R: Binned Data Analysis

# Binned Data Analysis

## Documentation for package ‘bda’ version 18.2.2

- DESCRIPTION file.

## Help Pages

.bdaConnect | Data Binning
bda | Binned (Grouped) Data Analysis
binning | Data Binning
binning.default | Data Binning
binning.histogram | Data Binning
bootkde | Density estimation for data with rounding errors
bootPRO | Effectiveness Evaluation based on PROs with bootstrapping method
bootsmooth | non-parametric regression
bw.blscv | Compute a Binned Kernel Density Estimate for Weighted Data
bw.wnrd | Compute a Binned Kernel Density Estimate for Weighted Data
bw.wnrd0 | Compute a Binned Kernel Density Estimate for Weighted Data
ddeg | Fitting log-normal distributions
dGBP | Fitting Mixture Model of Generalized Beta and Pareto
dmlnorm | The mixed lognormal distribution
dmnorm | The mixed normal distribution
dPareto | The Pareto distribution
Employment2 | Firm size data
EUFirmSize | Firm size data of 10 EU countries
Firm2 | Firm size data
FirmAge | Firm size data
FirmDeathAge | Firm size data
FirmDeathSize | Firm size data
FirmEmploymentAge | Firm size data
FirmEmploymentSize | Firm size data
FirmJobAge | Firm size data
FirmJobSize | Firm size data
FirmSize | Firm size data
fit.Copula | Fitting firm size-age distributions
fit.FSD | Fitting firm size-age distributions
fit.GBP | Fitting Mixture Model of Generalized Beta and Pareto
fit.GLD | Fitting firm size-age distributions
fit.lnorm | Fitting log-normal distributions
fit.lognormal | Fitting log-normal distributions
fit.mlnorm | Fitting log-normal distributions
fit.Pareto | Fit a Pareto Distribution to Binned Data
fit.PRO | Fitting distributions to Patient-reported Outcome Data
fnm | Distribution of Two Finite Gaussian Mixtures
FSD | Firm size data
ImportFSD | Import Firm Size and Firm Age Data
Job2 | Firm size data
LCL | Breast Cancer Data
lines.FSD | Fitting firm size-age distributions
lines.VAS | Algorithms for Visual Analogue Scales
lps.variance | compute the variance of the local polynomial regression function
lpsmooth | non-parametric regression
mediation.test | The Sobel mediation test
meta | Breast Cancer Data
mixlognormal | Fitting log-normal distributions
NGS.normalize | Fitting log-normal distributions
normal | Breast Cancer Data
npr | non-parametric regression
ofc | occipitofrontal head circumference data
Pain | Pain data
pain | Pain data
pGBP | Fitting Mixture Model of Generalized Beta and Pareto
plot.bdata | Data Binning
plot.FSD | Fitting firm size-age distributions
plot.VAS | Algorithms for Visual Analogue Scales
pmixPU | The Pareto distribution
pmlnorm | The mixed lognormal distribution
pmnorm | The mixed normal distribution
pPareto | The Pareto distribution
primary | Breast Cancer Data
print.bdata | Data Binning
print.FSD | Fitting firm size-age distributions
print.mixlognormal | Fitting log-normal distributions
print.scb | non-parametric regression
print.VAS | Algorithms for Visual Analogue Scales
pro.test | Test effectiveness based on PROs
qmixPU | The Pareto distribution
qmlnorm | The mixed lognormal distribution
qmnorm | The mixed normal distribution
qPareto | The Pareto distribution
rmlnorm | The mixed lognormal distribution
rmnorm | The mixed normal distribution
rPareto | The Pareto distribution
tkde | Distribution of Two Finite Gaussian Mixtures
VAS.ecdf | Algorithms for Visual Analogue Scales
VAS.npr | Algorithms for Visual Analogue Scales
VAS.pdf | Algorithms for Visual Analogue Scales
wdekde | Algorithms for Visual Analogue Scales
wkde | Compute a Binned Kernel Density Estimate for Weighted Data
wlpsmooth | non-parametric regression
Zipf.Normalize | Zipf Normalization
ZipfPlot | Draw Zipf Plot
ZipfPlot.bdata | Draw Zipf Plot
ZipfPlot.default | Draw Zipf Plot
ZipfPlot.FSD | Draw Zipf Plot
ZipfPlot.histogram | Draw Zipf Plot