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alanarnholt

BSDA:Basic Statistics and Data Analysis

Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.

Maintained by Alan T. Arnholt. Last updated 2 years ago.

14.1 match 7 stars 9.11 score 1.3k scripts 6 dependents

humaniverse

healthyr:R package for mapping UK health data

A package to distribute and summarise on UK health data.

Maintained by Mike Page. Last updated 1 months ago.

7.5 match 4 stars 4.86 score 90 scripts

sbgraves237

Ecdat:Data Sets for Econometrics

Data sets for econometrics, including political science.

Maintained by Spencer Graves. Last updated 4 months ago.

4.0 match 2 stars 7.25 score 740 scripts 3 dependents

openintrostat

usdata:Data on the States and Counties of the United States

Demographic data on the United States at the county and state levels spanning multiple years.

Maintained by Mine Çetinkaya-Rundel. Last updated 10 months ago.

dataopenintro

4.0 match 9 stars 6.89 score 294 scripts 1 dependents

hdvinod

generalCorr:Generalized Correlations, Causal Paths and Portfolio Selection

Function gmcmtx0() computes a more reliable (general) correlation matrix. Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() and causeSum2Blk() give easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3, for the causal path X to Y, requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. Functions whose names begin with 'boot' provide bootstrap statistical inference, including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. A new tool for evaluation of out-of-sample portfolio performance is outOFsamp(). Panel data implementation is now included. See eight vignettes of the package for theory, examples, and usage tips. See Vinod (2019) \doi{10.1080/03610918.2015.1122048}.

Maintained by H. D. Vinod. Last updated 1 years ago.

4.0 match 2 stars 4.48 score 63 scripts 1 dependents

cran

cluster.datasets:Cluster Analysis Data Sets

A collection of data sets for teaching cluster analysis.

Maintained by Frederick Novomestky. Last updated 11 years ago.

7.5 match 2.00 score

nseg4

durhamSLR:The durhamSLR package

Data for Statistical Learning modules at Durham University.

Maintained by Sarah.Heaps. Last updated 2 years ago.

3.5 match 1.70 score

jerryratcliffe

aoristic:Generates Aoristic Probability Distributions

It can sometimes be difficult to ascertain when some events (such as property crime) occur because the victim is not present when the crime happens. As a result, police databases often record a 'start' (or 'from') date and time, and an 'end' (or 'to') date and time. The time span between these date/times can be minutes, hours, or sometimes days, hence the term 'Aoristic'. Aoristic is one of the past tenses in Greek and represents an uncertain occurrence in time. For events with a location describes with either a latitude/longitude, or X,Y coordinate pair, and a start and end date/time, this package generates an aoristic data frame with aoristic weighted probability values for each hour of the week, for each observation. The coordinates are not necessary for the program to calculate aoristic weights; however, they are part of this package because a spatial component has been integral to aoristic analysis from the start. Dummy coordinates can be introduced if the user only has temporal data. Outputs include an aoristic data frame, as well as summary graphs and displays. For more information see: Ratcliffe, JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns, Journal of Quantitative Criminology. 18 (1): 23-43. Note: This package replaces an original 'aoristic' package (version 0.6) by George Kikuchi that has been discontinued with his permission.

Maintained by Jerry Ratcliffe. Last updated 2 years ago.

0.9 match 7 stars 3.54 score 9 scripts