Functions, data sets and examples for the book: Yves Croissant (2025) "Microeconometrics with R", Chapman and Hall/CRC The R Series <doi:10.1201/9781003100263>. 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>.

Contains functions for converting existing HTML/JavaScript
   source into equivalent 'shiny' functions. Bootstraps the process of making
   new 'shiny' functions by allowing us to turn HTML snippets directly into 
   R functions.

The rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging, which necessitates innovative, integrated, and efficient trial designs(Yuan, Y., et al. (2016) <doi:10.1002/sim.6971>). MIDAS-2 package enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We used a regression model to characterize the efficacy pattern in subgroups. Information borrowing was applied through Bayesian hierarchical model to improve trial efficiency considering the limited sample size in subgroups(Cunanan, K. M., et al. (2019) <doi:10.1177/1740774518812779>). MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion(Wathen, J. K., & Thall, P. F. (2017) <doi: 10.1177/1740774517692302>).