R packages
​Visit my GitHub page here ​for a complete list of R packages.

• ​BRdac: Performs regression analysis of Brown-Resnick and inverted Brown-Resnick max-stable spatial data using a divide-and-conquer approach. Divides the spatial domain into blocks, analyses blocks using censored composite likelihood, and combines estimators using a one-step update. Extensions for spatially-varying coefficient models are included. See this paper for reference.
• DIMM: Performs distributed and integrated method of moments regression for high- dimensional correlated responses. Divides outcomes into blocks, analyses blocks using composite likelihood, and combines estimators using a one-step update or an optimal generalized method of moments (GMM) equation. See this paper for reference.
• DDIMM: Performs doubly distributed and integrated method of moments regression for high-dimensional correlated responses. Divides outcomes and subjects into blocks, analyses blocks using composite likelihood or generalized estimating equations, and combines estimators using a one-step update or an optimal GMM equation. See this paper for reference.
• ​DIQIF: Performs doubly distributed and integrated method of moments regression for high-dimensional correlated responses. Divides outcomes and subjects into blocks, analyses blocks using composite likelihood, generalized estimating equations or quadratic inference functions, and combines estimators using a one-step update or an optimal GMM equation following a pre-specified homogeneity partition index. See this paper for reference.
• fusQIF: Performs fusion of parameters in data integration with dependence. Divides outcomes according to a supplied indicator, analyzes blocks using quadratic inference functions, then fuses block estimates using the alternating direction method of multipliers and the optimal GMM equation. See this paper for reference.
• IMdac: Performs possibilistic divide-and-conquer analysis in the inferential models framework. Combines block-specific estimates using a Gaussian-based relative likelihood, then validifies the relative likelihood using the sampling distribution of the block-specific summary statistics. See this paper for reference.
• ISEDI: Estimates the parameters of a generalized linear model in a dataset by borrowing information from a prior analysis on another dataset. Estimation is based on an information-shrinkage estimator that introduces discounted units of information from the prior analysis into the current analysis. See this paper for reference.
• ​SLA: Estimates mean regression parameters for longitudinal data using an online streaming procedure. The longitudinal data are observed in sequential streams that are sequentially analyzed using a time-varying regression model. Correlation between outcomes is modeled using a first-order autoregressive process. See this paper for reference.

Julia packages

• RECaST: Estimates recalibration parameters in Cauchy random effect model for transfer learning from a source model to a target model. Includes code for comparisons to other transfer learning approaches. See this paper for reference.