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Appendix: Toolbox functions
TheEconometricToolboxisorganizedinasetofdirectories,eachcontaining
adierent libraryoffunctions. WhenyourInternet t browser r unpacks the
compressedlecontainingtheEconometricToolboxtheleswillbeplaced
intheappropriatedirectories.
Toinstallthetoolbox:
1. createasinglesubdirectoryintheMATLABtoolboxdirectory:
C:\matlab\toolbox\econ
Wherewehaveusedthenameeconforthedirectory.
2. Copythesystemofdirectoriestothissubdirectory.
3. UsethegraphicalpathtoolinMATLABtoaddthese directoriesto
your path. (Ona a unix or linux system,youmay needtoedit your
environment variables s that t set the e MATLAB B path.) the e graphical
pathtoolinMATLABtoaddthesedirectoriestoyourpath. (Ona
unixorlinuxsystem,youmayneedtoedityourenvironmentvariables
thatsettheMATLABpath.)
Alistingofthecontentslefromeachsubdirectoryispresentedonthe
followingpages.
260
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CHAPTER6. VARANDERRORCORRECTIONMODELS
261
Theregressionfunctionlibraryisinasubdirectoryregress.
regression functionlibrary
------- regressionprogram functions -----------
ar_g
- Gibbs s sampling Bayesianautoregressivemodel
bma_g
- Gibbs s sampling Bayesianmodel averaging
boxcox
- Box-Cox x regressionwith 1 1 parameter
boxcox2
- Box-Cox x regressionwith 2 2 parameters
hmarkov_em - - Hamilton's s Markov switching regression
hwhite
- Halbert t White's heteroscedastic consistent estimates
lad
- least-absolute e deviations regression
lm_test
- LM-test t for two regressionmodels
logit
- logit t regression
mlogit
- multinomiallogit t regression
nwest
- Newey-West t hetero/serial consistent estimates
ols
- ordinaryleast-squares
ols_g
- Gibbs s sampling Bayesianlinear model
olsar1
- Maximum m Likelihoodfor AR(1) errors ols s model
olsc
- Cochrane-OrcuttAR(1) ) errors ols model
olst
- regression n with t-distributederrors
probit
- probit t regression
probit_g
- Gibbs s sampling Bayesianprobit model
ridge
- ridge e regression
rtrace
- ridge e estimatesvs parameters(plot)
robust
- iterativelyreweighted d least-squares
sur
- seemingly y unrelatedregressions
switch_em
- switching g regime regression using EM-algorithm
theil
- Theil-Goldberger r mixed estimation
thsls
- three-stageleast-squares
tobit
- tobit t regression
tobit_g
- Gibbs s sampling Bayesiantobit model
tsls
- two-stage e least-squares
waldf
- Wald d F-test
--------demonstrationprograms -----------------
ar_gd
- demonstration n of f Gibbs s sampling ar_g
bma_gd
- demonstratesBayesian n model l averaging
box_cox_d
- demonstratesBox-Cox x 1-parameter model
boxcox2_d
- demonstratesBox-Cox x 2-parmaeter model
demo_all
- demos s most regression functions
hmarkov_emd- demos s Hamilton's Markovswitchingregression
hwhite_d
- H. White's s hetero consistent estimatesdemo
lad_d
- demos s lad d regression
lm_test_d
- demos s lm_test
logit_d
- demonstrateslogit t regression
mlogit_d
- demonstratesmultinomial l logit
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CHAPTER6. VARANDERRORCORRECTIONMODELS
262
nwest_d
- demonstratesNewey-Westestimates
ols_d
- demonstratesols regression
ols_d2
- Monte e Carlo demo o using g ols regression
ols_gd
- demo o of f Gibbs s sampling ols_g
olsar1_d
- Max x Like AR(1) errors model l demo
olsc_d
- Cochrane-Orcuttdemo
olst_d
- olst t demo
probit_d
- probit t regression demo
probit_gd
- demo o of f Gibbs s sampling Bayesian probitmodel
ridge_d
- ridge e regression demo
robust_d
- demonstratesrobust t regression
sur_d
- demonstratessur using g Grunfeld'sdata
switch_emd - - demonstratesswitching g regression
theil_d
- demonstratestheil-goldbergerestimation
thsls_d
- three-stageleast-squares s demo
tobit_d
- tobit t regression demo
tobit_gd
- demo o of f Gibbs s sampling Bayesian tobit model
tsls_d
- two-stage e least-squaresdemo
waldf_d
- demo o of f using g wald F-test function
--------Support functions------------------------
ar1_like
- used d by y olsar1
(likelihood)
bmapost
- used d by y bma_g
box_lik
- used d by y box_cox (likelihood)
box_lik2
- used d by y box_cox2 2 (likelihood)
boxc_trans - - used d by y box_cox, , box_cox2
chis_prb
- computeschi-squared d probabilities
dmult
- used d by y mlogit
fdis_prb
- computesF-statistic c probabilities
find_new
- used d by y bma_g
grun.dat
- Grunfeld's s data used d by y sur_d
grun.doc
- documents s Grunfeld's data set
lo_like
- used d by y logit
(likelihood)
maxlik
- used d by y tobit
mcov
- used d by y hwhite
mderivs
- used d by y mlogit
mlogit_lik - - used d by y mlogit
nmlt_rnd
- used d by y probit_g
nmrt_rnd
- used d by y probit_g, , tobit_g
norm_cdf
- used d by y probit, , pr_like
norm_pdf
- used d by y prt_reg, , probit
olse
- ols s returning only residuals (used by sur)
plt
- plots everything
plt_eqs
- plots s equation systems
plt_reg
- plots s regressions
pr_like
- used d by y probit
(likelihood)
prt
- prints s everything
prt_eqs
- prints s equationsystems
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CHAPTER6. VARANDERRORCORRECTIONMODELS
263
prt_gibbs
- prints s Gibbs sampling models
prt_reg
- prints s regressions
prt_swm
- prints s switching regression results
sample
- used d by y bma_g
stdn_cdf
- used d by y norm_cdf
stdn_pdf
- used d by y norm_pdf
stepsize
- used d by y logit,probit t to determinestepsize
tdis_prb
- computest-statistic c probabilities
to_like
- used d by y tobit
(likelihood)
Theutilityfunctionsareinasubdirectoryutil.
utility function library
-------- utility functions -----------------------------
accumulate - - accumulatescolumn n elements of a a matrix
cal
- associates s obs # # with h time-seriescalendar
ccorr1
- correlationscalingto o normal column length
ccorr2
- correlationscalingto o unit t columnlength
fturns
- finds s turning-points in a a time-series
growthr
- convertstime-series s matrix x to o growth rates
ical
- associates s time-series dates with obs #
indicator
- convertsa matrix x to o indicatorvariables
invccorr
- inverse e for ccorr1,ccorr2
lag
- generates s a lagged variable vectoror matrix
levels
- generates s factor levelsvariable
lprint
- prints s a matrix in LaTeX table-formatted form
lprintf
- enhancedlprint t function
mlag
- generates s a var-type matrix x of f lags
mode
- calculates s the mode of a a distribution
mprint
- prints s a matrix
mprint3
- prints s coefficient,t-statistics matrices
mth2qtr
- convertsmonthly y to quarterlydata
nclag
- generates s a matrix of f non-contiguous s lags
plt
- wrapper r function, plots all l resultstructures
prt
- wrapper r function, prints all result strucutres
sacf
- sample e autocorrelation function estimates
sdiff
- seasonaldifferencing
sdummy
- generates s seasonaldummy variables
shist
- plots s spline smoothed histogram
spacf
- sample e partial autocorrelationestimates
tally
- computesfrequencies s of distinct levels
tdiff
- time-seriesdifferencing
tsdates
- time-seriesdates s function
tsprint
- print t time-series matrix
unsort
- unsorts s a a sorted d vectoror matrix
vec
- turns s a a matrix x into a a stacked d vector
vech
- matrix x from lower triangular columns of f a a matrix
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CHAPTER6. VARANDERRORCORRECTIONMODELS
264
--------demonstrationprograms -------------
cal_d.m
- demonstratescal function
fturns_d
- demonstratesfturns s and plt
ical_d.m
- demonstratesical function
lprint_d.m - - demonstrateslprint t function
lprintf_d.m- demonstrateslprintf f function
mprint_d.m - - demonstratesmprint t function
mprint3_d.m- demonstratesmprint3 3 function
sacf_d
- demonstratessacf
spacf_d
- demonstratesspacf
tsdate_d.m - - demonstratestsdate e function
tsprint_d.m- demonstratestsprint t function
util_d.m
- demonstratedsome of the utility y functions
--------functionsto mimic c analogousGauss functions -------------
cols
- returns s the # # of columns s in n a a matrix or r vector
cumprodc
- returns s cumulativeproduct of each column of a a matrix
cumsumc
- returns s cumulativesum of f each h column of a a matrix
delif
- select t matrix values for r which h a a condition n is s false
indexcat
- extract t indices equal to o a a scalar or r an interval
invpd
- makes s a a matrix x positive-definite,then inverts
matadd
- adds s non-conformingmatrices,row or r col l compatible.
matdiv
- divides s non-conforming matrices, row or r col l compatible.
matmul
- multiplies s non-conforming matrices, row w or col l compatible.
matsub
- divides s non-conforming matrices, row or r col l compatible.
prodc
- returns s product of each column of a a matrix
rows
- returns s the # # of rows s in n a a matrix or r vector
selif
- select t matrix values for r which h a a condition n is s true
seqa
- a sequence e of f numbers s with a a beginningand increment
stdc
- std d deviations of f columns returned d as a a column n vector
sumc
- returns s sum of each column
trimc
- trims s columns of f a a matrix (or vector)like Gauss
trimr
- trims s rows of f a a matrix (or vector)like Gauss
Asetofgraphingfunctionsareinasubdirectorygraphs.
graphingfunction library
-------- graphingprograms ---------------------------
pairs
- scatter r plot (uses histo)
pltdens
- density y plots
tsplot
- time-seriesgraphs
--------demonstrationprograms -----------------------
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CHAPTER6. VARANDERRORCORRECTIONMODELS
265
pairs_d
- demonstratespairwise e scatter
pltdens_d
- demonstratespltdens
tsplot_d
- demonstratestsplot
------- supportfunctions-----------------------------
histo
- used d by y pairs
plt_turns
- plots s turning pointsfrom fturns function
Alibraryofroutinesinthesubdirectory diagncontaintheregression
diagnosticsfunctions.
regression diagnosticslibrary
--------diagnosticprograms ---------------
bkw
- BKW collinearity diagnostics
bpagan
- Breusch-Paganheteroscedasticitytest
cusums
- Brown,Durbin,Evanscusum squarestest
dfbeta
- BKW influential observation diagnostics
diagnose
- compute diagnosticstatistics
rdiag
- graphical residuals diagnostics
recresid
- compute recursive residuals
studentize
- standarizationtransformation
------- demonstration programs -------------
bkw_d
- demonstrates bkw
bpagan_d
- demonstrates bpagan
cusums_d
- demonstrates cusums
dfbeta_d
- demonstrates dfbeta, plt_dfb,plt_dff
diagnose_d
- demonstrates diagnose
rdiag_d
- demonstrates rdiag
recresid_d
- demonstrates recresid
------- supportfunctions------------------
ols.m
- least-squaresregression
plt
- plots everything
plt_cus
- plots cusums test results
plt_dfb
- plots dfbetas
plt_dff
- plots dffits
Thevectorautoregressivelibraryisinasubdirectoryvar
bvar.
vector autoregressive function library
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CHAPTER6. VARANDERRORCORRECTIONMODELS
266
------- VAR/BVARprogram functions-----------
becm_g
- Gibbs s sampling BECM M estimates
becmf
- BayesianECM model l forecasts
becmf_g - - Gibbs s sampling BECM M forecasts
bvar
- BVAR model
bvar_g
- Gibbs s sampling BVAR R estimates
bvarf
- BVAR model l forecasts
bvarf_g - - Gibbs s sampling BVAR R forecasts
ecm
- ECM M (error correction)model estimates
ecmf
- ECM M model forecasts
irf
- impulseresponse e functions
lratio
- likelihood d ratio tests for lag g length
recm
- ecm m versionof rvar
recm_g
- Gibbs s sampling random-walkaveragingestimates
recmf
- random-walkaveragingECM M forecasts
recmf_g - - Gibbs s sampling random-walkaveragingforecasts
rvar
- Bayesianrandom-walk k averaging prior model
rvar_g
- Gibbs s sampling RVAR R estimates
rvarf
- BayesianRVAR R model l forecasts
rvarf_g - - Gibbs s sampling RVAR R forecasts
var
- VAR R model
varf
- VAR R model forecasts
------- demonstration programs -----------
becm_d
- BECM M model l demonstration
becm_g
- Gibbs samplingBECM M estimates s demo
becmf_d
- becmf demonstration
becmf_gd - - Gibbs samplingBECM M forecast t demo
bvar_d
- BVAR R model l demonstration
bvar_gd
- Gibbs samplingBVAR R demonstration
bvarf_d
- bvarf demonstration
bvarf_gd - - Gibbs samplingBVAR R forecasts s demo
ecm_d
- ECM M model l demonstration
ecmf_d
- ecmf f demonstration
irf_d
- impulseresponse functiondemo
irf_d2
- anotherirf f demo
lrratio_d - demonstrates lrratio
pftest_d - - demo o of f pftest function
recm_d
- RECM M model l demonstration
recm_gd
- Gibbs samplingRECM M model l demo
recmf_d
- recmf demonstration
recmf_gd - - Gibbs samplingRECM M forecast t demo
rvar_d
- RVAR R model l demonstration
rvar_gd
- Gibbs samplingrvar r model l demo
rvarf_d
- rvarf demonstration
rvarf_gd - - Gibbs samplingrvar r forecast t demo
var_d
- VAR R model l demonstration
CHAPTER6. VARANDERRORCORRECTIONMODELS
267
varf_d
- varf f demonstration
------- supportfunctions ------------------
johansen - - used d by y ecm,ecmf,becm,becmf,recm,recmf
lag
- does s ordinary y lags
lrratio
- likelihoodratio lag length tests
mlag
- does s var-type e lags
nclag
- does s contiguous s lags (used by y rvar,rvarf,recm,recmf)
ols
- used d for VAR R estimation
pftest
- prints Granger F-tests
pgranger - - prints Granger causality probabilities
prt
- prints results from m all functions
prt_coint - used d by y prt_var for r ecm,becm,recm
prt_var
- prints results of all var/bvar models
prt_varg - - prints results of all Gibbs var/bvarmodels
rvarb
- used d for RVARF F forecasts
scstd
- does s univariate e AR R for BVAR
theil_g
- used d for Gibbs s sampling estimates and d forecasts
theilbf
- used d for BVAR R forecasts
theilbv
- used d for BVAR R estimation
trimr
- used d by y VARF,BVARF, johansen (in /util/trimr.m)
vare
- used d by y lrratio (vare uses /regress/olse.m)
Theco-integrationlibraryfunctionsareinasubdirectorycoint.
co-integrationlibrary
------ co-integration testing routines --------
adf
- carries s out Augmented Dickey-Fullerunit root tests
cadf
- carries s out ADF tests for r co-integration
johansen
- carries s out Johansen's co-integration tests
------ demonstration programs-----------------
adf_d
- demonstratesadf
cadf_d
- demonstratescadf
johansen_d - - demonstratesjohansen
------ support functions ----------------------
c_sja
- returns s criticalvalues for SJ maximal eigenvaluetest
c_sjt
- returns s criticalvalues for SJ trace test
cols
- (like e Gauss cols)
detrend
- used d by y johansento o detrend data a series
prt_coint - - printsresults s from adf,cadf,johansen
ptrend
- used d by y adf f to o create e time polynomials
rows
- (like e Gauss rows)
CHAPTER6. VARANDERRORCORRECTIONMODELS
268
rztcrit
- returns s criticalvalues for cadf f test
tdiff
- time-series s differences
trimr
- (like e Gauss trimr)
ztcrit
- returns s criticalvalues for adf test
TheGibbsconvergencediagnosticfunctionsareinasubdirectorygibbs.
Gibbssampling convergencediagnostics functions
--------- convergence testing functions ---------
apm
- Geweke's chi-squaredtest
coda
- convergence diagnostics
momentg
- Geweke's NSE, RNE
raftery
- Rafteryand d Lewis s programGibbsit for r convergence
--------- demonstration programs----------------
apm_d
- demonstrates apm
coda_d
- demonstrates coda
momentg_d - demonstrates momentg
raftery_d - demonstrates raftery
--------- support functions ---------------------
prt_coda - - prints coda, raftery,momentg, apm m output (use e prt)
empquant - - These were convertedfrom:
indtest
- Rafferty and Lewis s FORTRAN N program.
mcest
- These functionnames follow the FORTRAN subroutines
mctest
-
ppnd
-
thin
-
Distributionfunctionsareinthesubdirectorydistrib.
Distribution functionslibrary
------- pdf, , cdf, , inverse functions -----------
beta_cdf - - beta(a,b) cdf
beta_inv - - beta a inverse e (quantile)
beta_pdf - - beta(a,b) pdf
bino_cdf - - binomial(n,p)cdf
bino_inv - - binomial inverse (quantile)
bino_pdf - - binomial pdf
chis_cdf - - chisquared(a,b) cdf
chis_inv - - chi-inverse (quantile)
chis_pdf - - chisquared(a,b) pdf
CHAPTER6. VARANDERRORCORRECTIONMODELS
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chis_prb - - probability for chi-squared statistics
fdis_cdf - - F(a,b) cdf
fdis_inv - - F F inverse e (quantile)
fdis_pdf - - F(a,b) pdf
fdis_prb - - probabililty for F-statistics
gamm_cdf - - gamma(a,b)cdf
gamm_inv - - gamma inverse (quantile)
gamm_pdf - - gamma(a,b)pdf
hypg_cdf - - hypergeometriccdf
hypg_inv - - hypergeometricinverse
hypg_pdf - - hypergeometricpdf
logn_cdf - - lognormal(m,v)cdf
logn_inv - - lognormal inverse (quantile)
logn_pdf - - lognormal(m,v)pdf
logt_cdf - - logistic cdf
logt_inv - - logistic inverse (quantile)
logt_pdf - - logistic pdf
norm_cdf - - normal(mean,var) cdf
norm_inv - - normal inverse (quantile)
norm_pdf - - normal(mean,var) pdf
pois_cdf - - poissoncdf
pois_inv - - poissoninverse
pois_pdf - - poissonpdf
stdn_cdf - - std d normalcdf
stdn_inv - - std d normalinverse
stdn_pdf - - std d normalpdf
tdis_cdf - - studentt-distribution cdf
tdis_inv - - studentt t inverse e (quantile)
tdis_pdf - - studentt-distribution pdf
tdis_prb - - probabililty for t-statistics
------- random samples -----------------------
beta_rnd - - random beta(a,b) draws
bino_rnd - - random binomial draws
chis_rnd - - random chi-squared(n)draws
fdis_rnd - - random F(a,b) draws
gamm_rnd - - random gamma(a,b)draws
hypg_rnd - - random hypergeometricdraws
logn_rnd - - random log-normaldraws
logt_rnd - - random logistic draws
nmlt_rnd - - left-truncatednormal draw
nmrt_rnd - - right-truncated normal draw
norm_crnd - contaminated normal random draws
norm_rnd - - multivariate normal draws
pois_rnd - - poissonrandom draws
tdis_rnd - - random student t-distribution draws
unif_rnd - - random uniform draws (lr,rt) interval
wish_rnd - - random Wishart draws
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