Laplace approximations and penalized B-splines are combined
for fast Bayesian inference in latent Gaussian models. The routines can be
used to fit survival models, especially proportional hazards and promotion
time cure models (Gressani, O. and Lambert, P. (2018)
<doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also
be implemented for inference in (generalized) additive models
(Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>).
See the associated website for more information and examples.

Native R Implementation of an Efficient BLAST-Like Algorithm
c(person("Manu", "Tamminen",
email = "mavatam@utu.fi",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-5891-7653")),
person("Timothy", "Julian",
email = "tim.julian@eawag.ch",
role = c("aut"),
comment = c(ORCID = "0000-0003-1000-0306")),
person("Steven", "Schmid",
email="stevschmid@gmail.com",
role = c("aut")))
Implementation of an efficient BLAST-like sequence
comparison algorithm, written in C++11 and using
native R datatypes. Blaster is based on 'nsearch' -
Schmid et al 2018; <doi:10.1101/399782>.
Manu Tamminen [aut, cre] (<https://orcid.org/0000-0001-5891-7653>),
Timothy Julian [aut] (<https://orcid.org/0000-0003-1000-0306>),
Steven Schmid [aut]

c(person("Manu", "Tamminen",
email = "mavatam@utu.fi",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-5891-7653")),
person("Timothy", "Julian",
email = "tim.julian@eawag.ch",
role = c("aut"),
comment = c(ORCID = "0000-0003-1000-0306")),
person("Aditya", "Jeevennavar",
email="aditya.a.jeevannavar@utu.fi",
role = c("aut"),
comment = c(ORCID = "0000-0002-0737-7316")),
person("Steven", "Schmid",
email="stevschmid@gmail.com",
role = c("aut")))
Manu Tamminen [aut, cre] (<https://orcid.org/0000-0001-5891-7653>),
Timothy Julian [aut] (<https://orcid.org/0000-0003-1000-0306>),
Aditya Jeevennavar [aut] (<https://orcid.org/0000-0002-0737-7316>),
Steven Schmid [aut]

person("Rich", "Iannone", email = "riannone@me.com", role = c("aut", "cre"))
Compose and send out responsive HTML email messages that render
perfectly across a range of email clients and device sizes. Messages are
composed using 'Markdown' and a text interpolation system that allows for
the injection of evaluated R code within the message body, footer, and
subject line. Helper functions let the user insert embedded images, web
link buttons, and 'ggplot2' plot objects into the message body. Messages
can be sent through an 'SMTP' server or through the 'Mailgun' API service
<http://mailgun.com/>.