma = 0;
for (var i = 0; i < n; i++) {
sigma += Math.pow(range[i] - mean, 4);
}
sigma = sigma / Math.pow(jStat.stdev(range, true), 4);
return ((n * (n + 1)) / ((n - 1) * (n - 2) * (n - 3))) * sigma - 3 * (n - 1) * (n - 1) / ((n - 2) * (n - 3));
};
exports.LARGE = function(range, k) {
range = utils.parseNumberArray(utils.flatten(range));
k = utils.parseNumber(k);
if (utils.anyIsError(range, k)) {
return range;
}
return range.sort(function(a, b) {
return b - a;
})[k - 1];
};
exports.LINEST = function(data_y, data_x) {
data_y = utils.parseNumberArray(utils.flatten(data_y));
data_x = utils.parseNumberArray(utils.flatten(data_x));
if (utils.anyIsError(data_y, data_x)) {
return error.value;
}
var ymean = jStat.mean(data_y);
var xmean = jStat.mean(data_x);
var n = data_x.length;
var num = 0;
var den = 0;
for (var i = 0; i < n; i++) {
num += (data_x[i] - xmean) * (data_y[i] - ymean);
den += Math.pow(data_x[i] - xmean, 2);
}
var m = num / den;
var b = ymean - m * xmean;
return [m, b];
};
// According to Microsoft:
// http://office.microsoft.com/en-us/starter-help/logest-function-HP010342665.aspx
// LOGEST returns are based on the following linear model:
// ln y = x1 ln m1 + ... + xn ln mn + ln b
exports.LOGEST = function(data_y, data_x) {
data_y = utils.parseNumberArray(utils.flatten(data_y));
data_x = utils.parseNumberArray(utils.flatten(data_x));
if (utils.anyIsError(data_y, data_x)) {
return error.value;
}
for (var i = 0; i < data_y.length; i ++) {
data_y[i] = Math.log(data_y[i]);
}
var result = exports.LINEST(data_y, data_x);
result[0] = Math.round(Math.exp(result[0])*1000000)/1000000;
result[1] = Math.round(Math.exp(result[1])*1000000)/1000000;
return result;
};
exports.LOGNORM = {};
exports.LOGNORM.DIST = function(x, mean, sd, cumulative) {
x = utils.parseNumber(x);
mean = utils.parseNumber(mean);
sd = utils.parseNumber(sd);
if (utils.anyIsError(x, mean, sd)) {
return error.value;
}
return (cumulative) ? jStat.lognormal.cdf(x, mean, sd) : jStat.lognormal.pdf(x, mean, sd);
};
exports.LOGNORM.INV = function(probability, mean, sd) {
probability = utils.parseNumber(probability);
mean = utils.parseNumber(mean);
sd = utils.parseNumber(sd);
if (utils.anyIsError(probability, mean, sd)) {
return error.value;
}
return jStat.lognormal.inv(probability, mean, sd);
};
exports.MAX = function() {
var range = utils.numbers(utils.flatten(arguments));
return (range.length === 0) ? 0 : Math.max.apply(Math, range);
};
exports.MAXA = function() {
var range = utils.arrayValuesToNumbers(utils.flatten(arguments));
return (range.length === 0) ? 0 : Math.max.apply(Math, range);
};
exports.MEDIAN = function() {
var range = utils.arrayValuesToNumbers(utils.flatten(arguments));
return jStat.median(range);
};
exports.MIN = function() {
var range = utils.numbers(utils.flatten(arguments));
return (range.length === 0) ? 0 : Math.min.apply(Math, range);
};
exports.MINA = function() {
var range = utils.arrayValuesToNumbers(utils.flatten(arguments));
return (range.length === 0) ? 0 : Math.min.apply(Math, range);
};
exports.MODE = {};
exports.MODE.MULT = function() {
// Credits: Roönaän
var range = utils.parseNumberArray(utils.flatten(arguments));
if (range instanceof Error) {
return range;
}