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jstat - npm Package Compare versions

Comparing version 1.7.2 to 1.8.0

66

doc/md/core.md

@@ -12,3 +12,3 @@ ## Core Functionality

Create a new jStat object from either an existing array or jStat object.
Creates a new jStat object from either an existing array or jStat object.
For example, create a new jStat matrix by doing the following:

@@ -85,3 +85,3 @@

Return a array from matrix row
Returns a array from matrix row.

@@ -113,3 +113,3 @@ rowa([[1,2],[3,4]]) === [1,2];

Return a array from matrix column (`col()` will return a matrix form instead of array form)
Returns an array from matrix column (`col()` will return a matrix form instead of an array form).

@@ -120,3 +120,3 @@ cola([[1,2],[3,4]]) === [1,3];

Slice matrix as numpy style
Slices matrix as numpy style.

@@ -129,3 +129,3 @@ A=[[1,2,3],[4,5,6],[7,8,9]];

Do slice assign as numpy style
Do slice assign as numpy style.

@@ -139,3 +139,3 @@ A = [[1,2,3],[4,5,6],[7,8,9]];

Returns and object with the dimensions of a matrix.
Returns an object with the dimensions of a matrix.

@@ -247,3 +247,3 @@ **dimensions( array )**

Create a new diagonal matrix by given 1d diag array
Creates a new diagonal matrix by given 1d diag array.

@@ -254,3 +254,3 @@ jStat.diagonal([1,2,3]) === [[1,0,0],[0,2,0],[0,0,3]];

Transpose a matrix.
Transposes a matrix.

@@ -276,3 +276,3 @@ **transpose( array )**

Map a function to all values and return a new object.
Maps a function to all values and return a new object.

@@ -295,3 +295,3 @@ **map( array, fn )**

Cumulatively reduce values using a function and return a new object.
Cumulatively reduces values using a function and return a new object.

@@ -314,3 +314,3 @@ **cumreduce( array, fn )**

Destructively map to an array.
Destructively maps to an array.

@@ -334,3 +334,3 @@ **alter( array, fn )**

Create a row by col matrix using the supplied function
Creates a row by col matrix using the supplied function.
If `col` is omitted then it will default to value `row`.

@@ -347,3 +347,3 @@

Use this technique for creating matrices in jStat instances.
Use this technique to create matrices in jStat instances.

@@ -357,3 +357,3 @@ jStat().create( 2, function( row, col ) {

Create a row by col matrix of all zeros.
Creates a row by col matrix of all zeros.
If `col` is omitted then it will default to value `row`.

@@ -368,3 +368,3 @@

Use this technique for creating matrices in jStat instances.
Use this technique to create matrices in jStat instances.

@@ -376,3 +376,3 @@ jStat().zeros( 2 );

Create a row by col matrix of all ones.
Creates a row by col matrix of all ones.
If `col` is omitted then it will default to value `row`.

@@ -387,3 +387,3 @@

Use this technique for creating matrices in jStat instances.
Use this technique to create matrices in jStat instances.

@@ -395,3 +395,3 @@ jStat().ones( 2 );

Create a matrix of normally distributed random numbers.
Creates a matrix of normally distributed random numbers.
If `col` is omitted then it will default to value `row`.

@@ -405,3 +405,3 @@

Use this technique for creating matrices in jStat instances.
Use this technique to create matrices in jStat instances.

@@ -412,7 +412,7 @@ jStat().rand( 3 );

Return a copy from given matrix
Returns a copy of given matrix.
### identity()
Create an identity matrix of row by col.
Creates an identity matrix of row by col.
If `col` is omitted then it will default to value `row`.

@@ -427,3 +427,3 @@

Use this technique for creating matrices in jStat instances.
Use this technique to create matrices in jStat instances.

@@ -434,3 +434,3 @@ jStat().identity( 2 );

Create an arithmetic sequence by given length
Creates an arithmetic sequence by given length.

@@ -441,3 +441,3 @@ jStat.seq(1,5,9) === [1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5];

Create an arithmetic sequence by given step
Creates an arithmetic sequence by given step.

@@ -452,3 +452,3 @@ arange(5) === [0,1,2,3,4]

Set all values in the vector or matrix to zero.
Sets all values in the vector or matrix to zero.

@@ -466,3 +466,3 @@ **clear( array )**

If a callback is passed then the original object is not altered
If a callback is passed then the original object is not altered.

@@ -483,7 +483,7 @@ var obj = jStat( 0, 1, 3 );

**fn.symmetric( callback )**
**fn.symmetric( [callback] )**
jStat([[1,2],[2,1]]).symmetric() === true
Can pass a callback to maintain chainability
Can pass a callback to maintain chainability.

@@ -496,18 +496,18 @@ jStat([[1,2],[2,1]]).symmetric(function( result ) {

Utilities that are used throughout the jStat library
Utilities that are used throughout the jStat library.
### utils.calcRdx( num0, num1 )
Calculate the decimal shift for the IEEE 754 floating point calculation correction.
Calculates the decimal shift for the IEEE 754 floating point calculation correction.
### utils.isArray( arg )
Test if `arg` is an array.
Tests if `arg` is an array.
### utils.isFunction( arg )
Test if `arg` is a function.
Tests if `arg` is a function.
### utils.isNumber( arg )
Test if `arg` is a number and not `NaN`.
Tests if `arg` is a number and not `NaN`.

274

doc/md/distributions.md

@@ -8,53 +8,53 @@ ## Distributions

Returns the value of x in the Beta distribution with parameters alpha and beta.
Returns the value of `x` in the Beta distribution with parameters `alpha` and `beta`.
#### jStat.beta.cdf( x, alpha, beta )
Returns the value of x in the cdf for the Beta distribution with parameters alpha and beta.
Returns the value of `x` in the cdf for the Beta distribution with parameters `alpha` and `beta`.
#### jStat.beta.inv( p, alpha, beta )
Returns the value of p in the inverse of the cdf for the Beta distribution with parameters alpha and beta.
Returns the value of `p` in the inverse of the cdf for the Beta distribution with parameters `alpha` and `beta`.
#### jStat.beta.mean( alpha, beta )
Returns the mean of the Beta distribution with parameters alpha and beta.
Returns the mean of the Beta distribution with parameters `alpha` and `beta`.
#### jStat.beta.median( alpha, beta )
Returns the median of the Beta distribution with parameters alpha and beta.
Returns the median of the Beta distribution with parameters `alpha` and `beta`.
#### jStat.beta.mode( alpha, beta )
Returns the mode of the Beta distribution with parameters alpha and beta.
Returns the mode of the Beta distribution with parameters `alpha` and `beta`.
#### jStat.beta.sample( alpha, beta )
Returns a random number whose distribution is the Beta distribution with parameters alpha and beta.
Returns a random number whose distribution is the Beta distribution with parameters `alpha` and `beta`.
#### jStat.beta.variance( alpha, beta )
Returns the variance of the Beta distribution with parameters alpha and beta.
Returns the variance of the Beta distribution with parameters `alpha` and `beta`.
### jStat.centralF( df1, df2 )
The F Distrbution is used frequently in in analyses of variance. The parameterized by two degrees of freedom. It is defined continuously on x in [0, infinity).
The F Distrbution is used frequently in analyses of variance. The distribution is parameterized by two degrees of freedom (`df1` and `df2`). It is defined continuously on x in [0, infinity).
In all cases, df1 is the "numerator degrees of freedom" and df2 is the "denominator degrees of freedom", which parameterize the distribtuion.
In all cases, `df1` is the "numerator degrees of freedom" and `df2` is the "denominator degrees of freedom", which parameterize the distribtuion.
#### jStat.centralF.pdf( x, df1, df2 )
Given x in the range [0, infinity), returns the probability density of the (central) f distribution at x.
Given `x` in the range [0, infinity), returns the probability density of the (central) F distribution at `x`.
This function corresponds to the df(x, df1, df2) function in R.
This function corresponds to the `df(x, df1, df2)` function in R.
#### jStat.centralF.cdf( x, df1, df2 )
Given x in the range [0, infinity), returns the cumulative probability density of the central F distribution. That is, `jStat.centralF.cdf(2.5, 10, 20)` will return the probability that a number randomly selected from the central F distribution with df1 = 10 and df2 = 20 will be less than 5.5.
Given x in the range [0, infinity), returns the cumulative probability density of the central F distribution. That is, `jStat.centralF.cdf(2.5, 10, 20)` will return the probability that a number randomly selected from the central F distribution with `df1 = 10` and `df2 = 20` will be less than 2.5.
This function corresponds to the pf(q, df1, df2) function in R.
This function corresponds to the `pf(q, df1, df2)` function in R.
#### jStat.centralF.inv(p, df1, df2 )
#### jStat.centralF.inv( p, df1, df2 )
Given p in [0, 1), returns the value of x for which the cumulative probability density of the central F distribution is p. That is, `jStat.centralF.inv(p, df1, df2) = x` if and only if `jStat.centralF.inv(x, df1, df2) = p`.
Given `p` in [0, 1), returns the value of x for which the cumulative probability density of the central F distribution is p. That is, `jStat.centralF.inv(p, df1, df2) = x` if and only if `jStat.centralF.inv(x, df1, df2) = p`.

@@ -85,27 +85,27 @@ This function corresponds to the `qf(p, df1, df2)` function in R.

Returns the value of x in the pdf of the Cauchy distribution with a location (median) of local and scale factor of scale.
Returns the value of `x` in the pdf of the Cauchy distribution with a location (median) of `local` and scale factor of `scale`.
#### jStat.cauchy.cdf( x, local, scale )
Returns the value of x in the cdf of the Cauchy distribution with a location (median) of local and scale factor of scale.
Returns the value of `x` in the cdf of the Cauchy distribution with a location (median) of `local` and scale factor of `scale`.
#### jStat.cauchy.inv( p, local, scale )
Returns the value of p in the inverse of the cdf for the Cauchy distribution with a location (median) of local and scale factor of scale.
Returns the value of `p` in the inverse of the cdf for the Cauchy distribution with a location (median) of `local` and scale factor of `scale`.
#### jStat.cauchy.median( local, scale )
Returns the value of the median for the Cauchy distribution with a location (median) of local and scale factor of scale.
Returns the value of the median for the Cauchy distribution with a location (median) of `local` and scale factor of `scale`.
#### jStat.cauchy.mode( local, scale )
Returns the value of the mode for the Cauchy distribution with a location (median) of local and scale factor of scale.
Returns the value of the mode for the Cauchy distribution with a location (median) of `local` and scale factor of `scale`.
#### jStat.cauchy.sample( local, scale )
Returns a random number whose distribution is the Cauchy distribution with a location (median) of local and scale factor of scale.
Returns a random number whose distribution is the Cauchy distribution with a location (median) of `local` and scale factor of `scale`.
#### jStat.cauchy.variance( local, scale )
Returns the value of the variance for the Cauchy distribution with a location (median) of local and scale factor of scale.
Returns the value of the variance for the Cauchy distribution with a location (median) of `local` and scale factor of `scale`.

@@ -116,31 +116,31 @@ ### jStat.chisquare( dof )

Returns the value of x in the pdf of the Chi Square distribution with degrees of freedom dof.
Returns the value of `x` in the pdf of the Chi Square distribution with `dof` degrees of freedom.
#### jStat.chisquare.cdf( x, dof )
Returns the value of x in the cdf of the Chi Square distribution with degrees of freedom dof.
Returns the value of `x` in the cdf of the Chi Square distribution with `dof` degrees of freedom.
#### jStat.chisquare.inv( p, dof )
Returns the value of x in the inverse of the cdf for the Chi Square distribution with degrees of freedom dof.
Returns the value of `x` in the inverse of the cdf for the Chi Square distribution with `dof` degrees of freedom.
#### jStat.chisquare.mean( dof )
Returns the value of the mean for the Chi Square distribution with degrees of freedom dof.
Returns the value of the mean for the Chi Square distribution with `dof` degrees of freedom.
#### jStat.chisquare.median( dof )
Returns the value of the median for the Chi Square distribution with degrees of freedom dof.
Returns the value of the median for the Chi Square distribution with `dof` degrees of freedom.
#### jStat.chisquare.mode( dof )
Returns the value of the mode for the Chi Square distribution with degrees of freedom dof.
Returns the value of the mode for the Chi Square distribution with `dof` degrees of freedom.
#### jStat.chisquare.sample( dof )
Returns a random number whose distribution is the Chi Square distribution with degrees of freedom dof.
Returns a random number whose distribution is the Chi Square distribution with `dof` degrees of freedom.
#### jStat.chisquare.variance( dof )
Returns the value of the variance for the Chi Square distribution with degrees of freedom dof.
Returns the value of the variance for the Chi Square distribution with `dof` degrees of freedom.

@@ -152,31 +152,31 @@

Returns the value of x in the pdf of the Exponential distribution with the parameter rate (lambda).
Returns the value of `x` in the pdf of the Exponential distribution with the parameter `rate` (lambda).
#### jStat.exponential.cdf( x, rate )
Returns the value of x in the cdf of the Exponential distribution with the parameter rate (lambda).
Returns the value of `x` in the cdf of the Exponential distribution with the parameter `rate` (lambda).
#### jStat.exponential.inv( p, rate )
Returns the value of x in the inverse of the cdf for the Exponential distribution with the parameter rate (lambda).
Returns the value of `p` in the inverse of the cdf for the Exponential distribution with the parameter `rate` (lambda).
#### jStat.exponential.mean( rate )
Returns the value of the mean for the Exponential distribution with the parameter rate (lambda).
Returns the value of the mean for the Exponential distribution with the parameter `rate` (lambda).
#### jStat.exponential.median( rate )
Returns the value of the median for the Exponential distribution with the parameter rate (lambda)
Returns the value of the median for the Exponential distribution with the parameter `rate` (lambda)
#### jStat.exponential.mode( rate )
Returns the value of the mode for the Exponential distribution with the parameter rate (lambda).
Returns the value of the mode for the Exponential distribution with the parameter `rate` (lambda).
#### jStat.exponential.sample( rate )
Returns a random number whose distribution is the Exponential distribution with the parameter rate (lambda).
Returns a random number whose distribution is the Exponential distribution with the parameter `rate` (lambda).
#### jStat.exponential.variance( rate )
Returns the value of the variance for the Exponential distribution with the parameter rate (lambda).
Returns the value of the variance for the Exponential distribution with the parameter `rate` (lambda).

@@ -187,31 +187,31 @@ ### jStat.gamma( shape, scale )

Returns the value of x in the pdf of the Gamma distribution with the parameters shape (k) and scale (theta). Notice that if using the alpha beta convention, scale = 1/beta.
Returns the value of `x` in the pdf of the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`.
#### jStat.gamma.cdf( x, shape, scale )
Returns the value of x in the cdf of the Gamma distribution with the parameters shape (k) and scale (theta). Notice that if using the alpha beta convention, scale = 1/beta.
Returns the value of `x` in the cdf of the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`.
This function is checked against R's pgamma function.
This function is checked against R's `pgamma` function.
#### jStat.gamma.inv( p, shape, scale )
Returns the value of p in the inverse of the cdf for the Gamma distribution with the parameters shape (k) and scale (theta). Notice that if using the alpha beta convention, scale = 1/beta.
Returns the value of `p` in the inverse of the cdf for the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`.
This function is checked against R's qgamma function.
This function is checked against R's `qgamma` function.
#### jStat.gamma.mean( shape, scale )
Returns the value of the mean for the Gamma distribution with the parameters shape (k) and scale (theta). Notice that if using the alpha beta convention, scale = 1/beta.
Returns the value of the mean for the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`.
#### jStat.gamma.mode( shape, scale )
Returns the value of the mode for the Gamma distribution with the parameters shape (k) and scale (theta). Notice that if using the alpha beta convention, scale = 1/beta.
Returns the value of the mode for the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`.
#### jStat.gamma.sample( shape, scale )
Returns a random number whose distribution is the Gamma distribution with the parameters shape (k) and scale (theta). Notice that if using the alpha beta convention, scale = 1/beta.
Returns a random number whose distribution is the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`.
#### jStat.gamma.variance( shape, scale )
Returns the value of the variance for the Gamma distribution with the parameters shape (k) and scale (theta). Notice that if using the alpha beta convention, scale = 1/beta.
Returns the value of the variance for the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`.

@@ -222,27 +222,27 @@ ### jStat.invgamma( shape, scale )

Returns the value of x in the pdf of the Inverse-Gamma distribution with parametres shape (alpha) and scale (beta).
Returns the value of `x` in the pdf of the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta).
#### jStat.invgamma.cdf( x, shape, scale )
Returns the value of x in the cdf of the Inverse-Gamma distribution with parametres shape (alpha) and scale (beta).
Returns the value of `x` in the cdf of the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta).
#### jStat.invgamma.inv( p, shape, scale )
Returns the value of p in the inverse of the cdf for the Inverse-Gamma distribution with parametres shape (alpha) and scale (beta).
Returns the value of `p` in the inverse of the cdf for the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta).
#### jStat.invgamma.mean( shape, scale )
Returns the value of the mean for the Inverse-Gamma distribution with parametres shape (alpha) and scale (beta).
Returns the value of the mean for the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta).
#### jStat.invgamma.mode( shape, scale )
Returns the value of the mode for the Inverse-Gamma distribution with parametres shape (alpha) and scale (beta).
Returns the value of the mode for the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta).
#### jStat.invgamma.sample( shape, scale )
Returns a random number whose distribution is the Inverse-Gamma distribution with parametres shape (alpha) and scale (beta).
Returns a random number whose distribution is the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta).
#### jStat.invgamma.variance( shape, scale )
Returns the value of the variance for the Inverse-Gamma distribution with parametres shape (alpha) and scale (beta).
Returns the value of the variance for the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta).

@@ -253,27 +253,29 @@ ### jStat.kumaraswamy( alpha, beta )

Returns the value of x in the pdf of the Kumaraswamy distribution with parameters a and b.
Returns the value of `x` in the pdf of the Kumaraswamy distribution with parameters `a` and `b`.
#### jStat.kumaraswamy.cdf( x, alpha, beta )
Returns the value of x in the cdf of the Kumaraswamy distribution with parameters a and b.
Returns the value of `x` in the cdf of the Kumaraswamy distribution with parameters `alpha` and `beta`.
#### jStat.kumaraswamy.inv( p, alpha, beta )
Returns the inverse of the Kumaraswamy PDF. This function corresponds to qkumar(p, alpha, beta) in R's VGAM package.
Returns the value of `p` in the inverse of the pdf for the Kumaraswamy distribution with parametres `alpha` and `beta`.
This function corresponds to `qkumar(p, alpha, beta)` in R's VGAM package.
#### jStat.kumaraswamy.mean( alpha, beta )
Returns the value of the mean of the Kumaraswamy distribution with parameters a and b.
Returns the value of the mean of the Kumaraswamy distribution with parameters `alpha` and `beta`.
#### jStat.kumaraswamy.median( alpha, beta )
Returns the value of the median of the Kumaraswamy distribution with parameters a and b.
Returns the value of the median of the Kumaraswamy distribution with parameters `alpha` and `beta`.
#### jStat.kumaraswamy.mode( alpha, beta )
Returns the value of the mode of the Kumaraswamy distribution with parameters a and b.
Returns the value of the mode of the Kumaraswamy distribution with parameters `alpha` and `beta`.
#### jStat.kumaraswamy.variance( alpha, beta )
Returns the value of the variance of the Kumaraswamy distribution with parameters a and b.
Returns the value of the variance of the Kumaraswamy distribution with parameters `alpha` and `beta`.

@@ -284,31 +286,31 @@ ### jStat.lognormal( mu, sigma )

Returns the value of x in the pdf of the Log-normal distribution with paramters mu (mean) and sigma (standard deviation).
Returns the value of `x` in the pdf of the Log-normal distribution with paramters `mu` (mean) and `sigma` (standard deviation).
#### jStat.lognormal.cdf( x, mu, sigma )
Returns the value of x in the cdf of the Log-normal distribution with paramters mu (mean) and sigma (standard deviation).
Returns the value of `x` in the cdf of the Log-normal distribution with paramters `mu` (mean) and `sigma` (standard deviation).
#### jStat.lognormal.inv( p, mu, sigma )
Returns the value of x in the inverse of the cdf for the Log-normal distribution with paramters mu (mean of the Normal distribution) and sigma (standard deviation of the Normal distribution).
Returns the value of `x` in the inverse of the cdf for the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution).
#### jStat.lognormal.mean( mu, sigma )
Returns the value of the mean for the Log-normal distribution with paramters mu (mean of the Normal distribution) and sigma (standard deviation of the Normal distribution).
Returns the value of the mean for the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution).
#### jStat.lognormal.median( mu, sigma )
Returns the value of the median for the Log-normal distribution with paramters mu (mean of the Normal distribution) and sigma (standard deviation of the Normal distribution).
Returns the value of the median for the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution).
#### jStat.lognormal.mode( mu, sigma )
Returns the value of the mode for the Log-normal distribution with paramters mu (mean of the Normal distribution) and sigma (standard deviation of the Normal distribution).
Returns the value of the mode for the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution).
#### jStat.lognormal.sample( mu, sigma )
Returns a random number whose distribution is the Log-normal distribution with paramters mu (mean of the Normal distribution) and sigma (standard deviation of the Normal distribution).
Returns a random number whose distribution is the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution).
#### jStat.lognormal.variance( mu, sigma )
Returns the value of the variance for the Log-normal distribution with paramters mu (mean of the Normal distribution) and sigma (standard deviation of the Normal distribution).
Returns the value of the variance for the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution).

@@ -319,31 +321,31 @@ ### jStat.normal( mean, std )

Returns the value of x in the pdf of the Normal distribution with parameters mean and std (standard deviation).
Returns the value of `x` in the pdf of the Normal distribution with parameters `mean` and `std` (standard deviation).
#### jStat.normal.cdf( x, mean, std )
Returns the value of x in the cdf of the Normal distribution with parameters mean and std (standard deviation).
Returns the value of `x` in the cdf of the Normal distribution with parameters `mean` and `std` (standard deviation).
#### jStat.normal.inv( p, mean, std )
Returns the value of p in the inverse cdf for the Normal distribution with parameters mean and std (standard deviation).
Returns the value of `p` in the inverse cdf for the Normal distribution with parameters `mean` and `std` (standard deviation).
#### jStat.normal.mean( mean, std )
Returns the value of the mean for the Normal distribution with parameters mean and std (standard deviation).
Returns the value of the mean for the Normal distribution with parameters `mean` and `std` (standard deviation).
#### jStat.normal.median( mean, std )
Returns the value of the median for the Normal distribution with parameters mean and std (standard deviation).
Returns the value of the median for the Normal distribution with parameters `mean` and `std` (standard deviation).
#### jStat.normal.mode( mean, std )
Returns the value of the mode for the Normal distribution with parameters mean and std (standard deviation).
Returns the value of the mode for the Normal distribution with parameters `mean` and `std` (standard deviation).
#### jStat.normal.sample( mean, std )
Returns a random number whose distribution is the Normal distribution with parameters mean and std (standard deviation).
Returns a random number whose distribution is the Normal distribution with parameters `mean` and `std` (standard deviation).
#### jStat.normal.variance( mean, std )
Returns the value of the variance for the Normal distribution with parameters mean and std (standard deviation).
Returns the value of the variance for the Normal distribution with parameters `mean` and `std` (standard deviation).

@@ -354,29 +356,29 @@ ### jStat.pareto( scale, shape )

Returns the value of x in the pdf of the Pareto distribution with parameters scale (x_m) and shape (alpha).
Returns the value of `x` in the pdf of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha).
#### jStat.pareto.inv( p, scale, shape )
Returns the inverse of the Pareto distribution with probability p, scale, shape.
Returns the inverse of the Pareto distribution with probability `p`, `scale`, `shape`.
This coresponds to `qpareto(p, scale, shape)` in R's VGAM package, and generally corresponds to the q<dist> function pattern in R.
This coresponds to `qpareto(p, scale, shape)` in R's VGAM package, and generally corresponds to the `q`<dist> function pattern in R.
#### jStat.pareto.cdf( x, scale, shape )
Returns the value of x in the cdf of the Pareto distribution with parameters scale (x_m) and shape (alpha).
Returns the value of `x` in the cdf of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha).
#### jStat.pareto.mean( scale, shape )
Returns the value of the mean of the Pareto distribution with parameters scale (x_m) and shape (alpha).
Returns the value of the mean of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha).
#### jStat.pareto.median( scale, shape )
Returns the value of the median of the Pareto distribution with parameters scale (x_m) and shape (alpha).
Returns the value of the median of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha).
#### jStat.pareto.mode( scale, shape )
Returns the value of the mode of the Pareto distribution with parameters scale (x_m) and shape (alpha).
Returns the value of the mode of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha).
#### jStat.pareto.variance( scale, shape )
Returns the value of the variance of the Pareto distribution with parameters scale (x_m) and shape (alpha).
Returns the value of the variance of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha).

@@ -387,31 +389,31 @@ ### jStat.studentt( dof )

Returns the value of x in the pdf of the Student's T distribution with degrees of freedom dof.
Returns the value of `x` in the pdf of the Student's T distribution with `dof` degrees of freedom.
#### jStat.studentt.cdf( x, dof )
Returns the value of x in the cdf of the Student's T distribution with degrees of freedom dof.
Returns the value of `x` in the cdf of the Student's T distribution with `dof` degrees of freedom.
#### jStat.studentt.inv( p, dof )
Returns the value of p in the inverse of the cdf for the Student's T distribution with degrees of freedom dof.
Returns the value of `p` in the inverse of the cdf for the Student's T distribution with `dof` degrees of freedom.
#### jStat.studentt.mean( dof )
Returns the value of the mean of the Student's T distribution with degrees of freedom dof.
Returns the value of the mean of the Student's T distribution with `dof` degrees of freedom.
#### jStat.studentt.median( dof )
Returns the value of the median of the Student's T distribution with degrees of freedom dof.
Returns the value of the median of the Student's T distribution with `dof` degrees of freedom.
#### jStat.studentt.mode( dof )
Returns the value of the mode of the Student's T distribution with degrees of freedom dof.
Returns the value of the mode of the Student's T distribution with `dof` degrees of freedom.
#### jStat.studentt.sample( dof )
Returns a random number whose distribution is the Student's T distribution with degrees of freedom dof.
Returns a random number whose distribution is the Student's T distribution with `dof` degrees of freedom.
#### jStat.studentt.variance( dof )
Returns the value of the variance for the Student's T distribution with degrees of freedom dof.
Returns the value of the variance for the Student's T distribution with `dof` degrees of freedom.

@@ -422,7 +424,7 @@ ### jStat.tukey( nmeans, dof )

Returns the value of q in the cdf of the Studentized range distribution with number of groups nmeans and degrees of freedom dof.
Returns the value of q in the cdf of the Studentized range distribution with `nmeans` number of groups nmeans and `dof` degrees of freedom.
#### jStat.tukey.inv( p, nmeans, dof )
Returns the value of p in the inverse of the cdf for the Studentized range distribution with number of groups nmeans and degrees of freedom dof.
Returns the value of `p` in the inverse of the cdf for the Studentized range distribution with `nmeans` number of groups and `dof` degrees of freedom.
Only accurate to 4 decimal places.

@@ -434,31 +436,31 @@

Returns the value x in the pdf for the Weibull distribution with parameters scale (lambda) and shape (k).
Returns the value `x` in the pdf for the Weibull distribution with parameters `scale` (lambda) and `shape` (k).
#### jStat.weibull.cdf( x, scale, shape )
Returns the value x in the cdf for the Weibull distribution with parameters scale (lambda) and shape (k).
Returns the value `x` in the cdf for the Weibull distribution with parameters `scale` (lambda) and `shape` (k).
#### jStat.weibull.inv( p, scale, shape )
Returns the value of x in the inverse of the cdf for the Weibull distribution with parameters scale (lambda) and shape (k).
Returns the value of `x` in the inverse of the cdf for the Weibull distribution with parameters `scale` (lambda) and `shape` (k).
#### jStat.weibull.mean( scale, shape )
Returns the value of the mean of the Weibull distribution with parameters scale (lambda) and shape (k).
Returns the value of the mean of the Weibull distribution with parameters `scale` (lambda) and `shape` (k).
#### jStat.weibull.median( scale, shape )
Returns the value of the median of the Weibull distribution with parameters scale (lambda) and shape (k).
Returns the value of the median of the Weibull distribution with parameters `scale` (lambda) and `shape` (k).
#### jStat.weibull.mode( scale, shape )
Returns the mode of the Weibull distribution with parameters scale (lambda) and shape (k).
Returns the mode of the Weibull distribution with parameters `scale` (lambda) and `shape` (k).
#### jStat.weibull.sample( scale, shape )
Returns a random number whose distribution is the Weibull distribution with parameters scale (lambda) and shape (k).
Returns a random number whose distribution is the Weibull distribution with parameters `scale` (lambda) and `shape` (k).
#### jStat.weibull.variance( scale, shape )
Returns the variance of the Weibull distribution with parameters scale (lambda) and shape (k).
Returns the variance of the Weibull distribution with parameters `scale` (lambda) and `shape` (k).

@@ -469,31 +471,31 @@ ### jStat.uniform( a, b )

Returns the value of x in the pdf of the Uniform distribution from a to b.
Returns the value of `x` in the pdf of the Uniform distribution from `a` to `b`.
#### jStat.uniform.cdf( x, a, b )
Returns the value of x in the cdf of the Uniform distribution from a to b.
Returns the value of `x` in the cdf of the Uniform distribution from `a` to `b`.
#### jStat.uniform.inv(p, a, b)
#### jStat.uniform.inv( p, a, b)
Returns the inverse of the uniform.cdf function; i.e. the value of x for which uniform.cdf(x, a, b) == p.
Returns the inverse of the `uniform.cdf` function; i.e. the value of `x` for which `uniform.cdf(x, a, b) == p`.
#### jStat.uniform.mean( a, b )
Returns the value of the mean of the Uniform distribution from a to b.
Returns the value of the mean of the Uniform distribution from `a` to `b`.
#### jStat.uniform.median( a, b )
Returns the value of the median of the Uniform distribution from a to b.
Returns the value of the median of the Uniform distribution from `a` to `b`.
#### jStat.uniform.mode( a, b )
Returns the value of the mode of the Uniform distribution from a to b.
Returns the value of the mode of the Uniform distribution from `a` to `b`.
#### jStat.uniform.sample( a, b )
Returns a random number whose distribution is the Uniform distribution from a to b.
Returns a random number whose distribution is the Uniform distribution from `a` to `b`.
#### jStat.uniform.variance( a, b )
Returns the variance of the Uniform distribution from a to b.
Returns the variance of the Uniform distribution from `a` to `b`.

@@ -504,7 +506,7 @@ ### jStat.binomial

Returns the value of k in the pdf of the Binomial distribution with parameters n and p.
Returns the value of `k` in the pdf of the Binomial distribution with parameters `n` and `p`.
#### jStat.binomial.cdf( k, n, p )
Returns the value of k in the cdf of the Binomial distribution with parameters n and p.
Returns the value of `k` in the cdf of the Binomial distribution with parameters `n` and `p`.

@@ -515,7 +517,7 @@ ### jStat.negbin

Returns the value of k in the pdf of the Negative Binomial distribution with parameters n and p.
Returns the value of `k` in the pdf of the Negative Binomial distribution with parameters `n` and `p`.
#### jStat.negbin.cdf( x, r, p )
Returns the value of x in the cdf of the Negative Binomial distribution with parameters n and p.
Returns the value of `x` in the cdf of the Negative Binomial distribution with parameters `n` and `p`.

@@ -526,7 +528,7 @@ ### jStat.hypgeom

Returns the value of k in the pdf of the Hypergeometric distribution with parameters N (the population size), m (the success rate), and n (the number of draws).
Returns the value of `k` in the pdf of the Hypergeometric distribution with parameters `N` (the population size), `m` (the success rate), and `n` (the number of draws).
#### jStat.hypgeom.cdf( x, N, m, n )
Returns the value of k in the pdf of the Hypergeometric distribution with parameters N (the population size), m (the success rate), and n (the number of draws).
Returns the value of `x` in the cdf of the Hypergeometric distribution with parameters `N` (the population size), `m` (the success rate), and `n` (the number of draws).

@@ -537,7 +539,7 @@ ### jStat.poisson

Returns the value of k in the pdf of the Poisson distribution with parameter l (lambda).
Returns the value of `k` in the pdf of the Poisson distribution with parameter `l` (lambda).
#### jStat.poisson.cdf( x, l )
Returns the value of x in the cdf of the Poisson distribution with parameter l (lambda).
Returns the value of `x` in the cdf of the Poisson distribution with parameter `l` (lambda).

@@ -552,27 +554,27 @@ #### jStat.poisson.sample( l )

Returns the value of x in the pdf of the Triangular distribution with the parameters a, b, and c.
Returns the value of `x` in the pdf of the Triangular distribution with the parameters `a`, `b`, and `c`.
#### jStat.triangular.cdf( x, a, b, c )
Returns the value of x in the cdf of the Triangular distribution with the parameters a, b, and c.
Returns the value of `x` in the cdf of the Triangular distribution with the parameters `a`, `b`, and `c`.
#### jStat.triangular.mean( a, b, c )
Returns the value of the mean of the Triangular distribution with the parameters a, b, and c.
Returns the value of the mean of the Triangular distribution with the parameters `a`, `b`, and `c`.
#### jStat.triangular.median( a, b, c )
Returns the value of the median of the Triangular distribution with the parameters a, b, and c.
Returns the value of the median of the Triangular distribution with the parameters `a`, `b`, and `c`.
#### jStat.triangular.mode( a, b, c )
Returns the value of the mode of the Triangular distribution with the parameters a, b, and c.
Returns the value of the mode of the Triangular distribution with the parameters `a`, `b`, and `c`.
#### jStat.triangular.sample( a, b, c )
Returns a random number whose distribution is the Triangular distribution with the parameters a, b, and c.
Returns a random number whose distribution is the Triangular distribution with the parameters `a`, `b`, and `c`.
#### jStat.triangular.variance( a, b, c )
Returns the value of the variance of the Triangular distribution with the parameters a, b, and c.
Returns the value of the variance of the Triangular distribution with the parameters `a`, `b`, and `c`.

@@ -583,30 +585,30 @@ ### jStat.arcsine( a, b )

Returns the value of x in the pdf of the arcsine distribution from a to b.
Returns the value of `x` in the pdf of the arcsine distribution from `a` to `b`.
#### jStat.arcsine.cdf( x, a, b )
Returns the value of x in the cdf of the arcsine distribution from a to b.
Returns the value of `x` in the cdf of the arcsine distribution from `a` to `b`.
#### jStat.arcsine.inv(p, a, b)
Returns the inverse of the arcsine.cdf function; i.e. the value of x for which arcsine.cdf(x, a, b) == p.
Returns the inverse of the `arcsine.cdf` function; i.e. the value of `x` for which `arcsine.cdf(x, a, b) == p`.
#### jStat.arcsine.mean( a, b )
Returns the value of the mean of the arcsine distribution from a to b.
Returns the value of the mean of the arcsine distribution from `a` to `b`.
#### jStat.arcsine.median( a, b )
Returns the value of the median of the arcsine distribution from a to b.
Returns the value of the median of the arcsine distribution from `a` to `b`.
#### jStat.arcsine.mode( a, b )
Returns the value of the mode of the arcsine distribution from a to b.
Returns the value of the mode of the arcsine distribution from `a` to `b`.
#### jStat.arcsine.sample( a, b )
Returns a random number whose distribution is the arcsine distribution from a to b.
Returns a random number whose distribution is the arcsine distribution from `a` to `b`.
#### jStat.arcsine.variance( a, b )
Returns the variance of the Uniform distribution from a to b.
Returns the variance of the Uniform distribution from `a` to `b`.

134

doc/md/linear-algebra.md

@@ -7,3 +7,3 @@ ## Linear Algebra

Add value to all entries.
Adds value to all entries.

@@ -14,3 +14,3 @@ jStat([[1,2,3]]).add( 2 ) === [[3,4,5]];

Subtract all entries by value.
Subtracts all entries by value.

@@ -21,3 +21,3 @@ jStat([[4,5,6]]).subtract( 2 ) === [[2,3,4]];

Divide all entries by value.
Divides all entries by value.

@@ -28,3 +28,3 @@ jStat([[2,4,6]]).divide( 2 ) === [[1,2,3]];

Multiply all entries by value.
Multiplies all entries by value.

@@ -35,7 +35,7 @@ jStat([[1,2,3]]).multiply( 2 ) === [[2,4,6]];

Take dot product.
Takes dot product.
### pow( arg )
Raise all entries by value.
Raises all entries by value.

@@ -46,3 +46,3 @@ jStat([[1,2,3]]).pow( 2 ) === [[1,4,9]];

Exponentiate all entries.
Exponentiates all entries.

@@ -53,3 +53,3 @@ jStat([[0,1]]).exp() === [[1, 2.718281828459045]]

Return the natural logarithm of all entries.
Returns the natural logarithm of all entries.

@@ -60,3 +60,3 @@ jStat([[1, 2.718281828459045]]).log() === [[0,1]];

Return the absolute values of all entries.
Returns the absolute values of all entries.

@@ -67,9 +67,9 @@ jStat([[1,-2,-3]]).abs() === [[1,2,3]];

Compulte the norm of a vector. Note that if a matrix is passed, then the
first row of the matrix will be used as a vector for norm().
Computes the norm of a vector. Note that if a matrix is passed, then the
first row of the matrix will be used as a vector for `norm()`.
### angle( arg )
Compute the angle between two vectors. Note that if a matrix is passed, then
the first row of the matrix will be used as the vector for angle().
Computes the angle between two vectors. Note that if a matrix is passed, then
the first row of the matrix will be used as the vector for `angle()`.

@@ -80,23 +80,23 @@ ## Static Functionality

Add arg to all entries of the array
Adds `arg` to all entries of `arr` array.
### subtract( arr, arg )
Subtract all entries of the array by arg
Subtracts all entries of the `arr` array by `arg`.
### divide( arr, arg )
Divide all entries of the array by arg.
Divides all entries of the `arr` array by `arg`.
### multiply( arr, arg )
Multiply all entries of the array by arg.
Multiplies all entries of the `arr` array by `arg`.
### dot( arr1, arr2 )
Take dot product of array 1 and array 2.
Takes the dot product of the `arr1` and `arr2` arrays.
### outer( A, B )
Take outer product of A and B.
Takes the outer product of the `A` and `B` arrays.

@@ -107,104 +107,104 @@ outer([1,2,3],[4,5,6]) === [[4,5,6],[8,10,12],[12,15,18]]

Raise all entries of the array to the power of arg
Raises all entries of the `arr` array to the power of `arg`.
### exp(arr)
### exp( arr )
Exponentiate all entries in the array
Exponentiates all entries in the `arr` array.
### log(arr)
### log( arr )
Return the natural logarithm of all entries in the array
Returns the natural logarithm of all entries in the `arr` array
### abs(arr)
### abs( arr )
Return the absolute values of all entries in the array
Returns the absolute values of all entries in the `arr` array
### norm(arr)
### norm( arr )
Compulte the norm of a vector.
Computes the norm of the `arr` vector.
### angle( arr1, arr2 )
Compute the angle between two vectors.
Computes the angle between the `arr1` and `arr2` vectors.
### aug(A,B)
### aug( A, B )
Augments matrix A by matrix B. Note that this method returns a plain matrix,
Augments matrix `A` by matrix `B`. Note that this method returns a plain matrix,
not a jStat object.
### det(A)
### det( A )
Calculates the determinant of matrix A.
Calculates the determinant of matrix `A`.
### inv(A)
### inv( A )
Returns the inverse of the matrix A.
Returns the inverse of the matrix `A`.
### gauss_elimination(A,B)
### gauss_elimination( A, B )
Performs Gaussian Elimination on matrix A augmented by matrix B.
Performs Gaussian Elimination on matrix `A` augmented by matrix `B`.
### gauss_jordan(A,B)
### gauss_jordan( A, B )
Performs Gauss-Jordan Elimination on matrix A augmented by matrix B.
Performs Gauss-Jordan Elimination on matrix `A` augmented by matrix `B`.
### lu(A)
### lu( A )
Perform the LU decomposition on matrix A.
Perform the LU decomposition on matrix `A`.
A -> [L,U]
`A` -> `[L,U]`
st.
A=LU
`A = LU`
L is lower triangular matrix
`L` is lower triangular matrix.
U is upper triangular matrix
`U` is upper triangular matrix.
### cholesky(A)
### cholesky( A )
Performs the Cholesky decomposition on matrix A.
Performs the Cholesky decomposition on matrix `A`.
A -> T
`A` -> `T`
st.
A=TT'
`A = TT'`
T is lower triangular matrix
`T` is lower triangular matrix.
### gauss_jacobi(A,b,x,r)
### gauss_jacobi( A, b, x, r )
Solves the linear system Ax = b using the Gauss-Jacobi method with an initial guess of r.
Solves the linear system `Ax = b` using the Gauss-Jacobi method with an initial guess of `r`.
### gauss_seidel(A,b,x,r)
### gauss_seidel( A, b, x, r )
Solves the linear system Ax = b using the Gauss-Seidel method with an initial guess of r.
Solves the linear system `Ax = b` using the Gauss-Seidel method with an initial guess of `r`.
### SOR(A,b,x,r,w)
### SOR( A, b, x, r, w )
Solves the linear system Ax = b using the sucessive over-relaxation method with an initial guess of r and parameter w (omega).
Solves the linear system `Ax = b` using the sucessive over-relaxation method with an initial guess of `r` and parameter `w` (omega).
### householder(A)
### householder( A )
Performs the householder transformation on the matrix A.
Performs the householder transformation on the matrix `A`.
### QR(A)
### QR( A )
Performs the Cholesky decomposition on matrix A.
Performs the Cholesky decomposition on matrix `A`.
A -> [Q,R]
`A` -> `[Q,R]`
Q is orthogonal matrix
`Q` is the orthogonal matrix.
R is upper triangular
`R` is the upper triangular.
### lstsq(A,b)
### lstsq( A, b )
solve least squard problem for Ax=b as QR decomposition way.
Solves least squard problem for Ax=b as QR decomposition way.
if b is [[b1],[b2],[b3]] form will return [[x1],[x2],[x3]] array form solution.
If `b` is of the `[[b1], [b2], [b3]]` form, the method will return an array of the `[[x1], [x2], [x3]]` form solution.
else b is [b1,b2,b3] form will return [x1,x2,x3] array form solution.
Otherwise, if `b` is of the `[b1, b2, b3]` form, the method will return an array of the `[x1,x2,x3]` form solution.

@@ -211,0 +211,0 @@

2

doc/md/models.md

@@ -5,3 +5,3 @@ ## Regression Models

### ols(endog,exog)
### ols( endog, exog )

@@ -8,0 +8,0 @@ What's the `endog`, `exog`?

34

doc/md/special-functions.md

@@ -5,23 +5,23 @@ ## Special Functions

Evaluates the Beta function at (x,y).
Evaluates the Beta function at `(x,y)`.
### betaln( x, y )
Evaluates the log Beta function at (x,y).
Evaluates the log Beta function at `(x,y)`.
### betacf( x, a, b )
Returns the continued fraction for the incomplete Beta function with parameters a and b modified by Lentz's method evaluated at x.
Returns the continued fraction for the incomplete Beta function with parameters a and b modified by Lentz's method evaluated at `x`.
### ibetainv( p, a, b)
Returns the inverse of the incomplete Beta function evaluated at (p,a,b).
Returns the inverse of the incomplete Beta function evaluated at `(p,a,b)`.
### ibeta( x, a, b)
Returns the incomplete Beta function evaluated at (x,a,b).
Returns the incomplete Beta function evaluated at `(x,a,b)`.
### gammafn( x )
Returns the Gamma function evaluated at x. This is sometimes called the 'complete' gamma function.
Returns the Gamma function evaluated at `x`. This is sometimes called the 'complete' gamma function.

@@ -32,7 +32,7 @@ This function is tested against Mathematica's Gamma[x].

Returns the Log-Gamma function evaluated at x.
Returns the Log-Gamma function evaluated at `x`.
### gammap( a, x )
Returns the lower incomplete gamma function evaluated at (a,x).
Returns the lower incomplete gamma function evaluated at `(a,x)`.
This function is usually written with a lower case greek gamma character, and is one of the two [incomplete gamma functions](http://mathworld.wolfram.com/IncompleteGammaFunction.html).

@@ -45,3 +45,3 @@

Returns the lower regularized incomplete gamma function evaluated at (a,x).
Returns the lower regularized incomplete gamma function evaluated at `(a,x)`.
It is defined as the quotient of the lower incomplete gamma function evaluated at (a, x) and the upper incomplete gamma function ('the gamma function') evaluated at (a).

@@ -54,3 +54,3 @@ This function is usually written as P(x, a); and is one of the two [regularized gamma functions](http://mathworld.wolfram.com/RegularizedGammaFunction.html).

Returns the inverse of the lower regularized incomplete Gamma function evaluated at (p,a).
Returns the inverse of the lower regularized incomplete Gamma function evaluated at `(p,a)`.
This function is the inverse of lowerRegularizedGamma(x, a).

@@ -60,15 +60,15 @@

Returns the natural log factorial of n.
Returns the natural log factorial of `n`.
### factorial( n )
Returns the factorial of n.
Returns the factorial of `n`.
### combination( n, m )
Returns the number of combinations of n, m.
Returns the number of combinations of `n`, `m`.
### permutation( n, m )
Returns the number of permutations of n, m.
Returns the number of permutations of `n`, `m`.

@@ -78,11 +78,11 @@

Returns the error function evaluated at x.
Returns the error function evaluated at `x`.
### erfc( x )
Returns the complementary error function evaluated at x.
Returns the complementary error function evaluated at `x`.
### erfcinv( p )
Returns the inverse of the complementary error function evaluated at p.
Returns the inverse of the complementary error function evaluated at `p`.

@@ -89,0 +89,0 @@ ### randn( n, m )

160

doc/md/test.md

@@ -11,24 +11,24 @@ ## Statistical Tests

### zscore( value[, flag])
### zscore( value[, flag] )
Returns the z-score of the value taking the jStat object as the observed
values. Flag==true denotes use of sample standard deviation.
Returns the z-score of `value` taking the jStat object as the observed
values. `flag===true` denotes use of sample standard deviation.
### ztest( value, sides[, flag])
### ztest( value, sides[, flag] )
Returns the p-value of the value taking the jStat object as the observed
values. Sides is an integer value 1 or 2 denoting a 1 or 2 sided z-test.
The test defaults to a 2 sided z-test if sides is not specified.Flag==true
denotes use of sample standard devaition.
Returns the p-value of `value` taking the jStat object as the observed
values. `sides` is an integer value 1 or 2 denoting a 1 or 2 sided z-test.
The test defaults to a 2 sided z-test if `sides` is not specified. `flag===true`
denotes use of sample standard deviation.
### tscore( value)
### tscore( value )
Returns the t-score of the value taking the jStat object as the observed
Returns the t-score of `value` taking the jStat object as the observed
values.
### ttest( value, sides)
### ttest( value, sides )
Returns the p-value of the value taking the jStat object as the observed
values. Sides is an integer value 1 or 2 denoting a 1 or 2 sided t-test.
The test defaults to a 2 sided t-test if sides is not specified.
Returns the p-value of `value` taking the jStat object as the observed
values. `sides` is an integer value 1 or 2 denoting a 1 or 2 sided t-test.
The test defaults to a 2 sided t-test if `sides` is not specified.

@@ -47,30 +47,30 @@ ### anovafscore()

### jStat.zscore( value, mean, sd)
### jStat.zscore( value, mean, sd )
Returns the z-score of value given the mean and the standard deviation
Returns the z-score of `value` given the `mean` mean and the `sd` standard deviation
of the test.
### jStat.zscore( value, array[, flag])
### jStat.zscore( value, array[, flag] )
Returns the z-score of value given the data from array. Flag==true denotes
Returns the z-score of `value` given the data from `array`. `flag===true` denotes
use of the sample standard deviation.
### jStat.ztest( value, mean, sd, sides)
### jStat.ztest( value, mean, sd, sides )
Returns the p-value of a the z-test of value given the mean and standard
deviation of the test. Sides is an integer value 1 or 2 denoting a
one or two sided z-test. If sides is not specified the test defaults
Returns the p-value of a the z-test of `value` given the `mean` mean and `sd` standard
deviation of the test. `sides` is an integer value 1 or 2 denoting a
one or two sided z-test. If `sides` is not specified the test defaults
to a two sided z-test.
### jStat.ztest( zscore, sides)
### jStat.ztest( zscore, sides )
Returns the p-value of the z-score. Sides is an integer value 1 or 2
denoting a one or two sided z-test. If sides is not specified the test
Returns the p-value of the `zscore` z-score. `sides` is an integer value 1 or 2
denoting a one or two sided z-test. If `sides` is not specified the test
defaults to a two sided z-test
### jStat.ztest( value, array, sides[, flag])
### jStat.ztest( value, array, sides[, flag] )
Returns the p-value of value given the data from the array. Sides is
an integer value 1 or 2 denoting a one or two sided z-test. If sides
is not specified the test defaults to a two sided z-test. flag==true
Returns the p-value of `value` given the data from `array`. `sides` is
an integer value 1 or 2 denoting a one or two sided z-test. If `sides`
is not specified the test defaults to a two sided z-test. `flag===true`
denotes the use of the sample standard deviation.

@@ -80,29 +80,29 @@

### jStat.tscore( value, mean, sd, n)
### jStat.tscore( value, mean, sd, n )
Returns the t-score of the value given the mean, standard deviation,
and the sample size n.
Returns the t-score of `value` given the `mean` mean, `sd` standard deviation,
and the sample size `n`.
### jStat.tscore( value, array)
### jStat.tscore( value, array )
Returns the t-score of value given the data from the array.
Returns the t-score of `value` given the data from `array`.
### jStat.ttest( value, mean, sd, n, sides)
### jStat.ttest( value, mean, sd, n, sides )
Returns the p-value of value given the mean, standard deviation,
and the sample size n. Sides is an integer value 1 or 2 denoting
a one or two sided t-test. If sides is not specified the test
Returns the p-value of `value` given the `mean` mean, `sd` standard deviation,
and the sample size `n`. `sides` is an integer value 1 or 2 denoting
a one or two sided t-test. If `sides` is not specified the test
defaults to a two sided t-test.
### jStat.ttest( tscore, n, sides)
### jStat.ttest( tscore, n, sides )
Returns the p-value of the t-score given the sample size n. Sides
Returns the p-value of the `tscore` t-score given the sample size `n`. `sides`
is an integer value 1 or 2 denoting a one or two sided t-test.
If sides is not specified the test defaults to a two sided t-test.
If `sides` is not specified the test defaults to a two sided t-test.
### jStat.ttest( value, array, sides)
### jStat.ttest( value, array, sides )
Returns the p-value of the value given the data in the array.
Sides is an integer value 1 or 2 denoting a one or two sided
t-test. If sides is not specified the test defaults to a two
Returns the p-value of `value` given the data in `array`.
`sides` is an integer value 1 or 2 denoting a one or two sided
t-test. If `sides` is not specified the test defaults to a two
sided t-test.

@@ -112,11 +112,11 @@

### jStat.anovafscore( array1, array2, ..., arrayn)
### jStat.anovafscore( array1, array2, ..., arrayn )
Returns the f-score of an ANOVA on the arrays.
### jStat.anovafscore( [array1,array2, ...,arrayn])
### jStat.anovafscore( [array1,array2, ...,arrayn] )
Returns the f-score of an ANOVA on the arrays.
### jStat.anovaftest( array1, array2, ...., arrayn)
### jStat.anovaftest( array1, array2, ...., arrayn )

@@ -128,4 +128,4 @@ Returns the p-value of the f-statistic from the ANOVA

Returns the p-value for the fscore with a numerator degress
of freedom df1 and the denominator degrees of freedom df2
Returns the p-value for the `fscore` f-score with a `df1` numerator degrees
of freedom and a `df2` denominator degrees of freedom.

@@ -137,4 +137,4 @@ ## Tukey's Range Test

Returns the q-score of a single pairwise comparison between arrays
of mean mean1 and mean2, size n1 and n2, and standard deviation (of
all vectors) sd.
of mean `mean1` and `mean2`, size `n1` and `n2`, and standard deviation (of
all vectors) `sd`.

@@ -148,4 +148,4 @@ ### jStat.qscore( array1, array2, sd )

Returns the p-value of the q-score given the total sample size n
and k number of populations.
Returns the p-value of the q-score given the total sample size `n`
and `k` number of populations.

@@ -155,5 +155,5 @@ ### jStat.qtest( mean1, mean2, n1, n2, sd, n, k )

Returns the p-value of a single pairwise comparison between arrays
of mean mean1 and mean2, size n1 and n2, and standard deviation (of
all vectors) sd, where the total sample size is n and the number of
populations is k.
of mean `mean1` and `mean2`, size `n1` and `n2`, and standard deviation (of
all vectors) `sd`, where the total sample size is `n` and the number of
populations is `k`.

@@ -183,37 +183,37 @@ ### jStat.qtest( array1, array2, sd, n, k )

### jStat.normalci( value, alpha, sd, n)
### jStat.normalci( value, alpha, sd, n )
Returns a 1-alpha confidence interval for the value given
a normal distribution with a standard deviation sd and a
sample size n
Returns a 1-alpha confidence interval for `value` given
a normal distribution with a standard deviation `sd` and a
sample size `n`
### jStat.normalci( value, alpha, array)
### jStat.normalci( value, alpha, array )
Returns a 1-alpha confidence interval for the value given
a normal distribution in the data from the array.
Returns a 1-alpha confidence interval for `value` given
a normal distribution in the data from `array`.
### jStat.tci( value, alpha, sd, n)
### jStat.tci( value, alpha, sd, n )
Returns a 1-alpha confidence interval for the value given
the standard deviation and the sample size n.
Returns a 1-alpha confidence interval for `value` given
the standard deviation `sd` and the sample size `n`.
### jStat.tci( value, alpha, array)
### jStat.tci( value, alpha, array )
Returns a 1-alpha confidence interval for the value given
the data from the array.
Returns a 1-alpha confidence interval for `value` given
the data from `array`.
### jStat.oneSidedDifferenceOfProportions( p1, n1, p2, n2)
### jStat.oneSidedDifferenceOfProportions( p1, n1, p2, n2 )
Returns the p-value for a 1-sided test for the difference
between two proportions. p1 is the sample proportion for
the first sample, whereas p2 is the sample proportion for
the second sample. Similiarly, n1 is the sample size of the
first sample and n2 is the sample size for the second sample.
between two proportions. `p1` is the sample proportion for
the first sample, whereas `p2` is the sample proportion for
the second sample. Similiarly, `n1` is the sample size of the
first sample and `n2` is the sample size for the second sample.
### jStat.twoSidedDifferenceOfProportions( p1, n1, p2, n2)
### jStat.twoSidedDifferenceOfProportions( p1, n1, p2, n2 )
Returns the p-value for a 2-sided test for the difference
between two proportions. p1 is the sample proportion for
the first sample, whereas p2 is the sample proportion for
the second sample. Similiarly, n1 is the sample size of the
first sample and n2 is the sample size for the second sample.
between two proportions. `p1` is the sample proportion for
the first sample, whereas `p2` is the sample proportion for
the second sample. Similiarly, `n1` is the sample size of the
first sample and `n2` is the sample size for the second sample.

188

doc/md/vector.md

@@ -7,9 +7,9 @@ ## Vector Functionality

Return the sum of a vector.
Returns the sum of the `array` vector.
jStat.sum([1,2,3]) === 6
**fn.sum( [bool][,callback] )**
**fn.sum( [bool][, callback] )**
Return the sum of a vector or matrix columns.
Returns the sum of a vector or matrix columns.

@@ -31,3 +31,3 @@ jStat( 1, 5, 5 ).sum() === 15

jStat([[1,2],[3,4]]).sum(true,function( result ) {
jStat([[1,2],[3,4]]).sum(true, function( result ) {
// result === 10

@@ -40,9 +40,9 @@ });

Return the sum squared of a vector.
Returns the sum squared of the `array` vector.
jStat.sumsqrd([1,2,3]) === 14
**fn.sumsqrd( [bool][,callback] )**
**fn.sumsqrd( [bool][, callback] )**
Return the sum squared of a vector or matrix columns.
Returns the sum squared of a vector or matrix columns.

@@ -72,9 +72,9 @@ jStat( 1, 5, 5 ).sumsqrd() === 55

Return the sum of squared errors of prediction of a vector.
Returns the sum of squared errors of prediction of the `array` vector.
jStat.sumsqerr([1,2,3]) === 2
**fn.sumsqerr( [bool][,callback] )**
**fn.sumsqerr( [bool][, callback] )**
Return the sum of squared errors of prediction of a vector or matrix columns.
Returns the sum of squared errors of prediction of a vector or matrix columns.

@@ -104,9 +104,9 @@ jStat( 1, 5, 5 ).sumsqerr() === 10

Return the sum of a vector in row-based order.
Returns the sum of the `array` vector in row-based order.
jStat.sumrow([1,2,3]) === 6
**fn.sumrow( [bool][,callback] )**
**fn.sumrow( [bool][, callback] )**
Return the sum of a vector or matrix rows.
Returns the sum of a vector or matrix rows.

@@ -137,9 +137,9 @@ jStat( 1, 5, 5 ).sumrow() === 15

Return the product of a vector.
Returns the product of the `array` vector.
jStat.product([1,2,3]) === 6
**fn.product( [bool][,callback] )**
**fn.product( [bool][, callback] )**
Return the product of a vector or matrix columns.
Returns the product of a vector or matrix columns.

@@ -169,9 +169,9 @@ jStat( 1, 5, 5 ).product() === 120

Return the minimum value of a vector.
Returns the minimum value of the `array` vector.
jStat.min([1,2,3]) === 1
**fn.min( [bool][,callback] )**
**fn.min( [bool][, callback] )**
Return the minimum value of a vector or matrix columns.
Returns the minimum value of a vector or matrix columns.

@@ -201,9 +201,9 @@ jStat( 1, 5, 5 ).min() === 1

Return the maximum value of a vector.
Returns the maximum value of the `array` vector.
jStat.max([1,2,3]) === 3
**fn.max( [bool][,callback] )**
**fn.max( [bool][, callback] )**
Return the maximum value of a vector or matrix columns.
Returns the maximum value of a vector or matrix columns.

@@ -233,3 +233,3 @@ jStat( 1, 5, 5 ).max() === 5

Return the mean of a vector.
Returns the mean of the `array` vector.

@@ -240,3 +240,3 @@ jStat.mean([1,2,3]) === 2

Return the mean of a vector or matrix columns.
Returns the mean of a vector or matrix columns.

@@ -266,9 +266,9 @@ jStat( 1, 5, 5 ).mean() === 3

Return the mean squared error of a vector.
Returns the mean squared error of the `array` vector.
jStat.meansqerr([1,2,3]) === 0.66666...
**fn.meansqerr( [bool][,callback] )**
**fn.meansqerr( [bool][, callback] )**
Return the mean squared error of a vector or matrix columns.
Returns the mean squared error of a vector or matrix columns.

@@ -298,9 +298,9 @@ jStat( 1, 5, 5 ).meansqerr() === 2

Return the geometric mean of a vector.
Returns the geometric mean of the `array` vector.
jStat.geomean([4,1,1/32]) === 0.5
**fn.geomean( [bool][,callback] )**
**fn.geomean( [bool][, callback] )**
Return the geometric mean of a vector or matrix columns.
Returns the geometric mean of a vector or matrix columns.

@@ -330,9 +330,9 @@ jStat([4,1,1\32]).geomean() === 0.5

Return the median of a vector.
Returns the median of the `array` vector.
jStat.median([1,2,3]) === 2
**fn.median( [bool][,callback] )**
**fn.median( [bool][, callback] )**
Return the median of a vector or matrix columns.
Returns the median of a vector or matrix columns.

@@ -362,9 +362,9 @@ jStat( 1, 5, 5 ).median() === 3

Return an array of partial sums in the sequence.
Returns an array of partial sums in the sequence.
jStat.cumsum([1,2,3]) === [1,3,6]
**fn.cumsum( [bool][,callback] )**
**fn.cumsum( [bool][, callback] )**
Return an array of partial sums for a vector or matrix columns.
Returns an array of partial sums for a vector or matrix columns.

@@ -394,9 +394,9 @@ jStat( 1, 5, 5 ).cumsum() === [1,3,6,10,15]

Return an array of partial products in the sequence.
Returns an array of partial products in the sequence.
jStat.cumprod([2,3,4]) === [2,6,24]
**fn.cumprod( [bool][,callback] )**
**fn.cumprod( [bool][, callback] )**
Return an array of partial products for a vector or matrix columns.
Returns an array of partial products for a vector or matrix columns.

@@ -426,9 +426,9 @@ jStat( 1, 5, 5 ).cumprod() === [1,2,6,24,120]

Return an array of the successive differences of the array.
Returns an array of the successive differences of the array.
jStat.diff([1,2,2,3]) === [1,0,1]
**fn.diff( [bool][,callback] )**
**fn.diff( [bool][, callback] )**
Return an array of successive differences for a vector or matrix columns.
Returns an array of successive differences for a vector or matrix columns.

@@ -458,9 +458,9 @@ jStat([1,2,2,3]).diff() === [1,0,1]

Return an array of the ranks of the array.
Returns an array of the ranks of the array.
jStat.rank([1, 2, 2, 3]) === [1, 2.5, 2.5, 4]
**fn.rank( [bool][,callback] )**
**fn.rank( [bool][, callback] )**
Return an array of ranks for a vector or matrix columns.
Returns an array of ranks for a vector or matrix columns.

@@ -490,3 +490,3 @@ jStat([1, 2, 2, 3]).rank() === [1, 2.5, 2.5, 4]

Return the mode of a vector.
Returns the mode of the `array` vector.
If there are multiple modes then `mode()` will return all of them.

@@ -497,5 +497,5 @@

**fn.mode( [bool][,callback] )**
**fn.mode( [bool][, callback] )**
Return the mode for a vector or matrix columns.
Returns the mode for a vector or matrix columns.

@@ -520,9 +520,9 @@ jStat([1,2,2,3]).mode() === 2

Return the range of a vector
Returns the range of the `array` vector.
jStat.range([1,2,3]) === 2
**fn.range( [bool][,callback] )**
**fn.range( [bool][, callback] )**
Return the range for a vector or matrix columns.
Returns the range for a vector or matrix columns.

@@ -550,7 +550,7 @@ jStat([1,2,3]).range() === 2

**variance( array[,flag])**
**variance( array[, flag] )**
Return the variance of a vector.
Returns the variance of the `array` vector.
By default, the population variance is calculated.
Passing true as the flag indicates computes the sample variance instead.
Passing `true` to `flag` indicates to compute the sample variance instead.

@@ -560,5 +560,5 @@ jStat.variance([1,2,3,4]) === 1.25

**fn.variance( [bool][,callback] )**
**fn.variance( [bool][, callback] )**
Return the variance for a vector or matrix columns.
Returns the variance for a vector or matrix columns.

@@ -591,3 +591,3 @@ **Note:** Cannot pass flag to indicate between population or sample for matrices.

Return the pooled (sample) variance of an array of vectors.
Returns the pooled (sample) variance of an array of vectors.
Assumes the population variance of the vectors are the same.

@@ -601,9 +601,9 @@

Return the deviation of a vector.
Returns the deviation of the `array` vector.
jStat.deviation([1,2,3,4]) === [-1.5, -0.5, 0.5, 1.5]
**fn.deviation( [bool][,callback] )**
**fn.deviation( [bool][, callback] )**
Return the deviation for a vector or matrix columns.
Returns the deviation for a vector or matrix columns.

@@ -631,7 +631,7 @@ jStat([1,2,3,4]).deviation() === [-1.5, -0.5, 0.5, 1.5]

**stdev( array[,flag])**
**stdev( array[, flag] )**
Return the standard deviation of a vector.
By defaut, the population standard deviation is returned.
Passing true for the flag parameter returns the sample standard deviation.
Returns the standard deviation of the `array` vector.
By default, the population standard deviation is returned.
Passing `true` to `flag` returns the sample standard deviation.

@@ -644,7 +644,7 @@ The 'sample' standard deviation is also called the 'corrected standard deviation', and is an unbiased estimator of the population standard deviation.

**fn.stdev( [bool][,callback] )**
**fn.stdev( [bool][, callback] )**
Return the standard deviation for a vector or matrix columns.
Returns the standard deviation for a vector or matrix columns.
**Note:** Cannot pass flag to indicate between population or sample for matrices.
**Note:** Cannot pass `flag` to indicate between population or sample for matrices.
There is a feature request for this on [Issue #51](https://github.com/jstat/jstat/issues/51).

@@ -679,3 +679,3 @@

Return the pooled (sample) standard deviation of an array of vectors.
Returns the pooled (sample) standard deviation of an array of vectors.
Assumes the population standard deviation of the vectors are the same.

@@ -689,9 +689,9 @@

Return the mean absolute deviation of a vector.
Returns the mean absolute deviation of the `array` vector.
jStat.meandev([1,2,3,4]) === 1
**fn.meandev( [bool][,callback] )**
**fn.meandev( [bool][, callback] )**
Return the mean absolute deviation for a vector or matrix columns.
Returns the mean absolute deviation for a vector or matrix columns.

@@ -721,9 +721,9 @@ jStat([1,2,3,4]).meandev() === 1

Return the median absolute deviation of a vector.
Returns the median absolute deviation of the `array` vector.
jStat.meddev([1,2,3,4]) === 1
**fn.meddev( [bool][,callback] )**
**fn.meddev( [bool][, callback] )**
Return the median absolute deviation for a vector or matrix columns.
Returns the median absolute deviation for a vector or matrix columns.

@@ -753,3 +753,3 @@ jStat([1,2,3,4]).meddev() === 1

Return the skewness of a vector (third standardized moment).
Returns the skewness of the `array` vector (third standardized moment).

@@ -762,3 +762,3 @@ jStat.skewness([1,2,2,3,5]) === 0.75003...

Return the excess kurtosis of a vector (fourth standardized moment - 3).
Returns the excess kurtosis of the `array` vector (fourth standardized moment - 3).

@@ -771,9 +771,9 @@ jStat.kurtosis([1,2,3,4]) === -0.63610...

Return the coefficient of variation of a vector.
Returns the coefficient of variation of the `array` vector.
jStat.coeffvar([1,2,3,4]) === 0.447...
**fn.coeffvar( [bool][,callback] )**
**fn.coeffvar( [bool][, callback] )**
Return the coefficient of variation for a vector or matrix columns.
Returns the coefficient of variation for a vector or matrix columns.

@@ -803,3 +803,3 @@ jStat([1,2,3,4]).coeffvar() === 0.447...

Return the quartiles of a vector.
Returns the quartiles of the `array` vector.

@@ -810,3 +810,3 @@ jStat.quartiles( jStat.seq(1,100,100)) === [25,50,75]

Return the quartiles for a vector or matrix columns.
Returns the quartiles for a vector or matrix columns.

@@ -826,5 +826,5 @@ jStat(1,100,100).quartiles() === [25,50,75]

**quantiles( dataArray, quantilesArray, [alphap[, betap]] )**
**quantiles( dataArray, quantilesArray[, alphap[, betap]] )**
Like quartiles, but calculate and return arbitrary quantiles of a vector
Like quartiles, but calculate and return arbitrary quantiles of the `dataArray` vector
or matrix (column-by-column).

@@ -843,3 +843,3 @@

Return the k-th percentile of values in a range, where k is in the range 0..1, exclusive.
Returns the k-th percentile of values in the `dataArray` range, where k is in the range 0..1, exclusive.

@@ -853,3 +853,3 @@ jStat.percentile([1, 2, 3, 4], 0.3) === 1.9;

The percentile rank of score in a given array. Returns the percentage
of all values in the input array that are less than (if `kind == 'strict'`) or
of all values in `dataArray` that are less than (if `kind == 'strict'`) or
less or equal than (if `kind == 'weak'`) score. Default is `'weak'`.

@@ -861,6 +861,6 @@

**histogram( dataArray, [numBins] )**
**histogram( dataArray[, numBins] )**
The histogram data defined as the number of dataArray elements found in
equally sized bins across the range of dataArray. Default number
The histogram data defined as the number of `dataArray` elements found in
equally sized bins across the range of `dataArray`. Default number
of bins is 4.

@@ -872,5 +872,5 @@

**covariance( array, array )**
**covariance( array1, array2 )**
Return the covariance of two vectors.
Returns the covariance of the `array1` and `array2` vectors.

@@ -882,5 +882,5 @@ var seq = jStat.seq( 0, 10, 11 );

**corrcoeff( array, array )**
**corrcoeff( array1, array2 )**
Return the population correlation coefficient of two vectors (Pearson's Rho).
Returns the population correlation coefficient of the `array1` and `array2` vectors (Pearson's Rho).

@@ -891,7 +891,7 @@ var seq = jStat.seq( 0, 10, 11 );

**spearmancoeff( array, array )**
**spearmancoeff( array1, array2 )**
Return the rank correlation coefficient of two vectors (Spearman's Rho).
Returns the rank correlation coefficient of the `array1` and `array2` vectors (Spearman's Rho).
jStat.spearmancoeff([1, 2, 3, 4], [5, 6, 9, 7]) == 0.8;
jStat.spearmancoeff([1, 2, 2, 4], [5, 2, 5, 7]) == 0.5;

44

package.json
{
"name" : "jstat",
"version" : "1.7.2",
"description" : "Statistical Library for JavaScript",
"homepage" : "http://github.com/jstat/jstat",
"main" : "./dist/jstat.js",
"author" : {
"name" : "Trevor Norris",
"email" : "trev.norris@gmail.com",
"url" : "http://trevorjnorris.com"
"name": "jstat",
"version": "1.8.0",
"description": "Statistical Library for JavaScript",
"homepage": "http://github.com/jstat/jstat",
"main": "./dist/jstat.js",
"author": {
"name": "Trevor Norris",
"email": "trev.norris@gmail.com",
"url": "http://trevorjnorris.com"
},
"licenses" : [{
"type" : "MIT",
"url" : "http://www.opensource.org/licenses/mit-license.php"
}],
"repository" : {
"type" : "git",
"url" : "http://github.com/jstat/jstat.git"
"licenses": [
{
"type": "MIT",
"url": "http://www.opensource.org/licenses/mit-license.php"
}
],
"repository": {
"type": "git",
"url": "http://github.com/jstat/jstat.git"
},
"devDependencies" : {
"uglify-js" : ">=2.6.0",
"vows" : "^0.8.2"
"devDependencies": {
"uglify-js": ">=2.6.0",
"vows": "^0.8.2"
},
"scripts" : {
"test" : "./node_modules/vows/bin/vows --spec --isolate"
"scripts": {
"test": "./node_modules/vows/bin/vows --spec --isolate"
}
}

2

README.md

@@ -32,3 +32,3 @@ [jStat](http://www.jstat.org/) - JavaScript Statistical Library

The library is hosted on [jsDelivr](http://www.jsdelivr.com/) using the follwing
The library is hosted on [jsDelivr](http://www.jsdelivr.com/) using the following
url:

@@ -35,0 +35,0 @@ ```

dist/jstat.js

Sorry, the diff of this file is too big to display

dist/jstat.min.js

Sorry, the diff of this file is too big to display

Makefile

Sorry, the diff of this file is not supported yet

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