Sat, 24 Nov 2018

Package photobiologyInOut updated to version 0.4.18 with previous version 0.4.17-1 dated 2018-09-27

Title: Read Spectral and Logged Data from Foreign Files
Description: Functions for reading, and in some cases writing, foreign files containing spectral data from spectrometers and their associated software, output from daylight simulation models in common use, and some spectral data repositories. As well as functions for exchange of spectral data with other R packages. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Author: Pedro J. Aphalo [aut, cre] (<https://orcid.org/0000-0003-3385-972X>), Titta K. Kotilainen [ctb] (<https://orcid.org/0000-0002-2822-9734>), Glenn Davis [ctb]
Maintainer: Pedro J. Aphalo <pedro.aphalo@helsinki.fi>

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Package EmpiricalCalibration updated to version 1.4.0 with previous version 1.3.6 dated 2017-11-07

Title: Routines for Performing Empirical Calibration of Observational Study Estimates
Description: Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls.
Author: Martijn Schuemie [aut, cre], Marc Suchard [aut]
Maintainer: Martijn Schuemie <schuemie@ohdsi.org>

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Package Rmagic updated to version 1.4.0 with previous version 1.3.0 dated 2018-09-21

Title: MAGIC - Markov Affinity-Based Graph Imputation of Cells
Description: MAGIC (Markov affinity-based graph imputation of cells) is a method for addressing technical noise in single-cell data, including under-sampling of mRNA molecules, often termed "dropout" which can severely obscure important gene-gene relationships. MAGIC shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. Read more: van Dijk et al. (2018) <DOI:10.1016/j.cell.2018.05.061>.
Author: David van Dijk [aut], Scott Gigante [cre] (<https://orcid.org/0000-0002-4544-2764>)
Maintainer: Scott Gigante <scott.gigante@yale.edu>

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Package RcppEigen updated to version 0.3.3.5.0 with previous version 0.3.3.4.0 dated 2018-02-07

Title: 'Rcpp' Integration for the 'Eigen' Templated Linear Algebra Library
Description: R and 'Eigen' integration using 'Rcpp'. 'Eigen' is a C++ template library for linear algebra: matrices, vectors, numerical solvers and related algorithms. It supports dense and sparse matrices on integer, floating point and complex numbers, decompositions of such matrices, and solutions of linear systems. Its performance on many algorithms is comparable with some of the best implementations based on 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header files from the 'Eigen' C++ template library (currently version 3.3.4). Thus users do not need to install 'Eigen' itself in order to use 'RcppEigen'. Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License (version 2); earlier version were licensed under the GNU LGPL version 3 or later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
Author: Douglas Bates, Dirk Eddelbuettel, Romain Francois, and Yixuan Qiu; the authors of Eigen for the included version of Eigen
Maintainer: Dirk Eddelbuettel <edd@debian.org>

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Package pbdBASE updated to version 0.4-5.2 with previous version 0.4-5.1 dated 2018-02-05

Title: Programming with Big Data -- Base Wrappers for Distributed Matrices
Description: An interface to and extensions for the 'PBLAS' and 'ScaLAPACK' numerical libraries. This enables R to utilize distributed linear algebra for codes written in the 'SPMD' fashion. This interface is deliberately low-level and mimics the style of the native libraries it wraps. For a much higher level way of managing distributed matrices, see the 'pbdDMAT' package.
Author: Drew Schmidt [aut, cre], Wei-Chen Chen [aut], George Ostrouchov [aut], Pragneshkumar Patel [aut], Ewan Higgs [ctb]
Maintainer: ORPHANED

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Package codebook updated to version 0.7.4 with previous version 0.6.3 dated 2018-08-01

Title: Automatic Codebooks from Survey Metadata Encoded in Attributes
Description: Easily automate the following tasks to describe data frames: summarise the distributions of scales and items graphically and using descriptive statistics, compute reliabilities (internal consistencies, retest, multilevel) for psychological scales, combine this information with metadata (such as item labels and labelled values) that is derived from R attributes. To do so, the package relies on 'rmarkdown' partials, so you can generate HTML, PDF, and Word documents. Codebooks are also available as tables (CSV, Excel, etc.). The metadata and codebooks are also available at your fingertips via RStudio Addins.
Author: Ruben Arslan [aut, cre]
Maintainer: Ruben Arslan <ruben.arslan@gmail.com>

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Package Funclustering updated to version 1.0.2 with previous version 1.0.1 dated 2014-01-15

Title: Functional Data Clustering
Description: This package proposes a model-based clustering algorithm for multivariate functional data. The parametric mixture model, based on the assumption of normality of the principal components resulting from a multivariate functional PCA, is estimated by an EM-like algorithm. The main advantage of the proposed algorithm is its ability to take into account the dependence among curves.
Author: Mohamed Soueidatt <mohamed.soueidatt@inria.fr>, <msoueidatt@gmail.com>*.
Maintainer: ORPHANED

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Package STPGA updated to version 5.2.1 with previous version 5.2 dated 2018-09-21

Title: Selection of Training Populations by Genetic Algorithm
Description: Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.
Author: Deniz Akdemir
Maintainer: Deniz Akdemir <deniz.akdemir.work@gmail.com>

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Package Numero updated to version 1.1.1 with previous version 1.0.3 dated 2017-12-01

Title: Statistical Framework to Define Subgroups in Complex Datasets
Description: High-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2018) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, dyy113, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
Author: Song Gao [aut], Stefan Mutter [aut], Aaron E. Casey [aut], Ville-Petteri Makinen [aut, cre]
Maintainer: Ville-Petteri Makinen <vpmakine@gmail.com>

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Package isoph updated to version 1.1.3 with previous version 1.1.2 dated 2017-12-11

Title: Isotonic Proportional Hazards Model
Description: Nonparametric estimation of an isotonic covariate effect for proportional hazards model.
Author: Yunro Chung [cre]
Maintainer: Yunro Chung <yunro.chung@asu.edu>

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Package eurostat updated to version 3.3.1 with previous version 3.2.9 dated 2018-09-20

Title: Tools for Eurostat Open Data
Description: Tools to download data from the Eurostat database <http://ec.europa.eu/eurostat> together with search and manipulation utilities.
Author: Leo Lahti [aut, cre], Janne Huovari [aut], Markus Kainu [aut], Przemyslaw Biecek [aut], Joona Lehtomaki [ctb], Francois Briatte [ctb], Oliver Reiter [ctb]
Maintainer: Leo Lahti <leo.lahti@iki.fi>

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Package stminsights updated to version 0.3.0 with previous version 0.2.2 dated 2018-08-30

Title: A 'Shiny' Application for Inspecting Structural Topic Models
Description: This app enables interactive validation, interpretation and visualization of structural topic models from the 'stm' package by Roberts and others (2014) <doi:10.1111/ajps.12103>. It also includes helper functions for model diagnostics and extracting data from effect estimates.
Author: Carsten Schwemmer [aut, cre] (<https://orcid.org/0000-0001-9084-946X>), Jonne Guyt [ctb]
Maintainer: Carsten Schwemmer <c.schwem2er@gmail.com>

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Package rpart.plot updated to version 3.0.6 with previous version 3.0.5 dated 2018-11-03

Title: Plot 'rpart' Models: An Enhanced Version of 'plot.rpart'
Description: Plot 'rpart' models. Extends plot.rpart() and text.rpart() in the 'rpart' package.
Author: Stephen Milborrow
Maintainer: Stephen Milborrow <milbo@sonic.net>

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Package ppitables updated to version 0.3.0 with previous version 0.2.0 dated 2018-05-25

Title: Lookup Tables to Generate Poverty Likelihoods and Rates using the Poverty Probability Index (PPI)
Description: The Poverty Probability Index (PPI) is a poverty measurement tool for organizations and businesses with a mission to serve the poor. The PPI is statistically-sound, yet simple to use: the answers to 10 questions about a household’s characteristics and asset ownership are scored to compute the likelihood that the household is living below the poverty line – or above by only a narrow margin. This package contains country-specific lookup data tables used as reference to determine the poverty likelihood of a household based on their score from the country-specific PPI questionnaire. These lookup tables have been extracted from documentation of the PPI found at <https://www.povertyindex.org> and managed by Innovations for Poverty Action <https://www.poverty-action.org>.
Author: Ernest Guevarra [aut, cre] (<https://orcid.org/0000-0002-4887-4415>)
Maintainer: Ernest Guevarra <ernestgmd@gmail.com>

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Package plotmo updated to version 3.5.1 with previous version 3.5.0 dated 2018-08-20

Title: Plot a Model's Residuals, Response, and Partial Dependence Plots
Description: Plot model surfaces for a wide variety of models using partial dependence plots and other techniques. Also plot model residuals and other information on the model.
Author: Stephen Milborrow
Maintainer: Stephen Milborrow <milbo@sonic.net>

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Package mcmcr updated to version 0.0.3 with previous version 0.0.2 dated 2018-10-30

Title: Manipulate MCMC Samples
Description: Functions and classes to store, manipulate and summarise Monte Carlo Markov Chain (MCMC) samples.
Author: Joe Thorley [aut, cre] (<https://orcid.org/0000-0002-7683-4592>)
Maintainer: Joe Thorley <joe@poissonconsulting.ca>

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Package grf updated to version 0.10.2 with previous version 0.10.1 dated 2018-09-24

Title: Generalized Random Forests (Beta)
Description: A pluggable package for forest-based statistical estimation and inference. GRF currently provides methods for non-parametric least-squares regression, quantile regression, and treatment effect estimation (optionally using instrumental variables). This package is currently in beta, and we expect to make continual improvements to its performance and usability.
Author: Julie Tibshirani [aut, cre], Susan Athey [aut], Stefan Wager [aut], Rina Friedberg [ctb], Luke Miner [ctb], Marvin Wright [ctb]
Maintainer: Julie Tibshirani <jtibs@cs.stanford.edu>

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 man/predict.quantile_forest.Rd                              |    4 
 man/predict.regression_forest.Rd                            |   14 -
 man/quantile_forest.Rd                                      |    9 
 man/regression_forest.Rd                                    |   17 -
 man/tune_causal_forest.Rd                                   |   10 
 man/tune_local_linear_forest.Rd                             |    5 
 man/tune_regression_forest.Rd                               |    9 
 src/InstrumentalForestBindings.cpp                          |    2 
 src/RcppExports.cpp                                         |   26 +-
 src/RegressionForestBindings.cpp                            |   35 ++
 src/src/forest/ForestPredictor.cpp                          |    3 
 src/src/forest/ForestPredictor.h                            |    1 
 src/src/forest/ForestPredictors.cpp                         |   15 -
 src/src/forest/ForestPredictors.h                           |    7 
 src/src/prediction/CustomPredictionStrategy.cpp             |    9 
 src/src/prediction/CustomPredictionStrategy.h               |   10 
 src/src/prediction/DefaultPredictionStrategy.h              |   58 +++-
 src/src/prediction/InstrumentalPredictionStrategy.cpp       |    4 
 src/src/prediction/LocalLinearPredictionStrategy.cpp        |  149 +++++++++++-
 src/src/prediction/LocalLinearPredictionStrategy.h          |   16 +
 src/src/prediction/QuantilePredictionStrategy.cpp           |   10 
 src/src/prediction/QuantilePredictionStrategy.h             |    9 
 src/src/prediction/collector/DefaultPredictionCollector.cpp |   36 ++
 src/src/prediction/collector/DefaultPredictionCollector.h   |    6 
 src/src/sampling/RandomSampler.cpp                          |   12 
 49 files changed, 708 insertions(+), 317 deletions(-)

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