An RStudio addin for teaching and learning data manipulation using the 'dplyr' package.
    You can learn each steps of data manipulation by clicking your mouse without coding.
    You can get resultant data (as a 'tibble') and the code for data manipulation.

Implements the dynamic panel models described by Allison, Williams,
 and Moral-Benito (2017 <doi:10.1177/2378023117710578>) in R. 
 This class of models uses structural equation modeling to specify dynamic 
 (lagged dependent variable) models with fixed effects for panel data. 
 Additionally, models may have predictors that are only weakly exogenous, i.e.,
 are affected by prior values of the dependent variable. Options also allow for
 random effects, dropping the lagged dependent variable, and a number of other 
 specification choices.

Create, install, and summarise data packages that follow
    the Open Knowledge Foundation's Data Package Protocol.

This MCMC method takes a data numeric vector (Y) and assigns the elements of Y
  to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred.
  Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.

An implementation of common statistical analysis and models with
    differential privacy (Dwork et al., 2006a) <doi:10.1007/11681878_14>
    guarantees. The package contains, for example, functions providing
    differentially private computations of mean, variance, median, histograms,
    and contingency tables. It also implements some statistical models and
    machine learning algorithms such as linear regression (Kifer et al., 2012)
    <https://proceedings.mlr.press/v23/kifer12.html>
    and SVM (Chaudhuri et al., 2011)
    <https://jmlr.org/papers/v12/chaudhuri11a.html>. In addition, it implements
    some popular randomization mechanisms, including
    the Laplace mechanism (Dwork et al., 2006a)
    <doi:10.1007/11681878_14>, Gaussian mechanism (Dwork et al., 2006b)
    <doi:10.1007/11761679_29>, analytic Gaussian mechanism (Balle & Wang, 2018)
    <https://proceedings.mlr.press/v80/balle18a.html>, and exponential mechanism
    (McSherry & Talwar, 2007) <doi:10.1109/FOCS.2007.66>.