Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. In case that the survival and truncation times are quasi-independent, the estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2018) <doi:10.1111/biom.12809>, Shen (2010) <doi:10.1007/s10463-008-0192-2>, Martin and Betensky (2005) <doi:10.1198/016214504000001538>. When the survival times are dependent on at least one of the truncation times, an EM algorithm is employed to obtain point estimates for the regression coefficients. The standard errors are calculated using the bootstrap method. See Rennert and Xie (2022) <doi:10.1111/biom.13451>. Both the independent and dependent cases assume no censoring is present in the data. Please contact Lior Rennert <liorr@clemson.edu> for questions regarding function coxDT and Yidan Shi <yidan.shi@pennmedicine.upenn.edu> for questions regarding function coxDTdep.

Survey to collect data about the social and economic conditions of Indonesian society. This activity aims to include: As a data source for planning and evaluating national, sectoral development programs, and providing indicators for Sustainable Development Goals (TPB), National Medium Term Development Plan (RPJMN), and Nawacita, GDP/GRDP and annual Integrated Institutional Balance Sheet.

Implements methods for variable selection in linear
    regression based on the "Sum of Single Effects" (SuSiE) model, as
    described in Wang et al (2020) <DOI:10.1101/501114> and Zou et al
    (2021) <DOI:10.1101/2021.11.03.467167>. These methods provide
    simple summaries, called "Credible Sets", for accurately
    quantifying uncertainty in which variables should be selected.
    The methods are motivated by genetic fine-mapping applications,
    and are particularly well-suited to settings where variables are
    highly correlated and detectable effects are sparse. The fitting
    algorithm, a Bayesian analogue of stepwise selection methods
    called "Iterative Bayesian Stepwise Selection" (IBSS), is simple
    and fast, allowing the SuSiE model be fit to large data sets
    (thousands of samples and hundreds of thousands of variables).