Analyses

linear(x, y; wts = similar(x, 0), interval = length(wts) > 0 ? nothing : :confidence)

Compute a linear fit of y ~ 1 + x. Weighted data is supported via the keyword wts. Use interval to specify what type of interval the shaded band should represent. Valid values of interval are :confidence delimiting the uncertainty of the predicted relationship, and :prediction delimiting estimated bounds for new data points.

smooth(x, y, span=0.75, degreee=2)

Fit a loess model. span is the degree of smoothing, typically in [0,1]. Smaller values result in smaller local context in fitting. degree is the polynomial degree used in the loess model.

density(data...; trim = false, boundary, npoints, kernel, bandwidth)

Fit a kernel density estimation of data. Only 1D and 2D are supported so far. The optional keyword arguments are

• boundary: the lower and upper limits of the kde as a tuple. Due to the fourier transforms used internally, there should be sufficient spacing to prevent wrap-around at the boundaries.
• npoints: the number of interpolation points to use. The function uses fast Fourier transforms (FFTs) internally, so for optimal efficiency this should be a power of 2 (default = 2048).
• kernel: the distributional family from Distributions.jl to use as the kernel (default = Normal). To add your own kernel, extend the internal kernel_dist function.
• bandwidth: the bandwidth of the kernel. Default is to use Silverman's rule.

histogram(data...; bins = automatic, wts = automatic, normalization = :none)

Plot a histogram of values. bins can be an Int to create that number of equal-width bins over the range of values. Alternatively, it can be a sorted iterable of bin edges. The histogram can be normalized by setting normalization. Possible values are:

• :pdf: Normalize by sum of weights and bin sizes. Resulting histogram has norm 1 and represents a PDF.
• :density: Normalize by bin sizes only. Resulting histogram represents count density of input and does not have norm 1.
• :probability: Normalize by sum of weights only. Resulting histogram represents the fraction of probability mass for each bin and does not have norm 1.
• :none: Do not normalize.

Weighted data is supported via the keyword wts.

frequency(data...)

Compute a frequency table of the arguments.

reducer(args...; agg=Mean())

Reduce the last argument conditioned on the preceding ones using the online statistic or binary function agg.