Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) <doi:10.1111/j.1541-0420.2012.01824.x>. Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.

Efficient statistical inference of two-sample MR (Mendelian Randomization) analysis. 
    It can account for the correlated instruments and the horizontal pleiotropy, 
    and can provide the accurate estimates of both causal effect and horizontal 
    pleiotropy effect as well as the two corresponding p-values. There are two main 
    functions in the 'PPMR' package. One is PMR_individual() for individual level data, 
    the other is PMR_summary() for summary data.

Provides a suite of functions that fit models that use PPM type priors for partitions.
                Models include hierarchical Gaussian and probit ordinal models with  a (covariate 
                dependent) PPM.  If a covariate dependent product partition model is selected, 
                then all the options detailed in Page, G.L.; Quintana, F.A. (2018) 
                <doi:10.1007/s11222-017-9777-z> are available.  If covariate values are missing, 
                then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020) 
                <doi:10.1080/10618600.2021.1999824> is employed.   Also included in the package is 
                a function that fits a Gaussian likelihood spatial product  partition model that is 
                detailed in Page, G.L.; Quintana, F.A. (2016)  <doi:10.1214/15-BA971>, and 
                multivariate PPM change point models that are detailed in Quinlan, J.J.; Page, G.L.; 
                Castro, L.M. (2023) <doi:10.1214/22-BA1344>. In addition, a function that fits a 
                univariate or bivariate functional data model that employs a PPM or a PPMx to 
                cluster curves based on B-spline coefficients is provided.