Provides a suite of functions that fit models that use PPM type priors for partitions.
Models include hierarchical Gaussian and probit ordinal models with  a (covariate
dependent) PPM.  If a covariate dependent product partition model is selected,
then all the options detailed in Page, G.L.; Quintana, F.A. (2018)
<doi:10.1007/s11222-017-9777-z> are available.  If covariate values are missing,
then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020)
<doi:10.1080/10618600.2021.1999824> is employed.  Also included in the package is
a function that fits a Gaussian likelihood spatial product  partition model that is
detailed in Page, G.L.; Quintana, F.A. (2016)  <doi:10.1214/15-BA971>, and
multivariate PPM change point models that are detailed in Quinlan, J.J.; Page, G.L.;
Castro, L.M. (2021) <arXiv:2201.07830>.

Read Census Privacy Protected Microdata Files
Implements data processing described in <doi:10.1126/sciadv.abk3283> to align modern differentially private data with formatting of older US Census data releases. The primary goal is to read in Census Privacy Protected Microdata Files data in a reproducible way. This includes tools for aggregating to relevant levels of geography by creating geographic identifiers which match the US Census Bureau's numbering. Additionally, there are tools for grouping race numeric identifiers into categories, consistent with OMB (Office of Management and Budget) classifications. Functions exist for downloading and linking to existing sources of privacy protected microdata.
censable, dplyr, magrittr, readr, rlang (>= 0.4.11), stringr,
tibble, tidyr, zip

PPM-LASSO: Point process models with LASSO penalties
This package contains tools to fit point process models with sequences of LASSO penalties ("regularisation paths"). Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.