Title: Congruent Rate Analyses in Birth-Death Scenarios

Description: R functions for cosmological research.
The main functions are similar to the python library, cosmolopy.
References: [H99] Hogg 1999, http://arxiv.org/abs/astro-ph/9905116

Title: RStudio Addin for Searching Packages in CRAN Database Based on
person("Becca", "Krouse", email = "rebecca_krouse@rhoworld.com", role = c("aut")),
person("Agustin", "Calatroni", email = "agustin_calatroni@rhoworld.com", role = c("cre","aut"))
Description: One of the strengths of R is its vast package ecosystem. Indeed, R packages extend from visualization to Bayesian inference and from spatial analyses to pharmacokinetics (<https://cran.r-project.org/web/views/>). There is probably not an area of quantitative research that isn't represented by at least one R package. At the time of this writing, there are more than 10,000 active CRAN packages. Because of this massive ecosystem, it is important to have tools to search and learn about packages related to your personal R needs. For this reason, we developed an RStudio addin capable of searching available CRAN packages directly within RStudio.
Imports: dplyr, curl, shiny, shinyjs, miniUI, stringr, DT, lubridate,

Title: Interpretable Subgroups Identification Through Ensemble Learning
person("Naeem", "Khoshnevis", , "nkhoshnevis@g.harvard.edu", role = c("aut", "cre"),
person("Daniela Maria", "Garcia", , "danielagarcia@college.harvard.edu", role = "aut",
person("Riccardo", "Cadei", , "rcadei@hsph.harvard.edu", role = "aut",
person("Kwonsang", "Lee", , "kwonsanglee.stat@gmail.com", role = "aut",
person("Falco Joannes", "Bargagli Stoffi", , "fbargaglistoffi@hsph.harvard.edu", role = "aut",
Description: Provides an interpretable identification of subgroups with
heterogeneous causal effect. The heterogeneous subgroups are
discovered through ensemble learning of causal rules. Causal rules are
highly interpretable if-then statement that recursively partition the
features space into heterogeneous subgroups. A small number of
significant causal rules are selected through Stability Selection to
control for family-wise error rate in the finite sample setting. It
proposes various estimation methods for the conditional causal effects
for each discovered causal rule.  It is highly flexible and multiple
causal estimands and imputation methods are implemented.  Lee, K.,
Bargagli-Stoffi, F. J., & Dominici, F. (2020). Causal rule ensemble:
Interpretable inference of heterogeneous treatment effects.  arXiv
Imports: MASS, stats, logger, gbm, randomForest, methods, xgboost, RRF,
data.table, xtable, glmnet, bartCause, stabs, stringr,
SuperLearner, dplyr, magrittr, ggplot2, bcf, inTrees
Suggests: baggr, grf, BART, gnm, covr, knitr, rmarkdown, testthat (>=
Author: Naeem Khoshnevis [aut, cre] (<https://orcid.org/0000-0003-4315-1426>,
Daniela Maria Garcia [aut] (<https://orcid.org/0000-0003-3226-3561>),