A generalization of the 'Synth' package that is designed for
    data at a more granular level (e.g., micro-level). Provides functions
    to construct weights (including propensity score-type weights) and run
    analyses for synthetic control methods with micro- and meso-level
    data; see Robbins, Saunders, and Kilmer (2017)
    <doi:10.1080/01621459.2016.1213634> and Robbins and Davenport (2021)
    <doi:10.18637/jss.v097.i02>.

This toolkit allows performing continuous-time microsimulation for a wide range of life science (demography, social sciences, epidemiology) applications. Individual life-courses are specified by a continuous-time multi-state model as described in Zinn (2014) <doi:10.34196/IJM.00105>.

Providing C implementation for the computing of monotonic spline bases, including M-splines, I-splines, and C-splines, denoted by MIC splines. The definitions of the spline bases are described in Meyer (2008) <doi: 10.1214/08-AOAS167>. The package also provides the computing of constrained least-squares estimates when a subset of or all of the regression coefficients are constrained to be non-negative.

Functions, data sets and examples for the book: Yves Croissant (2024) "Microeconometrics with R", Chapman and Hall/CRC The R Series. The package includes a set of estimators for models used in microeconometrics, especially for count data and limited dependent variables. Test functions include score test, Hausman test, Vuong test, Sargan test and conditional moment test. A small subset of the data set used in the book is also included.

Companion package of Carrion-i-Silvestre & Sansó (2023): 
  "Generalized Extreme Value Approximation to the CUMSUMQ Test for Constant 
  Unconditional Variance in Heavy-Tailed Time Series". It implements the Modified 
  Iterative Cumulative Sum of Squares Algorithm, which is an extension of 
  the Iterative Cumulative Sum of Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in the 
  unconditional variance of a time series controlling for the tail index of 
  the underlying distribution. The fourth order moment is estimated non-parametrically
  to avoid the size problems when the innovations are non-Gaussian (see, Sansó et al., 2004). 
  Critical values and p-values are generated using a Generalized Extreme Value distribution approach.
  References
  Carrion-i-Silvestre J.J & Sansó A (2023) <https://www.ub.edu/irea/working_papers/2023/202309.pdf>.
  Inclan C & Tiao G.C (1994) <doi:10.1080/01621459.1994.10476824>,
  Sansó A & Aragó V & Carrion-i-Silvestre J.L (2004) <https://dspace.uib.es/xmlui/bitstream/handle/11201/152078/524035.pdf>.