Improved Predictors

Improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.

Mixed FLP and ML Estimation of ETAS Space-Time Point Processes for Earthquake Description

Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; in version 2.3.0 improved update method. Chiodi, Adelfio (2017).

Helpers for Model Coefficients Tibbles

Provides suite of functions to work with regression model 'broom::tidy()' tibbles. The suite includes functions to group regression model terms by variable, insert reference and header rows for categorical variables, add variable labels, and more.

Derivative-Free Optimization Algorithms by Quadratic Approximation

Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell.

"Finding Groups in Data": Cluster Analysis Extended Rousseeuw et al.

Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".

SQLite Interface for R

Embeds the SQLite database engine in R and provides an interface compliant with the DBI package. The source for the SQLite engine (version 3.51.2) and for various extensions is included. System libraries will never be consulted because this package relies on static linking for the plugins it includes; this also ensures a consistent experience across all installations.

Detection of Univariate Outliers

Provides well-known techniques for detecting univariate outliers. Methods for handling skewed distributions are included. The Hidiroglou-Berthelot (1986) method for detecting outliers in ratios of historical data is also implemented. When available, survey weights can be incorporated in the detection process.

Parametric Mortality Models, Life Tables and HMD

Fit the most popular human mortality 'laws', and construct full and abridge life tables given various input indices. A mortality law is a parametric function that describes the dying-out process of individuals in a population during a significant portion of their life spans. For a comprehensive review of the most important mortality laws see Tabeau (2001) . Practical functions for downloading data from various human mortality databases are provided as well.

Robust Data-Driven Statistical Inference in Regression-Discontinuity Designs

Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).

Analysis Results Data

Construct CDISC (Clinical Data Interchange Standards Consortium) compliant Analysis Results Data objects. These objects are used and re-used to construct summary tables, visualizations, and written reports. The package also exports utilities for working with these objects and creating new Analysis Results Data objects.