**Annuity Random Interest Rates** (**AnnuityRIR**)

Annuity Random Interest Rates proposes different techniques for the approximation of the present and final value of a unitary annuity-due or annuity-immediate considering interest rate as a random variable. Cruz Rambaud et al. (2017) <doi:10.1007/978-3-319-54819-7_16>. Cruz Rambaud et al. (2015) <doi:10.23755/rm.v28i1.25>.

**SPEW Framework for Generating Synthetic Ecosystems** (**spew**)

Tools for generating synthetic synthetic using the SPEW (Synthetic Populations and Ecosystems of the World) framework. We provide functions for the ‘synthesis’ step of the SPEW, which converts harmonized data into a synthetic ecosystem. We also provide functions for visualizing and summarizing synthetic ecosystems generated by SPEW. For details see Gallagher, S., Richardson, L.F., Ventura, S.L., Eddy W.F. (2017) <arXiv:1701.02383>.

**Query-Driven Pipeline Execution and Query Templates** (**condusco**)

Runs a function iteratively over each row of either a dataframe or the results of a query. Use the ‘BigQuery’ and ‘DBI’ wrappers to iteratively pass each row of query results to a function. If a field contains a ‘JSON’ string, it will be converted to an object. This is helpful for queries that return ‘JSON’ strings that represent objects. These fields can then be treated as objects by the pipeline.

**Information Matrix for Diagnostic Classification Models** (**dcminfo**)

A set of asymptotic methods that can be used to directly estimate the expected (Fisher) information matrix by Liu, Tian, and Xin (2016) <doi:10.3102/1076998615621293> in diagnostic classification models or cognitive diagnostic models are provided when marginal maximum likelihood estimation is used. For these methods, both the item and structural model parameters are considered simultaneously. Specifically, the observed information matrix, the empirical cross-product information matrix and the sandwich-type co-variance matrix that can be used to estimate the asymptotic co-variance matrix (or the model parameter standard errors) within the context of diagnostic classification models are provided.

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