parglm - Parallel GLM

Provides a parallel estimation method for generalized linear models without compiling with a multithreaded LAPACK or BLAS.

Last updated 3 years ago

generalized-linear-modelsparallel-computing

6.38 score 11 stars 4 packages 36 scripts 655 downloads

DtD - Distance to Default

Provides fast methods to work with Merton's distance to default model introduced in Merton (1974) <doi:10.1111/j.1540-6261.1974.tb03058.x>. The methods includes simulation and estimation of the parameters.

Last updated 4 years ago

blackscholesdefaultfinance

5.09 score 5 stars 49 scripts 205 downloads

psqn - Partially Separable Quasi-Newton

Provides quasi-Newton methods to minimize partially separable functions. The methods are largely described by Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.

Last updated 2 months ago

optimizationoptimization-algorithmsquasi-newton

5.08 score 2 stars 2 packages 5 scripts 333 downloads

SimSurvNMarker - Simulate Survival Time and Markers

Provides functions to simulate from joint survival and marker models. The user can specific all basis functions of time, random or deterministic covariates, random or deterministic left-truncation and right-censoring times, and model parameters.

Last updated 2 years ago

simulationsurvival-analysis

4.81 score 3 stars 43 scripts 192 downloads

mmcif - Mixed Multivariate Cumulative Incidence Functions

Fits the mixed cumulative incidence functions model suggested by <doi:10.1093/biostatistics/kxx072> which decomposes within cluster dependence of risk and timing. The estimation method supports computation in parallel using a shared memory C++ implementation. A sandwich estimator of the covariance matrix is available. Natural cubic splines are used to provide a flexible model for the cumulative incidence functions.

Last updated 2 years ago

competing-riskcomposite-likelihoodmixed-modelssandwich-estimatorsurvival-analysis

4.00 score 10 scripts 146 downloads

mdgc - Missing Data Imputation Using Gaussian Copulas

Provides functions to impute missing values using Gaussian copulas for mixed data types as described by Christoffersen et al. (2021) <arXiv:2102.02642>. The method is related to Hoff (2007) <doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845> but differs by making a direct approximation of the log marginal likelihood using an extended version of the Fortran code created by Genz and Bretz (2002) <doi:10.1198/106186002394> in addition to also support multinomial variables.

Last updated 2 years ago

binarygaussian-copulaimputationmultinomial-variablesordinalsemi-parametric

3.78 score 10 stars 12 scripts 216 downloads