Bayesian estimation of spatial weight matrices in spatial
    econometric panel models. Allows for estimation of spatial
    autoregressive (SAR), spatial error (SEM), spatial Durbin (SDM),
    spatial error Durbin (SDEM) and spatially lagged explanatory variable
    (SLX) type specifications featuring an unknown spatial weight matrix.
    Methodological details are given in Krisztin and Piribauer (2022)
    <doi:10.1080/17421772.2022.2095426>.

Total Time on Test plot and routines for parameter estimation of
    any lifetime distribution implemented in R via maximum likelihood (ML) given
    a data set. It is implemented thinking on parametric survival analysis, but
    it feasible to use in parameter estimation of probability density or mass
    functions in any field. The main routines 'maxlogL' and 'maxlogLreg' are
    wrapper functions specifically developed for ML estimation. There are
    included optimization procedures such as 'nlminb' and 'optim' from base
    package, and 'DEoptim' Mullen (2011) <doi:10.18637/jss.v040.i06>. Standard
    errors are estimated with 'numDeriv' Gilbert (2011)
    <https://CRAN.R-project.org/package=numDeriv> or the option 'Hessian = TRUE'
    of 'optim' function.

Implements estimation methods for parameters of common
    distribution families. The common d, p, q, r function family for each
    distribution is enriched with the ll, e, and v counterparts, computing
    the log-likelihood, performing estimation, and calculating the asymptotic
    variance - covariance matrix, respectively. Parameter estimation is
    performed analytically whenever possible.

Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.