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lib/PhpSpreadsheet/Calculation/Statistical/Distributions/LogNormal.php

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+<?php
+
+namespace PhpOffice\PhpSpreadsheet\Calculation\Statistical\Distributions;
+
+use PhpOffice\PhpSpreadsheet\Calculation\ArrayEnabled;
+use PhpOffice\PhpSpreadsheet\Calculation\Exception;
+use PhpOffice\PhpSpreadsheet\Calculation\Information\ExcelError;
+
+class LogNormal
+{
+    use ArrayEnabled;
+
+    /**
+     * LOGNORMDIST.
+     *
+     * Returns the cumulative lognormal distribution of x, where ln(x) is normally distributed
+     * with parameters mean and standard_dev.
+     *
+     * @param mixed $value Float value for which we want the probability
+     *                      Or can be an array of values
+     * @param mixed $mean Mean value as a float
+     *                      Or can be an array of values
+     * @param mixed $stdDev Standard Deviation as a float
+     *                      Or can be an array of values
+     *
+     * @return array|float|string The result, or a string containing an error
+     *         If an array of numbers is passed as an argument, then the returned result will also be an array
+     *            with the same dimensions
+     */
+    public static function cumulative(mixed $value, mixed $mean, mixed $stdDev)
+    {
+        if (is_array($value) || is_array($mean) || is_array($stdDev)) {
+            return self::evaluateArrayArguments([self::class, __FUNCTION__], $value, $mean, $stdDev);
+        }
+
+        try {
+            $value = DistributionValidations::validateFloat($value);
+            $mean = DistributionValidations::validateFloat($mean);
+            $stdDev = DistributionValidations::validateFloat($stdDev);
+        } catch (Exception $e) {
+            return $e->getMessage();
+        }
+
+        if (($value <= 0) || ($stdDev <= 0)) {
+            return ExcelError::NAN();
+        }
+
+        return StandardNormal::cumulative((log($value) - $mean) / $stdDev);
+    }
+
+    /**
+     * LOGNORM.DIST.
+     *
+     * Returns the lognormal distribution of x, where ln(x) is normally distributed
+     * with parameters mean and standard_dev.
+     *
+     * @param mixed $value Float value for which we want the probability
+     *                      Or can be an array of values
+     * @param mixed $mean Mean value as a float
+     *                      Or can be an array of values
+     * @param mixed $stdDev Standard Deviation as a float
+     *                      Or can be an array of values
+     * @param mixed $cumulative Boolean value indicating if we want the cdf (true) or the pdf (false)
+     *                      Or can be an array of values
+     *
+     * @return array|float|string The result, or a string containing an error
+     *         If an array of numbers is passed as an argument, then the returned result will also be an array
+     *            with the same dimensions
+     */
+    public static function distribution(mixed $value, mixed $mean, mixed $stdDev, mixed $cumulative = false)
+    {
+        if (is_array($value) || is_array($mean) || is_array($stdDev) || is_array($cumulative)) {
+            return self::evaluateArrayArguments([self::class, __FUNCTION__], $value, $mean, $stdDev, $cumulative);
+        }
+
+        try {
+            $value = DistributionValidations::validateFloat($value);
+            $mean = DistributionValidations::validateFloat($mean);
+            $stdDev = DistributionValidations::validateFloat($stdDev);
+            $cumulative = DistributionValidations::validateBool($cumulative);
+        } catch (Exception $e) {
+            return $e->getMessage();
+        }
+
+        if (($value <= 0) || ($stdDev <= 0)) {
+            return ExcelError::NAN();
+        }
+
+        if ($cumulative === true) {
+            return StandardNormal::distribution((log($value) - $mean) / $stdDev, true);
+        }
+
+        return (1 / (sqrt(2 * M_PI) * $stdDev * $value))
+            * exp(0 - ((log($value) - $mean) ** 2 / (2 * $stdDev ** 2)));
+    }
+
+    /**
+     * LOGINV.
+     *
+     * Returns the inverse of the lognormal cumulative distribution
+     *
+     * @param mixed $probability Float probability for which we want the value
+     *                      Or can be an array of values
+     * @param mixed $mean Mean Value as a float
+     *                      Or can be an array of values
+     * @param mixed $stdDev Standard Deviation as a float
+     *                      Or can be an array of values
+     *
+     * @return array|float|string The result, or a string containing an error
+     *         If an array of numbers is passed as an argument, then the returned result will also be an array
+     *            with the same dimensions
+     *
+     * @TODO    Try implementing P J Acklam's refinement algorithm for greater
+     *            accuracy if I can get my head round the mathematics
+     *            (as described at) http://home.online.no/~pjacklam/notes/invnorm/
+     */
+    public static function inverse(mixed $probability, mixed $mean, mixed $stdDev): array|string|float
+    {
+        if (is_array($probability) || is_array($mean) || is_array($stdDev)) {
+            return self::evaluateArrayArguments([self::class, __FUNCTION__], $probability, $mean, $stdDev);
+        }
+
+        try {
+            $probability = DistributionValidations::validateProbability($probability);
+            $mean = DistributionValidations::validateFloat($mean);
+            $stdDev = DistributionValidations::validateFloat($stdDev);
+        } catch (Exception $e) {
+            return $e->getMessage();
+        }
+
+        if ($stdDev <= 0) {
+            return ExcelError::NAN();
+        }
+        /** @var float $inverse */
+        $inverse = StandardNormal::inverse($probability);
+
+        return exp($mean + $stdDev * $inverse);
+    }
+}