Methods for Partial Linear Single Index Model
Shunyao Wu, Qi Zhang, Zhiruo Li, Hua Liang
Estimation, hypothesis tests, and variable selection in partially linear single-index models. Please see H. (2010) at <doi:10.1214/10-AOS835> for more details.
Rcpp (>= 0.12.11), crayon, methods, stats

Rcpp (>= 0.12.11), crayon, methods, stats, purrr

Estimate (Generalized) Linear Mixed Models with Factor
Structures
c(person("Minjeong", "Jeon", role = "aut"),
person("Nicholas", "Rockwood", email = "rockwood.19@osu.edu", role = c("aut", "cre")))
Utilizes the 'lme4' package and the optim() function from 'stats'
to estimate (generalized) linear mixed models (GLMM) with factor
structures using a profile likelihood approach, as outlined in
Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628>.
Factor analysis and item response models can be extended to allow
for an arbitrary number of nested and crossed random effects,
making it useful for multilevel and cross-classified models.
lme4, Matrix (>= 1.1.1), numDeriv, stats
Minjeong Jeon [aut],
Nicholas Rockwood [aut, cre]

c(person("Minjeong", "Jeon", role = "aut"),
person("Nicholas", "Rockwood", email = "nrockwood@llu.edu", role = c("aut", "cre")))
Utilizes the 'lme4' and 'optimx' packages (previously the optim()
function from 'stats') to estimate (generalized) linear mixed models (GLMM)
with factor structures using a profile likelihood approach, as outlined in
Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and
Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>.
Factor analysis and item response models can be extended to allow
for an arbitrary number of nested and crossed random effects,
making it useful for multilevel and cross-classified models.
lme4, Matrix (>= 1.1.1), numDeriv, stats, optimx

c(person("Minjeong", "Jeon", role = "aut"),
person("Nicholas", "Rockwood", email = "njrockwood@gmail.com", role = c("aut", "cre")))

Multivariate Ordered Probit Model via Pairwise Likelihood
Euloge Clovis Kenne Pagui [aut,cre], Antonio Canale [aut], Alan Genz [ctb],  Adelchi Azzalini [ctb]
Euloge Clovis Kenne Pagui <kenne@stat.unipd.it>
Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likehood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function  is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.