Name  Description 
linearRegress(Y, input)

Perform a linear regression as in linearRegressBeta, but return a
RegressRes with useful stuff for statistical inference. If the last element
of input is a real, this is used to specify the confidence intervals to
be calculated. Otherwise, the default of 0.95 is used. The rest of input
should be the elements of X.

linearRegressBeta(Y, XIn)

Perform a linear regression and return just the beta values. The advantages
to just returning the beta values are that it's faster and that each range
needs to be iterated over only once, and thus can be just an input range.
The beta values are returned such that the smallest index corresponds to
the leftmost element of X. X can be either a tuple or a range of input
ranges. Y must be an input range.

linearRegressBetaBuf(buf, Y, XRidge)

Same as linearRegressBeta, but allows the user to specify a buffer for
the beta terms. If the buffer is too short, a new one is allocated.
Otherwise, the results are returned in the userprovided buffer.

linearRegressPenalized(yIn, xIn, lasso, ridge)

Performs lasso (L1) and/or ridge (L2) penalized linear regression. Due to the
way the data is standardized, no intercept term should be included in x
(unlike linearRegress and linearRegressBeta). The intercept coefficient is
implicitly included and returned in the first element of the returned array.
Usage is otherwise identical.

loess1D(y, x, span, degree)

This function performs loess regression. Loess regression is a local
regression procedure, where a prediction of the dependent (y) variable
is made from an observation of the independent (x) variable by weighted
least squares over x values in the neighborhood of the value being evaluated.

logistic(xb)

The logistic function used in logistic regression.

logisticRegress(yIn, input)

Similar to logisticRegressBeta, but returns a LogisticRes with useful stuff for
statistical inference. If the last element of input is a floating point
number instead of a range, it is used to specify the confidence interval
calculated. Otherwise, the default of 0.95 is used.

logisticRegressBeta(yIn, xRidge)

Computes a logistic regression using a maximum likelihood estimator
and returns the beta coefficients. This is a generalized linear model with
the link function f(XB) = 1 / (1 + exp(XB)). This is generally used to model
the probability that a binary Y variable is 1 given a set of X variables.

logisticRegressPenalized(yIn, xIn, lasso, ridge)

Performs lasso (L1) and/or ridge (L2) penalized logistic regression. Due to the
way the data is standardized, no intercept term should be included in x
(unlike logisticRegress and logisticRegressBeta). The intercept coefficient is
implicitly included and returned in the first element of the returned array.
Usage is otherwise identical.

polyFit(Y, X, N, confInt)

Convenience function that takes a forward range X and a forward range Y,
creates an array of PowMap structs for integer powers 0 through N,
and calls linearRegress.

polyFitBeta(Y, X, N, ridge)

Convenience function that takes a forward range X and a forward range Y,
creates an array of PowMap structs for integer powers from 0 through N,
and calls linearRegressBeta.

polyFitBetaBuf(buf, Y, X, N, ridge)

Same as polyFitBeta, but allows the caller to provide an explicit buffer
to return the coefficients in. If it's too short, a new one will be
allocated. Otherwise, results will be returned in the userprovided buffer.

powMap(range, exponent)

Maps a forward range to a power determined at runtime. ExpType is the type
of the exponent. Using an int is faster than using a double, but obviously
less flexible.

residuals(betas, Y, X)

Given the beta coefficients from a linear regression, and X and Y values,
returns a range that lazily computes the residuals.

_arrayExpSliceMulSliceAddass_d(p2, p1, c0)


_arrayExpSliceMulSliceAssign_d(p2, p1, c0)


_arrayExpSliceMulSliceMinass_d(p2, p1, c0)


_arraySliceExpMulSliceAddass_d(p2, c1, p0)


_arraySliceSliceMinSliceAssign_d(p2, p1, p0)


_arraySliceSliceMulSliceAssign_d(p2, p1, p0)

