Interface to ‘TensorFlow’ Estimators (tfestimators)
Interface to ‘TensorFlow’ Estimators <https://…/estimators>, a high-level API that provides implementations of many different model types including linear models and deep neural networks.

MCMC Sampling from ‘TMB’ Model Object using ‘Stan’ (tmbstan)
Enables all ‘rstan’ functionality for a ‘TMB’ model object, in particular MCMC sampling and chain visualization. Sampling can be performed with or without Laplace approximation for the random effects.

Sparse Principal Component Analysis via Random Projections (SPCAvRP) (SPCAvRP)
Implements the SPCAvRP algorithm, developed and analysed in ‘Sparse principal component analysis via random projections’ Gataric, M., Wang, T. and Samworth, R. J. (2017) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.

Compact and Flexible Summaries of Data (skimr)
A simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the ‘Using skimr’ vignette and the README.

Trio Model with a Combination of Lasso and Group Lasso Regularization (TrioSGL)
Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.

Optimal Level of Significance for Regression and Other Statistical Tests (OptSig)
Calculates the optimal level of significance based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim, Jae H. and Choi, In, Choosing the Level of Significance: A Decision-Theoretic Approach (December 18, 2017), available at SSRN: <https://ssrn.com/abstract=2652773> or <doi:10.2139/ssrn.2652773>. See also Kim and Ji (2015) <doi:10.1016/j.jempfin.2015.08.006>.

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