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adcomp

AD computation with Template Model Builder (TMB)

backblaze_survival_analysis_prep

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

causaloptim

An Interface to Specify Causal Graphs and Compute Balke Bounds

cfaad

comp-ex

dtd

R package with fast methods for Merton's distance-to-default model

dynamichazard

R package for survival analysis with state space models

extreme_asymptotic_demo

fastncdf

Fast Computation of Normal CDF

fska

ghq-cpp

grim

lbfgs

Efficient L-BFGS and OWL-QN Optimization in R

mdgc

Provides functions to impute missing values using Gaussian copulas for mixed data types.

mixprobit

mmcif

Fits the mixed cumulative incidence function models suggested by Cederkvist et al (2018; https://doi.org/10.1093%2Fbiostatistics%2Fkxx072) which decomposes within cluster dependence of risk and timing.

mssm

R package for multivariate state space models

oumuamua

Parallel implementation of the Multivariate Adaptive Regression Splines algorithm

parglm

R package that provides a parallel estimation method for generalized linear models

pedmod

R package with quasi-Monte Carlo methods to estimate mixed models commonly used for random effect structures from pedigrees.

pre

R package for deriving Prediction Rule Ensembles

psqn

Provides quasi-Newton methods to minimize partially separable functions. The package includes both a header-only C++ interface and a R interface.

psqn-va-ex

rcpp

Seamless R and C++ Integration

rcpp-gallery

Source code for the Rcpp Gallery website

rollregres

R package for fast rolling and expanding linear regression models

rstpm2

An R package for generalised survival models

rtorchcpp

simsurvnmarker

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.