Long Term Prediction for Epidemic and Pandemic Data
c(person("Débora", "de Freitas Magalhães", email = "deborafmag@gmail.com", role = "aut"),
person("Marta Cristina", "Colozza Bianchi da Costa", email = "marta_bianchi@ufg.br", role = "aut"),
person("Guido", "Alberti Moreira", email = "guidoalber@gmail.com", role = c("cre","aut"),comment=c(ORCID="0000-0001-7557-0874")),
person("Thais", "Pacheco Menezes", email = "thaispacheco1207@gmail.com", role = "aut"),
person("Marcos", "Oliveira Prates", email = "marcosop@est.ufmg.br", role = "ctb",comment=c(ORCID="0000-0001-8077-4898")))
Implementation of the methodology described in <http://est.ufmg.br/covidlp/home/pt/> which can be
also found in help(models). Implemented models are currently the Poisson distribution. The mean function can be the
basic generalized logistic form, or the seasonal effect which has under- or over-reporting effects in up to three
weekdays, or the two curves form. Bayesian inference is made available through the 'stan' software and its diagnostic
functions pool can be used. Plot methods are implemented to mimic the graphics from the 'shiny' app in the URL using
the 'plotly' library.
Débora de Freitas Magalhães [aut],
Marta Cristina Colozza Bianchi da Costa [aut],
Guido Alberti Moreira [cre, aut]
(<https://orcid.org/0000-0001-7557-0874>),
Thais Pacheco Menezes [aut],
Marcos Oliveira Prates [ctb] (<https://orcid.org/0000-0001-8077-4898>)

Random Effects and Sample Selection Models for Panel Counting
Data
A High Performance Package Implementing Random Effects and Sample Selection Models for Panel Counting Data.

Random Effects and/or Sample Selection Models for Panel Count
Data
A high performance package implementing random effects and/or sample selection models for panel count data.

A high performance package implementing random effects and/or
sample selection models for panel count data. The details of the models are discussed in Peng and Van den Bulte (2022) <doi:10.2139/ssrn.2702053>.

Matching Methods for Causal Inference with Time-Series
Cross-Sectional Data
c(
person("In Song", "Kim", email = "insong@mit.edu", role = c("aut", "cre")),
person("Adam", "Rauh", email = "adamrauh@mit.edu", role = c("aut")),
person("Erik", "Wang", email = "haixiaow@Princeton.edu", role = c("aut")),
person("Kosuke", "Imai", email = "imai@harvard.edu", role = c("aut"))
)
Implements a set of methodological tools
that enable researchers to apply matching methods to
time-series cross-sectional data. Imai, Kim, and Wang
(2018) <http://web.mit.edu/insong/www/pdf/tscs.pdf>
proposes a nonparametric generalization of the
difference-in-differences estimator, which does not rely
on the linearity assumption as often done in
practice. Researchers first select a method of matching
each treated observation for a given unit in a
particular time period with control observations from
other units in the same time period that have a similar
treatment and covariate history. These methods include
standard matching methods based on propensity score and
Mahalanobis distance, as well as weighting methods. Once
matching is done, both short-term and long-term average
treatment effects for the treated can be estimated with
standard errors. The package also offers a visualization
technique that allows researchers to assess the quality
of matches by examining the resulting covariate balance.
Rcpp (>= 0.12.5), data.table, ggplot2, CBPS, stats, graphics,
grDevices, MASS, Matrix, methods
In Song Kim [aut, cre],
Adam Rauh [aut],
Erik Wang [aut],
Kosuke Imai [aut]