Fri, 06 Apr 2018

Package arules updated to version 1.6-1 with previous version 1.6-0 dated 2018-03-06

Title: Mining Association Rules and Frequent Itemsets
Description: Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat.
Author: Michael Hahsler [aut, cre, cph], Christian Buchta [aut, cph], Bettina Gruen [aut, cph], Kurt Hornik [aut, cph], Ian Johnson [ctb, cph], Christian Borgelt [ctb, cph]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>

Diff between arules versions 1.6-0 dated 2018-03-06 and 1.6-1 dated 2018-04-06

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 arules-1.6-1/arules/MD5                                    |   56 
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 arules-1.6-1/arules/R/discretize.R                         |    3 
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 arules-1.6-1/arules/R/itemsets.R                           |    3 
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Package hbmem updated to version 0.3-3 with previous version 0.3 dated 2012-12-20

Title: Hierarchical Bayesian Analysis of Recognition Memory
Description: Contains functions for fitting hierarchical versions of EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and our gamma signal detection model to recognition memory confidence-ratings data.
Author: Michael S. Pratte
Maintainer: Mike Pratte <prattems@gmail.com>

Diff between hbmem versions 0.3 dated 2012-12-20 and 0.3-3 dated 2018-04-06

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Package sommer updated to version 3.4 with previous version 3.3 dated 2018-03-05

Title: Solving Mixed Model Equations in R
Description: Structural multivariate-univariate linear mixed model solver for multiple random effects allowing the specification of variance-covariance structures for random effects and allowing the fit of heterogeneous variance models (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>). ML/REML estimates can be obtained using the Direct-Inversion Newton-Raphson, and Efficient Mixed Model Association algorithms. Designed for genomic prediction and genome wide association studies (GWAS), particularly focused in the p > n problem (more coefficients than observations) to include multiple relationship matrices or other covariance structures. Spatial models can be fitted using the two-dimensional spline functionality in sommer.
Author: Giovanny Covarrubias-Pazaran
Maintainer: Giovanny Covarrubias-Pazaran <cova_ruber@live.com.mx>

Diff between sommer versions 3.3 dated 2018-03-05 and 3.4 dated 2018-04-06

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Package mdsOpt updated to version 0.3-1 with previous version 0.2-2 dated 2017-12-11

Title: Searching for Optimal MDS Procedure for Metric and Interval-Valued Symbolic Data
Description: Selecting the optimal multidimensional scaling (MDS) procedure for metric data via metric MDS (ratio, interval, mspline) and nonmetric MDS (ordinal). Selecting the optimal multidimensional scaling (MDS) procedure for interval-valued symbolic data via metric MDS (ratio, interval, mspline). Selecting the optimal MDS procedure for statistical data referring to the evaluation of tourist attractiveness of Lower Silesian counties. (Borg, I., Groenen, P.J.F., Mair, P. (2013) <doi:10.1007/978-3-642-31848-1>, De Leeuw, J., Mair, P. (2015) <doi:10.1002/9781118445112.stat06268.pub2>, Groenen, P.J.F., Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Walesiak, M. (2016) <doi:10.15611/ekt.2016.2.01>, Walesiak, M. (2017) <doi:10.15611/ekt.2017.3.01>).
Author: Marek Walesiak <marek.walesiak@ue.wroc.pl>, Andrzej Dudek <andrzej.dudek@ue.wroc.pl>
Maintainer: Andrzej Dudek <andrzej.dudek@ue.wroc.pl>

Diff between mdsOpt versions 0.2-2 dated 2017-12-11 and 0.3-1 dated 2018-04-06

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New package dggridR with initial version 2.0.3
Package: dggridR
Type: Package
Title: Discrete Global Grids
Version: 2.0.3
Date: 2018-04-04
Author: Richard Barnes [aut, cre], Kevin Sahr [cph], Gerald Evenden [cph], Angus Johnson [cph], Frank Warmerdam [cph]
Maintainer: Richard Barnes <rbarnes@umn.edu>
NeedsCompilation: yes
Depends: R (>= 3.4.0), rgdal (>= 1.1), ggplot2 (>= 2.1), dplyr (>= 0.4), sp (>= 1.2)
Suggests: knitr, rmarkdown, maps, mapproj, R.rsp, testthat
License: MIT + file LICENCE
Authors@R: c(person("Richard", "Barnes", role = c("aut", "cre"), email = "rbarnes@umn.edu"), person("Kevin", "Sahr", role = "cph", email = "sahrk@sou.edu"), person("Gerald", "Evenden", role = "cph"), person("Angus", "Johnson", role = "cph"), person("Frank", "Warmerdam", role = "cph"))
Description: Spatial analyses involving binning require that every bin have the same area, but this is impossible using a rectangular grid laid over the Earth or over any projection of the Earth. Discrete global grids use hexagons, triangles, and diamonds to overcome this issue, overlaying the Earth with equally-sized bins. This package provides utilities for working with discrete global grids, along with utilities to aid in plotting such data.
URL: https://github.com/r-barnes/dggridR/
BugReports: https://github.com/r-barnes/dggridR/
Imports: Rcpp (>= 0.12.12), methods (>= 3.4.0)
LinkingTo: Rcpp
RcppModules: dgfuncs, gridgens, gridstats
RoxygenNote: 6.0.1
VignetteBuilder: knitr, R.rsp
SystemRequirements: C++11
Packaged: 2018-04-05 18:33:40 UTC; rick
X-CRAN-Comment: Archived on 2018-04-01 as issues were not corrected despite reminders. Incompatible with current rgdal (reported by its maintainer and CRAN).
Repository: CRAN
Date/Publication: 2018-04-06 20:58:39 UTC

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Package costat updated to version 2.4 with previous version 2.3 dated 2013-10-14

Title: Time Series Costationarity Determination
Description: Contains functions that can determine whether a time series is second-order stationary or not (and hence evidence for locally stationarity). Given two non-stationary series (i.e. locally stationary series) this package can then discover time-varying linear combinations that are second-order stationary.
Author: Guy Nason [aut, cre], Alessandro Cardinali [aut, ctb]
Maintainer: Guy Nason <g.p.nason@bristol.ac.uk>

Diff between costat versions 2.3 dated 2013-10-14 and 2.4 dated 2018-04-06

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 4 files changed, 24 insertions(+), 10 deletions(-)

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Package stepR updated to version 2.0-2 with previous version 2.0-1 dated 2017-05-19

Title: Multiscale Change-Point Inference
Description: Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.
Author: Pein Florian [aut, cre], Thomas Hotz [aut], Hannes Sieling [aut], Timo Aspelmeier [ctb]
Maintainer: Pein Florian <fpein@uni-goettingen.de>

Diff between stepR versions 2.0-1 dated 2017-05-19 and 2.0-2 dated 2018-04-06

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New package pimeta with initial version 1.0.0
Package: pimeta
Type: Package
Title: Prediction Intervals for Random-Effects Meta-Analysis
Version: 1.0.0
Authors@R: c( person("Kengo", "Nagashima", email = "nshi1201@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4529-9045")), person("Hisashi", "Noma", role = c("aut")), person("Toshi A.", "Furukawa", role = c("aut")))
Description: An implementation of prediction intervals for random-effects meta-analysis: Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley (2017) <doi:10.1002/sim.7140>, and Nagashima et al. (2018) <arXiv:1804.01054>.
Depends: R (>= 3.0.0)
Imports: stats, Rcpp (>= 0.12.16)
LinkingTo: Rcpp, RcppEigen
License: GPL-3
RoxygenNote: 6.0.1
NeedsCompilation: yes
Packaged: 2018-04-06 14:21:04 UTC; NAGASHIMA
Author: Kengo Nagashima [aut, cre] (<https://orcid.org/0000-0003-4529-9045>), Hisashi Noma [aut], Toshi A. Furukawa [aut]
Maintainer: Kengo Nagashima <nshi1201@gmail.com>
Repository: CRAN
Date/Publication: 2018-04-06 16:38:08 UTC

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Package netgwas updated to version 0.1.4.2 with previous version 0.1.3.2 dated 2018-03-13

Title: Network-Based Genome Wide Association Studies
Description: A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. For this purpose, we use conditional (in)dependence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <arXiv:1710.00894> and Behrouzi and Wit (2017) <arXiv:1710.01063> .
Author: Pariya Behrouzi and Ernst C. Wit
Maintainer: Pariya Behrouzi <pariya.behrouzi@gmail.com>

Diff between netgwas versions 0.1.3.2 dated 2018-03-13 and 0.1.4.2 dated 2018-04-06

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New package konfound with initial version 0.1.0
Type: Package
Package: konfound
Title: Quantify the Robustness of Causal Inferences
Version: 0.1.0
Authors@R: c( person(c("Joshua", "M"), "Rosenberg", role = c("aut", "cre"), email = "jmichaelrosenberg@gmail.com"), person(c("Ran"), "Xu", role = "ctb", email = "ranxu@msu.edu"), person(c("Kenneth", "A"), "Frank", role = "ctb", email = "kenfrank@msu.edu") )
Description: Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al. (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with null hypothesis cases to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user's own model, a single published study and multiple studies respectively.
License: MIT + file LICENSE
Imports: broom, dplyr, ggplot2, margins, pbkrtest, purrr, rlang, tidyr
Suggests: devtools, forcats, knitr, lme4, rmarkdown, roxygen2, testthat
VignetteBuilder: knitr
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-04-06 13:01:24 UTC; joshuarosenberg
Author: Joshua M Rosenberg [aut, cre], Ran Xu [ctb], Kenneth A Frank [ctb]
Maintainer: Joshua M Rosenberg <jmichaelrosenberg@gmail.com>
Repository: CRAN
Date/Publication: 2018-04-06 16:12:41 UTC

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New package GMDH2 with initial version 1.0
Package: GMDH2
Type: Package
Title: Binary Classification via GMDH-Type Neural Network Algorithm
Version: 1.0
Date: 2018-04-06
Depends: R (>= 2.15.0)
Imports: e1071, utils, graphics, stats, randomForest, glmnet, nnet, MASS, plotly, magrittr, xtable
Suggests: mlbench
Author: Osman Dag [aut, cre], Erdem Karabulut [aut], Reha Alpar [aut]
Maintainer: Osman Dag <osman.dag@hacettepe.edu.tr>
Description: Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithm. Also, it produces a well-formatted table of descriptives for a binary response. Moreover, it returns confusion matrix and related statistics and scatter plot with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response. See Dag O. and Yozgatligil C. (2016, ISSN:2073-4859) and Kondo T. and Ueno J. (2016, ISSN:1349-4198) for the details of GMDH algorithms.
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2018-04-06 15:56:00 UTC; osmandag
Repository: CRAN
Date/Publication: 2018-04-06 16:40:28 UTC

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Package eply updated to version 0.1.2 with previous version 0.1.0 dated 2017-01-27

Title: Apply a Function Over Expressions
Description: Evaluate a function over a data frame of expressions.
Author: William Michael Landau [aut, cre], Eli Lilly and Company [cph]
Maintainer: William Michael Landau <will.landau@gmail.com>

Diff between eply versions 0.1.0 dated 2017-01-27 and 0.1.2 dated 2018-04-06

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Package rtop updated to version 0.5-14 with previous version 0.5-13 dated 2018-01-12

Title: Interpolation of Data with Variable Spatial Support
Description: Geostatistical interpolation of data with irregular spatial support such as runoff related data or data from administrative units.
Author: Jon Olav Skoien
Maintainer: Jon Olav Skoien <jon.skoien@gmail.com>

Diff between rtop versions 0.5-13 dated 2018-01-12 and 0.5-14 dated 2018-04-06

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Package treeman updated to version 1.1.2 with previous version 1.1.1 dated 2017-06-27

Title: Phylogenetic Tree Manipulation Class and Methods
Description: S4 class and methods for intuitive and efficient phylogenetic tree manipulation.
Author: D.J. Bennett
Maintainer: D.J. Bennett <dominic.john.bennett@gmail.com>

Diff between treeman versions 1.1.1 dated 2017-06-27 and 1.1.2 dated 2018-04-06

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New package SmartEDA with initial version 0.1.0
Package: SmartEDA
Type: Package
Title: Summarize and Explore the Data
Version: 0.1.0
Author: Dayanand Ubrangala; Kiran R; Ravi Prasad Kondapalli
Maintainer: Dayanand Ubrangala<daya6489@gmail.com>
Depends: R (>= 3.3.0)
Imports: ggplot2,gridExtra,scales,utils,rmarkdown,ISLR
Description: Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.
License: GPL (>= 2)
Suggests: psych, Hmisc, smbinning,testthat,knitr
Encoding: UTF-8
LazyData: true
Repository: CRAN
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-04-06 07:06:09 UTC; dubrangala
Date/Publication: 2018-04-06 12:51:58 UTC

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Package skynet updated to version 1.0.1 with previous version 1.0 dated 2018-01-29

Title: Generates Networks from BTS Data
Description: A flexible tool that allows generating bespoke air transport statistics for urban studies based on publicly available data from the Bureau of Transport Statistics (BTS) in the United States <https://www.transtats.bts.gov/databases.asp?Mode_ID=1&Mode_Desc=Aviation&Subject_ID2=0>.
Author: Filipe Teixeira [aut, cre]
Maintainer: Filipe Teixeira <Filipe.MarquesTeixeira@Ugent.be>

Diff between skynet versions 1.0 dated 2018-01-29 and 1.0.1 dated 2018-04-06

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New package sealr with initial version 0.1.0
Package: sealr
Title: Sealing the R Objects Test and Assert Conditions
Version: 0.1.0
Authors@R: c( person(given = "Shinya", family = "Uryu", email = "suika1127@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-0493-6186")) )
Description: Record the state of R object. Outputs include object class and attributes. This helps reduce errors in test results by manual description. The goal is to improve the efficiency of data analysis and package development.
License: MIT + file LICENSE
URL: https://github.com/uribo/sealr#readme
BugReports: https://github.com/uribo/sealr/issues
Depends: R (>= 3.1.0)
Imports: clipr (>= 0.4.0), clisymbols (>= 1.2.0), digest (>= 0.6.15), dplyr (>= 0.7.4), glue (>= 1.2.0), magrittr (>= 1.5), purrr (>= 0.2.4), rlang (>= 0.2.0), rstudioapi (>= 0.7), styler (>= 1.0.0), tibble (>= 1.4.2), tidyr (>= 0.8.0)
Suggests: covr (>= 3.0.1), testthat (>= 2.0.0), knitr (>= 1.20), rmarkdown (>= 1.9)
ByteCompile: true
Encoding: UTF-8
Language: en-US
LazyData: true
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-04-05 19:41:13 UTC; rstudio
Author: Shinya Uryu [aut, cre] (<https://orcid.org/0000-0002-0493-6186>)
Maintainer: Shinya Uryu <suika1127@gmail.com>
Repository: CRAN
Date/Publication: 2018-04-06 12:11:57 UTC

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New package mueRelativeRisk with initial version 0.1.1
Package: mueRelativeRisk
Title: Relative Risk Based on the Ratio of Median Unbiased Estimates
Version: 0.1.1
Authors@R: person("Rickey", "Carter", email = "carter.rickey@mayo.edu", role = c("aut", "cre"))
Description: Implements an estimator for relative risk based on the median unbiased estimator. The relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. The details of the method are available in Carter et al (2010) <doi:10.1111/j.1467-9876.2010.00711.x>.
Depends: R (>= 3.2.3)
Imports: stats
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-04-05 17:05:06 UTC; m070316
Author: Rickey Carter [aut, cre]
Maintainer: Rickey Carter <carter.rickey@mayo.edu>
Repository: CRAN
Date/Publication: 2018-04-06 12:58:02 UTC

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New package indirect with initial version 0.1.2
Package: indirect
Type: Package
Title: Elicitation of Independent Conditional Means Priors for Generalised Linear Models
Version: 0.1.2
Date: 2018-04-06
Author: Geoffrey R. Hosack
Maintainer: Geoff Hosack <geoff.hosack@csiro.au>
Description: Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.
License: GPL-3
Depends: R (>= 3.1.0)
Imports: MASS, gplots
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-04-06 05:22:05 UTC; hos06b
Repository: CRAN
Date/Publication: 2018-04-06 12:42:33 UTC

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New package fixerapi with initial version 0.1.1
Package: fixerapi
Version: 0.1.1
Title: An R Client for the "Fixer.io" Currency API
Description: An R client for the "fixer.io" currency conversion and exchange rate API. The API requires registration and some features are only available on paid accounts. The full documentation is available at <https://fixer.io/documentation>.
Authors@R: person("Evan", "Odell", email = "evanodell91@gmail.com", role = c("aut", "cre"), comment = c(ORCID='0000-0003-1845-808X'))
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
ByteCompile: true
Suggests: testthat, covr, knitr, rmarkdown
RoxygenNote: 6.0.1
URL: https://docs.evanodell.com/fixerapi
BugReports: https://github.com/evanodell/fixerapi/issues
Imports: jsonlite, tibble, tidyr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-04-06 05:06:27 UTC; evanodell
Author: Evan Odell [aut, cre] (<https://orcid.org/0000-0003-1845-808X>)
Maintainer: Evan Odell <evanodell91@gmail.com>
Repository: CRAN
Date/Publication: 2018-04-06 12:40:21 UTC

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New package featurefinder with initial version 1.0
Package: featurefinder
Title: Feature Finder
Version: 1.0
Authors@R: person("Richard", "Davis", email = "davisconsulting@gmail.com", role = c("aut", "cre"))
Description: Finds modelling features through a detailed analysis of model residuals using 'rpart' classification and regression trees. Scans the residuals of a model across subsets of the data to identify areas where the model prediction differs from the actual target variable. S. Chatterjee, A. S. Hadi (2006) <doi:10.1002/0470055464>.
Depends: R (>= 3.2.0)
License: MIT + file LICENSE
LazyData: true
RoxygenNote: 6.0.1
Suggests: png, knitr, Metrics, mlr, gbm, randomForest
VignetteBuilder: knitr
Imports: rpart, rpart.plot, plyr, grDevices
NeedsCompilation: no
Packaged: 2018-04-06 01:40:54 UTC; davisri
Author: Richard Davis [aut, cre]
Maintainer: Richard Davis <davisconsulting@gmail.com>
Repository: CRAN
Date/Publication: 2018-04-06 12:30:02 UTC

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Package breakDown updated to version 0.1.5 with previous version 0.1.4 dated 2018-02-27

Title: Break Down Plots
Description: Break Down Plots are inspired by waterfall plots created by 'xgboostExplainer' package (see <https://github.com/AppliedDataSciencePartners/xgboostExplainer>). The idea behind Break Down Plots it to decompose model prediction for a single observation. Break Down Plots show the contribution of every variable present in the model. Such plots will work for binary classifiers and general regression models.
Author: Przemyslaw Biecek [aut, cre]
Maintainer: Przemyslaw Biecek <przemyslaw.biecek@gmail.com>

Diff between breakDown versions 0.1.4 dated 2018-02-27 and 0.1.5 dated 2018-04-06

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Package ARTP updated to version 2.0.5 with previous version 2.0.4 dated 2014-02-20

Title: Gene and Pathway p-Values Computed using the Adaptive Rank Truncated Product
Description: For calculating gene and pathway p-values using the Adaptive Rank Truncated Product test.
Author: Kai Yu, Qizhai Li and William Wheeler
Maintainer: Bill Wheeler <wheelerb@imsweb.com>

Diff between ARTP versions 2.0.4 dated 2014-02-20 and 2.0.5 dated 2018-04-06

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Package alfred updated to version 0.1.6 with previous version 0.1.5 dated 2018-02-20

Title: Downloading Time Series from ALFRED Database for Various Vintages
Description: Provides direct access to the ALFRED (<https://alfred.stlouisfed.org>) and FRED (<https://fred.stlouisfed.org>) databases. Its functions return tidy data frames for different releases of the specified time series. Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.
Author: Onno Kleen [aut, cre]
Maintainer: Onno Kleen <r@onnokleen.de>

Diff between alfred versions 0.1.5 dated 2018-02-20 and 0.1.6 dated 2018-04-06

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New package RRphylo with initial version 1.0.0
Package: RRphylo
Type: Package
Title: Phylogenetic Ridge Regression Methods for Comparative Studies
Version: 1.0.0
Author: Pasquale Raia, Silvia Castiglione, Carmela Serio, Alessandro Mondanaro, Marina Melchionna, Mirko Di Febbraro, Antonio Profico, Francesco Carotenuto
Maintainer: Pasquale Raia <pasquale.raia@unina.it>
Description: Functions for phylogenetic analysis (Castiglione et al, 2017 <doi:10.1111/2041-210X.12954>). The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, and the computation of ontogenetic shape vectors.
License: GPL-2
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.4.0)
Imports: ape, phytools, geiger, stats4, foreach, doParallel, pvclust, mvMORPH, lmtest, parallel, phangorn, smatr, rlist, scales, R.utils
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-04-05 16:43:47 UTC; pas
Repository: CRAN
Date/Publication: 2018-04-06 11:37:52 UTC

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New package netregR with initial version 0.1.0
Package: netregR
Title: Regression of Network Responses
Version: 0.1.0
Authors@R: person("Frank W. Marrs", email = "frank.marrs@colostate.edu", role = c("aut", "cre"))
Description: Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and generates random covariance matrices of this class. For more detail on methods, see Marrs (2017) <arXiv:1701.05530>.
Depends: R (>= 3.3.0)
Imports: Matrix, stats, graphics
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-04-05 17:20:24 UTC; fmarrs3
Author: Frank W. Marrs [aut, cre]
Maintainer: Frank W. Marrs <frank.marrs@colostate.edu>
Repository: CRAN
Date/Publication: 2018-04-06 11:44:53 UTC

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New package komaletter with initial version 0.1.3
Package: komaletter
Type: Package
Title: Simple yet Flexible Letters via the 'KOMA-Script LaTeX Bundle'
Version: 0.1.3
Authors@R: c( person("Robert", "Nuske", role=c("aut", "cre"), email="robert.nuske@mailbox.org", comment=c(ORCID="0000-0001-9773-2061")), person("Dirk", "Eddelbuettel", role="aut"), person("Aaron", "Wolen", role="aut"))
Maintainer: Robert Nuske <robert.nuske@mailbox.org>
Description: An R Markdown template for writing beautiful yet versatile letters, using the 'KOMA-Script' letter class 'scrlttr2' and an adaptation of the 'pandoc-letter' template. 'scrlttr2' provides layouts for many different window envelope types and the possibility to define your own.
Depends: R (>= 3.0)
Imports: rmarkdown (>= 0.6)
Suggests: knitr
License: GPL-3
URL: https://github.com/rnuske/komaletter
BugReports: https://github.com/rnuske/komaletter/issues
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-04-05 19:17:47 UTC; rnuske
Author: Robert Nuske [aut, cre] (<https://orcid.org/0000-0001-9773-2061>), Dirk Eddelbuettel [aut], Aaron Wolen [aut]
Repository: CRAN
Date/Publication: 2018-04-06 11:50:04 UTC

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New package InterVA5 with initial version 1.0
Package: InterVA5
Type: Package
Title: Replicate and Analyse 'InterVA5'
Version: 1.0
Date: 2018-04-03
Author: Jason Thomas, Zehang Li, Tyler McCormick, Sam Clark, Peter Byass
Maintainer: Jason Thomas <jarathomas@gmail.com>
Description: Provides an R version of the 'InterVA5' software (<http://www.interva.net>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.
License: GPL-3
NeedsCompilation: no
Repository: CRAN
RoxygenNote: 6.0.1
Packaged: 2018-04-05 19:32:14 UTC; jason
Date/Publication: 2018-04-06 11:52:57 UTC

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New package graphframes with initial version 0.1.0
Package: graphframes
Type: Package
Title: Interface for 'GraphFrames'
Version: 0.1.0
Authors@R: person("Kevin", "Kuo", email = "kevin.kuo@rstudio.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7803-7901"))
Maintainer: Kevin Kuo <kevin.kuo@rstudio.com>
Description: A 'sparklyr' <https://spark.rstudio.com/> extension that provides an R interface for 'GraphFrames' <https://graphframes.github.io/>. 'GraphFrames' is a package for 'Apache Spark' that provides a DataFrame-based API for working with graphs. Functionality includes motif finding and common graph algorithms, such as PageRank and Breadth-first search.
URL: https://github.com/rstudio/graphframes
BugReports: https://github.com/rstudio/graphframes/issues
License: Apache License 2.0 | file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1.9000
Imports: sparklyr, tibble
Suggests: testthat, covr
NeedsCompilation: no
Packaged: 2018-04-05 14:36:05 UTC; kevinykuo
Author: Kevin Kuo [aut, cre] (<https://orcid.org/0000-0001-7803-7901>)
Repository: CRAN
Date/Publication: 2018-04-06 11:42:02 UTC

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Package gapfill updated to version 0.9.6 with previous version 0.9.5-3 dated 2017-06-08

Title: Fill Missing Values in Satellite Data
Description: Tools to fill missing values in satellite data and to develop new gap-fill algorithms. The methods are tailored to data (images) observed at equally-spaced points in time. The package is illustrated with MODIS NDVI data.
Author: Florian Gerber
Maintainer: Florian Gerber <florian.gerber@math.uzh.ch>

Diff between gapfill versions 0.9.5-3 dated 2017-06-08 and 0.9.6 dated 2018-04-06

 DESCRIPTION                   |    8 +-
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 18 files changed, 255 insertions(+), 150 deletions(-)

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New package flexmet with initial version 1.0.0.0
Package: flexmet
Type: Package
Title: Flexible Latent Trait Metrics using the Filtered Monotonic Polynomial Item Response Model
Version: 1.0.0.0
Authors@R: person("Leah", "Feuerstahler", email = "feuer024@umn.edu", role = c("aut","cre"))
Description: Application of the filtered monotonic polynomial (FMP) item response model to flexibly fit item response models. The package includes tools that allow the item response model to be build on any monotonic transformation of the latent trait metric, as described by Feuerstahler (2016) <http://hdl.handle.net/11299/182267>.
License: GPL-3
Suggests: MonoPoly, knitr, rmarkdown
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-04-05 15:49:49 UTC; Leah
Author: Leah Feuerstahler [aut, cre]
Maintainer: Leah Feuerstahler <feuer024@umn.edu>
Repository: CRAN
Date/Publication: 2018-04-06 11:33:07 UTC

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New package essurvey with initial version 1.0.0
Package: essurvey
Title: Download Data from the European Social Survey on the Fly
Version: 1.0.0
Authors@R: c( person("Jorge", "Cimentada", email = "cimentadaj@gmail.com", role = c("aut", "cre")), person("Thomas", "Leeper", role = "rev", comment = "Thomas reviewed the package for rOpensci,see https://github.com/ropensci/onboarding/issues/201"), person("Nujcharee", "Haswell", role = "rev", comment = "Nujcharee reviewed the package for rOpensci, see https://github.com/ropensci/onboarding/issues/201") )
BugReports: https://github.com/ropensci/essurvey/issues
Description: Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two families of functions that allow you to download and interactively check all countries and rounds available.
Depends: R (>= 3.4.0)
License: MIT + file LICENSE
URL: https://github.com/ropensci/essurvey, https://ropensci.github.io/essurvey/
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
Imports: xml2, httr, stringr, haven, rvest, tibble
Suggests: testthat, knitr, rmarkdown, covr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-04-05 13:20:30 UTC; cimentadaj
Author: Jorge Cimentada [aut, cre], Thomas Leeper [rev] (Thomas reviewed the package for rOpensci,see https://github.com/ropensci/onboarding/issues/201), Nujcharee Haswell [rev] (Nujcharee reviewed the package for rOpensci, see https://github.com/ropensci/onboarding/issues/201)
Maintainer: Jorge Cimentada <cimentadaj@gmail.com>
Repository: CRAN
Date/Publication: 2018-04-06 11:16:45 UTC

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New package anomalize with initial version 0.1.0
Package: anomalize
Type: Package
Title: Tidy Anomaly Detection
Version: 0.1.0
Authors@R: c( person("Matt", "Dancho", email = "mdancho@business-science.io", role = c("aut", "cre")), person("Davis", "Vaughan", email = "dvaughan@business-science.io", role = c("aut")) )
Description: The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the associated functions for specific references for these methods.
URL: https://github.com/business-science/anomalize
BugReports: https://github.com/business-science/anomalize/issues
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.0.0)
Imports: dplyr, glue, timetk, sweep, tibbletime, purrr, rlang, tibble, tidyr, ggplot2
RoxygenNote: 6.0.1
Suggests: tidyverse, tidyquant, testthat, covr, knitr, rmarkdown, devtools, roxygen2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-04-05 17:06:16 UTC; mdancho
Author: Matt Dancho [aut, cre], Davis Vaughan [aut]
Maintainer: Matt Dancho <mdancho@business-science.io>
Repository: CRAN
Date/Publication: 2018-04-06 11:39:58 UTC

More information about anomalize at CRAN
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New package metaboGSE with initial version 1.0.1
Package: metaboGSE
Type: Package
Title: Gene Set Enrichment Analysis via Integration of Metabolic Networks and RNA-Seq Data
Version: 1.0.1
Depends: R (>= 3.4.0), sybil (>= 2.0.4), AnnotationDbi (>= 1.32.3), ape (>= 3.3), topGO (>= 2.24.0)
Suggests: clpAPI (>= 1.2.6), glpkAPI (>= 1.2.11), KernSmooth, KEGGREST, survival, ctc, knitr, rmarkdown
Imports: utils, stats, methods, parallel, grDevices, graphics, Matrix
Packaged: 2018-04-04 20:30:18 UTC; ttran
Author: Van Du T. Tran [aut, cre], Marco Pagni [aut]
Maintainer: Van Du T. Tran <thuong.tran@sib.swiss>
Description: Integrates metabolic networks and RNA-seq data to construct condition-specific series of metabolic sub-networks and applies to gene set enrichment analysis (Tran et al. (2018) <doi:10.1101/200964>).
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
VignetteBuilder: knitr
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2018-04-06 10:45:25 UTC

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New package CustomerScoringMetrics with initial version 1.0.0
Package: CustomerScoringMetrics
Type: Package
Title: Evaluation Metrics for Customer Scoring Models Depending on Binary Classifiers
Version: 1.0.0
Author: Koen W. De Bock
Maintainer: Koen W. De Bock <kdebock@audencia.com>
Description: Functions for evaluating and visualizing predictive model performance (specifically: binary classifiers) in the field of customer scoring. These metrics include lift, lift index, gain percentage, top-decile lift, F1-score, expected misclassification cost and absolute misclassification cost. See Berry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank (2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008, ISBN:978–0–387–72578–9) for details. Visualization functions are included for lift charts and gain percentage charts. All metrics that require class predictions offer the possibility to dynamically determine cutoff values for transforming real-valued probability predictions into class predictions.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-04-04 18:46:18 UTC; kdebock
Repository: CRAN
Date/Publication: 2018-04-06 10:39:01 UTC

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Package bsplinePsd updated to version 0.5.0 with previous version 0.2.0 dated 2018-02-23

Title: Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors
Description: Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series. The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). <doi.org/10.1007/s11222-017-9796-9>.
Author: Matthew C. Edwards [aut, cre], Renate Meyer [aut], Nelson Christensen [aut]
Maintainer: Matthew C. Edwards <matt.edwards@auckland.ac.nz>

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New package CARRoT with initial version 0.1.0
Package: CARRoT
Title: Predicting Categorical and Continuous Outcomes Using Rule of Ten
Version: 0.1.0
Authors@R: c(person("Alina", "Bazarova", email = "a.bazarova@bham.ac.uk", role = c("aut", "cre")),person("Marko", "Raseta", email = "m.raseta@keele.ac.uk", role = c("aut")))
Description: Predicts categorical or continuous outcomes while concentrating on four key points. These are Cross-validation, Accuracy, Regression and Rule of Ten (CARRoT). It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying a rule of ten events per variable are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Recever Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> and Rhemtulla et al. (2012) <doi:10.1037/a0029315>.
Depends: R (>= 3.4.0)
License: GPL-2
Encoding: UTF-8
Imports: stats,utils,nnet,doParallel,parallel,foreach,Rdpack
LazyData: true
RoxygenNote: 6.0.1.9000
RdMacros: Rdpack
NeedsCompilation: no
Packaged: 2018-04-04 16:28:37 UTC; alina
Author: Alina Bazarova [aut, cre], Marko Raseta [aut]
Maintainer: Alina Bazarova <a.bazarova@bham.ac.uk>
Repository: CRAN
Date/Publication: 2018-04-06 10:52:24 UTC

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Package TOSTER updated to version 0.3.1 with previous version 0.3 dated 2017-11-15

Title: Two One-Sided Tests (TOST) Equivalence Testing
Description: Two one-sided tests (TOST) procedure to test equivalence for t-tests, correlations, differences between proportions, and meta-analyses, including power analysis for t-tests and correlations. Allows you to specify equivalence bounds in raw scale units or in terms of effect sizes. See: Lakens (2017) <doi:10.1177/1948550617697177>.
Author: Daniel Lakens <D.Lakens@tue.nl>
Maintainer: Daniel Lakens <D.Lakens@tue.nl>

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Package RPostgres updated to version 1.1.0 with previous version 1.0-4 dated 2017-12-20

Title: 'Rcpp' Interface to 'PostgreSQL'
Description: Fully 'DBI'-compliant 'Rcpp'-backed interface to 'PostgreSQL' <https://www.postgresql.org/>, an open-source relational database.
Author: Hadley Wickham [aut], Jeroen Ooms [aut], Kirill Müller [aut, cre] (<https://orcid.org/0000-0002-1416-3412>), RStudio [cph], R Consortium [fnd], Tomoaki Nishiyama [ctb] (Code for encoding vectors into strings derived from RPostgreSQL)
Maintainer: Kirill Müller <krlmlr+r@mailbox.org>

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Package gmfd updated to version 1.0.1 with previous version 1.0 dated 2017-12-18

Title: Inference and Clustering of Functional Data
Description: Some methods for the inference and clustering of univariate and multivariate functional data, using a generalization of Mahalanobis distance, along with some functions useful for the analysis of functional data. For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.
Author: Andrea Martino [aut, cre], Andrea Ghiglietti [aut], Francesca Ieva [aut], Anna Maria Paganoni [aut]
Maintainer: Andrea Martino <andrea.martino@polimi.it>

Diff between gmfd versions 1.0 dated 2017-12-18 and 1.0.1 dated 2018-04-06

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Package furniture updated to version 1.7.6 with previous version 1.7.3 dated 2018-01-17

Title: Furniture for Quantitative Scientists
Description: Contains three main functions (i.e., three pieces of furniture): table1() which produces a well-formatted table of descriptives common as Table 1 in research articles, tableC() which produces a well-formatted table of correlations, tableF() which provides frequency counts, and washer() which is helpful in cleaning up the data. In addition, long() and wide(), built on the stats::reshape(), can help reformat the data. These furniture-themed functions are designed to simplify common tasks in quantitative analysis. Other data summary and cleaning tools are also available.
Author: Tyson S. Barrett [aut, cre] (<https://orcid.org/0000-0002-2137-1391>), Emily Brignone [aut], Daniel J. Laxman [aut]
Maintainer: Tyson S. Barrett <t.barrett@aggiemail.usu.edu>

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New package kdtools with initial version 0.1.0
Package: kdtools
Type: Package
Title: Tools for Working with Multidimensional Data
Version: 0.1.0
Authors@R: person("Timothy", "Keitt", email = "tkeitt@gmail.com", role = c("aut", "cre"))
Description: Provides various tools for working with multidimensional data in R and C++, including extremely fast nearest-neighbor- and range- queries without the overhead of linked tree nodes.
License: GPL
Encoding: UTF-8
LazyData: true
Imports: Rcpp (>= 0.12.14)
LinkingTo: Rcpp, strider, BH
Suggests: testthat, knitr, rmarkdown, covr, ggplot2, tidytext, printr, scales
VignetteBuilder: knitr
RoxygenNote: 6.0.1
URL: https://github.com/thk686/kdtools
BugReports: https://github.com/thk686/kdtools/issues
NeedsCompilation: yes
Packaged: 2018-04-04 17:24:08 UTC; tkeitt
Author: Timothy Keitt [aut, cre]
Maintainer: Timothy Keitt <tkeitt@gmail.com>
Repository: CRAN
Date/Publication: 2018-04-06 08:36:57 UTC

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Package Anthropometry updated to version 1.10 with previous version 1.9 dated 2017-08-29

Title: Statistical Methods for Anthropometric Data
Description: Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis.
Author: Guillermo Vinue, Irene Epifanio, Amelia Simo, M. Victoria Ibanez, Juan Domingo, Guillermo Ayala
Maintainer: Guillermo Vinue <Guillermo.Vinue@uv.es>

Diff between Anthropometry versions 1.9 dated 2017-08-29 and 1.10 dated 2018-04-06

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Package pcadapt (with last version 4.0.0) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2018-04-05 4.0.0

Permanent link
Package makedummies updated to version 1.1 with previous version 1.0 dated 2017-01-02

Title: Create Dummy Variables from Categorical Data
Description: Create dummy variables from categorical data. This package can convert categorical data (factor and ordered) into dummy variables and handle multiple columns simultaneously. This package enables to select whether a dummy variable for base group is included (for principal component analysis/factor analysis) or excluded (for regression analysis) by an option.
Author: Toshiaki Ara [aut, cre]
Maintainer: Toshiaki Ara <toshiaki.ara@gmail.com>

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Package kde1d updated to version 0.1.1 with previous version 0.1.0 dated 2018-03-26

Title: Univariate Kernel Density Estimation
Description: Provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded and discrete data. See Geenens (2014) <arXiv:1303.4121>, Geenens and Wang (2018) <arXiv:1602.04862>, Nagler (2018a) <arXiv:1704.07457>, Nagler (2018b) <arXiv:1705.05431>.
Author: Thomas Nagler [aut, cre], Thibault Vatter [aut]
Maintainer: Thomas Nagler <mail@tnagler.com>

Diff between kde1d versions 0.1.0 dated 2018-03-26 and 0.1.1 dated 2018-04-06

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Package bartMachine updated to version 1.2.4.1 with previous version 1.2.4 dated 2018-03-02

Title: Bayesian Additive Regression Trees
Description: An advanced implementation of Bayesian Additive Regression Trees with expanded features for data analysis and visualization.
Author: Adam Kapelner and Justin Bleich (R package)
Maintainer: Adam Kapelner <kapelner@qc.cuny.edu>

Diff between bartMachine versions 1.2.4 dated 2018-03-02 and 1.2.4.1 dated 2018-04-06

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Package BayesRS updated to version 0.1.3 with previous version 0.1.2 dated 2017-10-18

Title: Bayes Factors for Hierarchical Linear Models with Continuous Predictors
Description: Runs hierarchical linear Bayesian models. Samples from the posterior distributions of model parameters in JAGS (Just Another Gibbs Sampler; Plummer, 2017, <http://mcmc-jags.sourceforge.net>). Computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio (Wetzels, Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).
Author: Mirko Thalmann [aut, cre], Marcel Niklaus [aut], Klaus Oberauer [ths], John Kruschke [ctb]
Maintainer: Mirko Thalmann <mirkothalmann@hotmail.com>

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Package ziphsmm updated to version 2.0.3 with previous version 2.0.2 dated 2018-03-10

Title: Zero-Inflated Poisson Hidden (Semi-)Markov Models
Description: Fit zero-inflated Poisson hidden (semi-)Markov models with or without covariates by directly minimizing the negative log likelihood function using the gradient descent algorithm. Multiple starting values should be used to avoid local minima.
Author: Zekun (Jack) Xu, Ye Liu
Maintainer: Zekun (Jack) Xu <zekunxu@gmail.com>

Diff between ziphsmm versions 2.0.2 dated 2018-03-10 and 2.0.3 dated 2018-04-06

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Package selectr updated to version 0.4-1 with previous version 0.4-0 dated 2018-03-27

Title: Translate CSS Selectors to XPath Expressions
Description: Translates a CSS3 selector into an equivalent XPath expression. This allows us to use CSS selectors when working with the XML package as it can only evaluate XPath expressions. Also provided are convenience functions useful for using CSS selectors on XML nodes. This package is a port of the Python package 'cssselect' (<https://cssselect.readthedocs.io/>).
Author: Simon Potter [aut, trl, cre], Simon Sapin [aut], Ian Bicking [aut]
Maintainer: Simon Potter <simon@sjp.co.nz>

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Package MplusAutomation updated to version 0.7-2 with previous version 0.7-1 dated 2018-01-31

Title: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus
Description: Leverages the R language to automate latent variable model estimation and interpretation using 'Mplus', a powerful latent variable modeling program developed by Muthen and Muthen (<http://www.statmodel.com>). Specifically, this package provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics.
Author: Michael Hallquist [aut, cre], Joshua Wiley [aut], Caspar van Lissa [ctb]
Maintainer: Michael Hallquist <michael.hallquist@gmail.com>

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Package jmv updated to version 0.8.6.2 with previous version 0.8.6 dated 2018-03-27

Title: The 'jamovi' Analyses
Description: A suite of common statistical methods such as descriptives, t-tests, ANOVAs, regression, correlation matrices, proportion tests, contingency tables, and factor analysis. This package is also useable from the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more information).
Author: Ravi Selker, Jonathon Love, Damian Dropmann
Maintainer: Jonathon Love <jon@thon.cc>

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 tests/testthat/testanovarmnp.R        |only
 tests/testthat/testconttablespaired.R |only
 tests/testthat/testcorrmatrix.R       |    5 ++-
 tests/testthat/testdescriptives.R     |   19 ++++++++++----
 tests/testthat/testproptest2.R        |only
 tests/testthat/testproptestn.R        |only
 24 files changed, 197 insertions(+), 107 deletions(-)

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