Can be used to simultaneously estimate networks (Gaussian Graphical Models) in data from different groups or classes via Joint Graphical Lasso. Tuning parameters are selected via information criteria (AIC / BIC / extended BIC) or cross validation.

Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial Durbin (SDM), 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.

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>.

Extension of 'testthat' package to make unit tests on empirical distributions of estimators and functions for diagnostics of their finite-sample performance.

Estimation of four-fold table cell frequencies (raw data) from risk ratios (relative risks), risk differences and odds ratios. While raw data can be useful for doing meta-analysis, such data is often not provided by primary studies (with summary statistics being solely presented). Therefore, based on summary statistics (namely, risk ratios, risk differences and odds ratios), this package estimates the value of each cell in a 2x2 table according to the equations described in Di Pietrantonj C (2006) <doi:10.1002/sim.2287>.