Provides functions and data files to help CE Public-Use
    Microdata (PUMD) users calculate annual estimated expenditure means,
    standard errors, and quantiles according to the methods used by the CE with
    PUMD. For more information on the CE please visit <https://www.bls.gov/cex>.
    For further reading on CE estimate calculations please see the CE
    Calculation section of the U.S. Bureau of Labor Statistics (BLS) Handbook of
    Methods at <https://www.bls.gov/opub/hom/cex/calculation.htm>. For
    further information about CE PUMD please visit
    <https://www.bls.gov/cex/pumd.htm>.

Perform censored quantile regression of Huang (2010) <doi:10.1214/09-AOS771>, and restore monotonicity respecting via adaptive interpolation for dynamic regression of Huang (2017) <doi:10.1080/01621459.2016.1149070>. The monotonicity-respecting restoration applies to general dynamic regression models including (uncensored or censored) quantile regression model, additive hazards model, and dynamic survival models of Peng and Huang (2007) <doi:10.1093/biomet/asm058>, among others.

Download imagery tiles to a standard cache and load the data into raster objects. 
 Facilities for 'AWS' terrain <https://registry.opendata.aws/terrain-tiles/> terrain and 'Mapbox' 
 <https://www.mapbox.com/> servers are provided.

Estimates sugar beet canopy closure with remotely sensed leaf area
    index and estimates when action might be needed to protect the crop from a 
    Leaf Spot epidemic with a negative prognosis model based on published 
    models.

The 'vctrs' package provides a concept of vector prototype
    that can be especially useful when deploying models and code.  Serialize 
    these object prototypes to 'JSON' so they can be used to check and coerce 
    data in production systems, and deserialize 'JSON' back to the correct 
    object prototypes.

Fits a Causal Effect Random Forest of Interaction Tress (CERFIT) which is a modification of the Random Forest algorithm where each split is chosen to maximize subgroup treatment heterogeneity. Doing this allows it to estimate the individualized treatment effect for each observation in either randomized controlled trial (RCT) or observational data. For more information see L. Li, R. A. Levine, and J. Fan (2022) <doi:10.1002/sta4.457>.