Fits a linear model to survey data in each node obtained by
recursively partitioning the data.  The splitting variables and splits
selected are obtained using a procedure which adjusts for complex sample
design features used to obtain the data. Likewise the model fitting
algorithm produces design-consistent coefficients to the least squares
linear model between the dependent and independent variables.
The first stage of the design is accounted for in the provided variance
estimates. The main function returns the resulting binary tree with the
linear model fit at every end-node. The package provides a number of
functions and methods for these trees.

person("Daniell", "Toth", email="danielltoth@yahoo.com",
role=c("aut", "cre"))

Functions to allow users to build and analyze design consistent
tree and random forest models using survey data from a complex sample
design.  The algorithm can fit a linear model to survey data in each
node obtained by recursively partitioning the data.  The splitting
variables and selected splits are obtained using a randomized permutation
test procedure which adjusted for complex sample design features used to
obtain the data. Likewise the model fitting algorithm produces
design-consistent coefficients to any specified least squares linear model
between the dependent and independent variables used in the end nodes.
The main functions return the resulting binary tree or random forest as
an object of "rpms" or "rpms_forest" type. The package also provides a number
of functions and methods available for use with these object types.

Functions to allow users to build and analyze design consistent
tree and random forest models using survey data from a complex sample
design.  The tree model algorithm can fit a linear model to survey data
in each node obtained by recursively partitioning the data.  The splitting
variables and selected splits are obtained using a randomized permutation
test procedure which adjusted for complex sample design features used to
obtain the data. Likewise the model fitting algorithm produces
design-consistent coefficients to any specified least squares linear model
between the dependent and independent variables used in the end nodes.
The main functions return the resulting binary tree or random forest as
an object of "rpms" or "rpms_forest" type. The package also provides methods
modeling a "boosted" tree or forest model and a tree model for zero-inflated
data as well as a number of functions and methods available for use with
these object types.