Here is a list of all topics with brief descriptions:

Core	The first layer (decisions.md [layered-headers]): what the rest of the library builds on and what depends on nothing but Eigen
Allocation	Storage sized by a space never touches the heap when the space is fixed or bounded (see Spaces), and an unbounded space allocates through Eigen's ordinary heap path
Execution policies	Where loops run (decisions.md [execution-policies])
Resamplers	A resampler is a function object called as `r(weights, ancestors, rng)`
Time steps	What a step of time may be (decisions.md [time-steps])
Distributions	The third layer (decisions.md [layered-headers]): the distributions, each stored as parameters and evaluated through a density, and what is computed from them
Divergences	How far apart two distributions are
Upkeep	Upkeep keeps a filter's representation in bounds without meaning to change the distribution much: resampling particles whose weights have degenerated, roughening them after, pruning the improbable particles, components, or cells, or repairing a covariance that rounding has left not positive definite
Filters	The fifth layer (decisions.md [layered-headers]): a filter owns its process model and a state, predicts with predict(dt, inputs...), and updates with update(measurement) [filters-and-recursions]
Upkeep	Upkeep keeps a filter's representation in bounds without meaning to change the distribution much: resampling particles whose weights have degenerated, roughening them after, pruning the improbable particles, components, or cells, or repairing a covariance that rounding has left not positive definite
Models	The fourth layer (decisions.md [layered-headers]): how a distribution moves through a function and what a filter is given to work with
Transforms	Transform() pushes independent uncertain inputs through a function \( y = f(x) \boxplus v \) and returns the distribution of \( y \) as a Gaussian (decisions.md [transform-policies])
Smoothers	The last layer (decisions.md [layered-headers]): estimates of past states from later data [smoothing]
Spaces	The second layer (decisions.md [layered-headers]): where values live
Discrete spaces	A discrete space is a finite set of values, such as a maneuver regime, a sensor's inlier/outlier state, or the cells of a grid